WO2024004220A1 - Terminal, radio communication method, and base station - Google Patents

Terminal, radio communication method, and base station Download PDF

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Publication number
WO2024004220A1
WO2024004220A1 PCT/JP2022/026514 JP2022026514W WO2024004220A1 WO 2024004220 A1 WO2024004220 A1 WO 2024004220A1 JP 2022026514 W JP2022026514 W JP 2022026514W WO 2024004220 A1 WO2024004220 A1 WO 2024004220A1
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Prior art keywords
model
information
csi
performance
performance monitoring
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PCT/JP2022/026514
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French (fr)
Japanese (ja)
Inventor
春陽 越後
浩樹 原田
リュー リュー
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株式会社Ｎｔｔドコモ
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Priority to PCT/JP2022/026514 priority Critical patent/WO2024004220A1/en
Publication of WO2024004220A1 publication Critical patent/WO2024004220A1/en

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/24—Cell structures
    • H04W16/28—Cell structures using beam steering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/18—Negotiating wireless communication parameters

Definitions

  • the present disclosure relates to a terminal, a wireless communication method, and a base station in a next-generation mobile communication system.
  • LTE Long Term Evolution
  • 3GPP Rel. 10-14 is a specification for the purpose of further increasing capacity and sophistication of LTE (Third Generation Partnership Project (3GPP (registered trademark)) Release (Rel. 8, 9). was made into
  • LTE Long Term Evolution
  • 5G 5th generation mobile communication system
  • 5G+ plus
  • NR New Radio
  • E-UTRA Evolved Universal Terrestrial Radio Access
  • E-UTRAN Evolved Universal Terrestrial Radio Access Network
  • AI artificial intelligence
  • ML machine learning
  • DL beam prediction spatial domain downlink (DL) beam prediction or temporal DL beam prediction using a one-sided AI model in the UE or NW is being considered.
  • DL beam prediction method may be called AI-based beam prediction (beam reporting), AI-based beam management (BM), or the like.
  • Performance monitoring of the AI model may be performed at a terminal (terminal, user terminal, user equipment (UE)), or at a base station (BS).
  • UE user terminal
  • BS base station
  • AI-based beam reporting no progress has been made in considering specific lifecycle management of performance monitoring in the UE/BS.
  • one of the purposes of the present disclosure is to provide a terminal, a wireless communication method, and a base station that can realize suitable overhead reduction/channel estimation/resource utilization.
  • a terminal includes a receiving unit that receives setting information for reporting for performance monitoring regarding artificial intelligence (AI) based beam management (BM), and a receiving unit that receives setting information for reporting for performance monitoring; and a control unit that controls transmission of a report regarding Layer 1 Reference Signal Received Power (L1-RSRP) measured based on the information.
  • AI artificial intelligence
  • BM beam management
  • L1-RSRP Layer 1 Reference Signal Received Power
  • suitable overhead reduction/channel estimation/resource utilization can be achieved.
  • FIG. 1 is a diagram illustrating an example of an AI model management framework.
  • 2A and 2B are diagrams illustrating an example of an AI-based beam report.
  • FIG. 3 is a diagram illustrating an example of a lifecycle management framework for performance monitoring in a UE according to an embodiment.
  • FIG. 4 is a diagram illustrating an example of a lifecycle management framework for performance monitoring in a BS according to an embodiment.
  • FIG. 5 is a diagram illustrating an example of CSI-RS opportunities for performance monitoring determined based on method 2-1 or 2-2.
  • FIG. 6 is a diagram illustrating an example of CSI-RS opportunities for performance monitoring determined based on method 2-3 or 2-4.
  • FIG. 7 is a diagram showing an example of model evaluation in the second embodiment.
  • FIG. 1 is a diagram illustrating an example of an AI model management framework.
  • 2A and 2B are diagrams illustrating an example of an AI-based beam report.
  • FIG. 3 is a diagram illustrating an example of a lifecycle management framework for
  • FIG. 8 is a diagram illustrating an example of a CSI report according to the sixth embodiment.
  • FIG. 9 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment.
  • FIG. 10 is a diagram illustrating an example of the configuration of a base station according to an embodiment.
  • FIG. 11 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment.
  • FIG. 12 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment.
  • FIG. 13 is a diagram illustrating an example of a vehicle according to an embodiment.
  • AI Artificial Intelligence
  • ML machine learning
  • improved Channel State Information Reference Signal (CSI) feedback e.g., reduced overhead, improved accuracy, prediction
  • improved beam management e.g., improved accuracy, prediction in the time/spatial domain
  • CSI Channel State Information Reference Signal
  • UE user terminals
  • BS Base Stations
  • the AI model may output at least one information such as an estimated value, a predicted value, a selected action, a classification, etc.
  • the UE/BS inputs channel state information, reference signal measurements, etc. to the AI model, and provides highly accurate channel state information/measurements/beam selection/position, future channel state information/radio link quality, etc. may be output.
  • AI may be read as an object (also referred to as a target, object, data, function, program, etc.) that has (implements) at least one of the following characteristics: ⁇ Estimation based on observed or collected information; - Selection based on observed or collected information; - Predictions based on observed or collected information.
  • estimation, prediction, and inference may be used interchangeably.
  • estimate the terms “estimate,” “predict,” and “infer” may be used interchangeably.
  • an object may be, for example, an apparatus, a device, etc., such as a UE or a BS. Furthermore, in the present disclosure, an object may correspond to a program/model/entity that operates on the device.
  • the AI model may be replaced by an object that has (implements) at least one of the following characteristics: ⁇ Produce estimates by feeding information, ⁇ Predict the estimated value by giving information, ⁇ Discover characteristics by providing information, ⁇ Select an action by providing information.
  • an AI model may refer to a data-driven algorithm that applies AI technology and generates a set of outputs based on a set of inputs.
  • AI models models, ML models, predictive analytics, predictive analysis models, tools, autoencoders (autoencoders), encoders, decoders, neural network models, AI algorithms, Schemes etc.
  • the AI model may be derived using at least one of regression analysis (eg, linear regression analysis, multiple regression analysis, logistic regression analysis), support vector machine, random forest, neural network, deep learning, etc.
  • the autoencoder may be interchanged with any autoencoder such as a stacked autoencoder or a convolutional autoencoder.
  • the encoder/decoder of the present disclosure may adopt models such as Residual Network (ResNet), DenseNet, RefineNet, etc.
  • an encoder encoding, encode/encoded, modification/change/control by an encoder, compressing, compress/compressed, generation ( “generate”, “generate/generated”, etc. may be used interchangeably.
  • a decoder decoding, decode/decoded, modification/change/control by a decoder, decompressing, decompress/decompressed, re- Reconstructing, reconstruct/reconstructed, etc. may be used interchangeably.
  • layers may be interchanged with layers (input layer, intermediate layer, etc.) used in the AI model.
  • the layers of the present disclosure include an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolution layer, an activation layer, a dense layer, a normalization layer, a pooling layer, an attention layer, a dropout layer, It may correspond to at least one of the fully connected layers.
  • AI model training methods may include supervised learning, unsupervised learning, reinforcement learning, federated learning, and the like.
  • Supervised learning may refer to the process of training a model from input and corresponding labels.
  • Unsupervised learning may refer to the process of training a model without labeled data.
  • Reinforcement learning is the process of training a model from inputs (in other words, states) and feedback signals (in other words, rewards) resulting from the model's outputs (in other words, actions) in the environment in which the models are interacting. It can also mean
  • generation, calculation, derivation, etc. may be read interchangeably.
  • implementation, operation, operation, execution, etc. may be read interchangeably.
  • training, learning, updating, retraining, etc. may be used interchangeably.
  • inference, after-training, production use, actual use, etc. may be read interchangeably.
  • a signal may be interchanged with a signal/channel.
  • FIG. 1 is a diagram illustrating an example of an AI model management framework.
  • each stage related to the AI model is shown as a block.
  • This example is also expressed as AI model life cycle management.
  • the data collection stage corresponds to the stage of collecting data for generating/updating an AI model.
  • the data collection stage includes data reduction (e.g., deciding which data to transfer for model training/model inference), data transfer (e.g., to entities performing model training/model inference (e.g., UE, gNB)), and transfer data).
  • data collection may refer to a process in which data is collected by a network node, management entity, or UE for the purpose of AI model training/data analysis/inference.
  • process and “procedure” may be interchanged with each other.
  • model training is performed based on the data (training data) transferred from the collection stage.
  • This stage includes data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model training/validation, and model testing (e.g., whether the trained model meets performance thresholds). verification), model exchange (e.g., transferring a model for distributed learning), model deployment/updating (deploying/updating a model to an entity that performs model inference), etc.
  • AI model training may refer to processing for training an AI model in a data-driven manner and obtaining a trained AI model for inference.
  • AI model validation may refer to a training sub-process for evaluating the quality of an AI model using a data set different from the data set used for model training. This sub-processing helps select model parameters that generalize beyond the dataset used to train the model.
  • AI model testing refers to a sub-process of training to evaluate the performance of the final AI model using a dataset different from the dataset used for model training/validation. You may. Note that unlike validation, testing does not have to be based on subsequent model tuning.
  • model inference is performed based on the data (inference data) transferred from the collection stage.
  • This stage includes data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model inference, model monitoring (e.g., monitoring the performance of model inference), and model performance feedback (the entity performing model training). (feedback of model performance to actors), output (provide model output to actors), etc.
  • AI model inference may refer to processing for producing a set of outputs from a set of inputs using a trained AI model.
  • a UE side model may refer to an AI model whose inference is completely performed in the UE.
  • a network side model may refer to an AI model whose inference is performed entirely in the network (eg, gNB).
  • the one-sided model may mean a UE-side model or a network-side model.
  • a two-sided model may refer to a pair of AI models in which joint inference is performed.
  • joint inference may include AI inference where the inference is performed jointly across the UE and the network, e.g., the first part of the inference is performed by the UE first and the remaining part is performed by the gNB. (or vice versa).
  • AI model monitoring may mean processing for monitoring the inference performance of an AI model, and may be interchanged with model performance monitoring, performance monitoring, etc.
  • model registration may mean making the model executable by assigning a version identifier to the model and compiling it on specific hardware used in the inference stage.
  • Model deployment also refers to delivering (or distributing) a fully developed and tested model runtime image (or image of an execution environment) to a target (e.g., UE/gNB) on which inference is performed. It may also mean ⁇ enabled''.
  • the actor stage includes action triggers (e.g., deciding whether to trigger an action on other entities), feedback (e.g., feeding back information necessary for training data/inference data/performance feedback), etc. May include.
  • action triggers e.g., deciding whether to trigger an action on other entities
  • feedback e.g., feeding back information necessary for training data/inference data/performance feedback
  • training of a model for mobility optimization may be performed, for example, in Operation, Administration and Maintenance (Management) (OAM) in a network (Network (NW)) / gNodeB (gNB).
  • OAM Operation, Administration and Maintenance
  • NW Network
  • gNodeB gNodeB
  • the former has advantages in interoperability, large storage capacity, operator manageability, and model flexibility (e.g., feature engineering). In the latter case, the advantage is that there is no need for model update latency or data exchange for model development.
  • Inference of the above model may be performed in the gNB, for example.
  • the entity that performs the training/inference may be different.
  • Functions of the AI model may include beam management, beam prediction, autoencoder (or information compression), CSI feedback, position positioning, etc.
  • the OAM/gNB may perform model training and the gNB may perform model inference.
  • a Location Management Function may perform model training, and the LMF may perform model inference.
  • the OAM/gNB/UE may perform model training and the gNB/UE (jointly) may perform model inference.
  • the OAM/gNB/UE may perform model training and the UE may perform model inference.
  • model activation may mean activating an AI model for a specific function.
  • Model deactivation may mean disabling an AI model for a particular function.
  • Model switching may mean deactivating the currently active AI model for a particular function and activating a different AI model.
  • model transfer may mean distributing the AI model over the air interface. This distribution may include distributing one or both of the parameters of the model structure known at the receiving end, or a new model with the parameters. This distribution may also include complete models or partial models. Model download may refer to model transfer from the network to the UE. Model upload may refer to model transfer from the UE to the network.
  • AI based beam report As a use case for utilizing AI models, spatial domain downlink (DL) beam prediction or temporal DL beam prediction using a one-sided AI model in the UE or NW is being considered. Such a beam prediction method may be called AI-based beam prediction (beam reporting), AI-based beam management (BM), or the like.
  • FIGS. 2A and 2B are diagrams showing an example of an AI-based beam report.
  • FIG. 2A shows spatial domain DL beam prediction.
  • the UE may measure a spatially sparse (or thick) beam, input the measurement results etc. to an AI model, and output a prediction result of the beam quality of a spatially dense (or thin) beam. .
  • Figure 2B shows temporal DL beam prediction.
  • the UE may measure a time-series beam, input the measurement results and the like into an AI model, and output a prediction result of the beam quality of a future beam.
  • BM case 1 spatial domain DL beam prediction
  • BM case 2 temporal DL beam prediction
  • the beams related to the output (prediction results) of the AI model may be referred to as a set A of beams.
  • the beams associated with the input of the AI model may be referred to as set B of beams.
  • the input candidates for the AI model for BM case 1/2 are L1-RSRP (Layer 1 Reference Signal Received Power), assistance information (for example, beam shape information, UE position/direction information, Examples include transmit beam usage information), channel impulse response (CIR) information, and corresponding DL transmit/receive beam ID.
  • L1-RSRP Layer 1 Reference Signal Received Power
  • assistance information for example, beam shape information, UE position/direction information, Examples include transmit beam usage information
  • CIR channel impulse response
  • the output candidates of the AI model for BM case 1 are the IDs of the top K transmit/receive beams (K is an integer), the predicted L1-RSRP of these beams, and the predicted L1-RSRP of each beam. These include the probability of entry, the angle of these beams, etc.
  • Candidates for the output of the AI model in BM case 2 include predicted beam failures in addition to candidates for the output of the AI model in BM case 1.
  • the performance monitoring shown in FIG. 1 may be performed at the UE or at the BS.
  • AI-based beam reporting no progress has been made in considering specific lifecycle management of performance monitoring in the UE/BS.
  • the present inventors conceived of a suitable method for implementing performance monitoring in the UE/BS.
  • A/B and “at least one of A and B” may be read interchangeably. Furthermore, in the present disclosure, “A/B/C” may mean “at least one of A, B, and C.”
  • Radio Resource Control RRC
  • RRC parameters RRC parameters
  • RRC messages upper layer parameters, fields, Information Elements (IEs), settings, etc.
  • IEs Information Elements
  • CE Medium Access Control Element
  • update command activation/deactivation command, etc.
  • the upper layer signaling may be, for example, Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information, etc., or a combination thereof.
  • RRC Radio Resource Control
  • MAC Medium Access Control
  • MAC signaling may use, for example, a MAC Control Element (MAC CE), a MAC Protocol Data Unit (PDU), or the like.
  • Broadcast information includes, for example, a master information block (MIB), a system information block (SIB), a minimum system information (RMSI), and other system information ( Other System Information (OSI)) may also be used.
  • MIB master information block
  • SIB system information block
  • RMSI minimum system information
  • OSI Other System Information
  • the physical layer signaling may be, for example, downlink control information (DCI), uplink control information (UCI), etc.
  • DCI downlink control information
  • UCI uplink control information
  • an index an identifier (ID), an indicator, a resource ID, etc.
  • ID an identifier
  • indicator an indicator
  • resource ID a resource ID
  • sequences, lists, sets, groups, groups, clusters, subsets, etc. may be used interchangeably.
  • a panel, a UE panel, a panel group, a beam, a beam group, a precoder, an uplink (UL) transmitting entity, a transmission/reception point (TRP), a base station, and a spatial relation information (SRI) are described.
  • SRS resource indicator SRI
  • control resource set CONtrol REsource SET (CORESET)
  • Physical Downlink Shared Channel PDSCH
  • codeword CW
  • Transport Block Transport Block
  • TB transport Block
  • RS reference signal
  • antenna port e.g. demodulation reference signal (DMRS) port
  • antenna port group e.g.
  • DMRS port group groups (e.g., spatial relationship groups, Code Division Multiplexing (CDM) groups, reference signal groups, CORESET groups, Physical Uplink Control Channel (PUCCH) groups, PUCCH resource groups), resources (e.g., reference signal resources, SRS resource), resource set (for example, reference signal resource set), CORESET pool, downlink Transmission Configuration Indication state (TCI state) (DL TCI state), uplink TCI state (UL TCI state), unified TCI Unified TCI state, common TCI state, quasi-co-location (QCL), QCL assumption, etc. may be read interchangeably.
  • groups e.g., spatial relationship groups, Code Division Multiplexing (CDM) groups, reference signal groups, CORESET groups, Physical Uplink Control Channel (PUCCH) groups, PUCCH resource groups
  • resources e.g., reference signal resources, SRS resource
  • resource set for example, reference signal resource set
  • CORESET pool downlink Transmission Configuration Indication state (TCI state) (DL TCI state), up
  • CSI-RS Non Zero Power (NZP) CSI-RS, Zero Power (ZP) CSI-RS, CSI Interference Measurement (CSI-IM), CSI - SSB and SSB may be read interchangeably. Additionally, the CSI-RS may include other reference signals.
  • RS to be measured/reported may mean RS to be measured/reported for CSI reporting.
  • timing, time, time, slot, subslot, symbol, subframe, etc. may be read interchangeably.
  • direction, axis, dimension, domain, polarization, polarization component, etc. may be read interchangeably.
  • RS may be interchanged with, for example, Channel State Information Reference Signal (CSI-RS), SS/PBCH block (SS block (SSB)), etc.
  • CSI-RS Channel State Information Reference Signal
  • SS block SS block
  • the RS index may be interchanged with a CSI-RS resource indicator (CSI-RS resource indicator (CRI)), an SS/PBCH block resource indicator (SS/PBCH block indicator (SSBRI)), etc.
  • CSI-RS resource indicator CRI
  • SS/PBCH block indicator SS/PBCH block indicator
  • channel measurement/estimation includes, for example, a channel state information reference signal (CSI-RS), a synchronization signal (SS), a synchronization signal/broadcast channel (Synchronization Signal/Physical It may be performed using at least one of a Broadcast Channel (SS/PBCH) block, a demodulation reference signal (DMRS), a measurement reference signal (Sounding Reference Signal (SRS)), and the like.
  • CSI-RS channel state information reference signal
  • SS synchronization signal
  • SS/PBCH Broadcast Channel
  • DMRS demodulation reference signal
  • SRS Sounding Reference Signal
  • CSI includes a channel quality indicator (CQI), a precoding matrix indicator (PMI), and a CSI-RS resource indicator (CRI).
  • CQI channel quality indicator
  • PMI precoding matrix indicator
  • CRI CSI-RS resource indicator
  • SSBRI SS/PBCH Block Resource Indicator
  • LI Layer Indicator
  • RI Rank Indicator
  • L1-RSRP Reference in Layer 1 Signal received power (Layer 1 Reference Signal Received Power), L1-RSRQ (Reference Signal Received Quality), L1-SINR (Signal to Interference plus Noise Ratio), L1-SNR (Signal to Noise Ratio), channel matrix (or channel information regarding the precoding matrix (or precoding coefficients), and the like.
  • UCI UCI
  • CSI report CSI feedback
  • feedback information feedback bit
  • CSI feedback method CSI feedback scheme
  • beam report beam report scheme, etc.
  • bits, bit strings, bit sequences, sequences, values, information, values obtained from bits, information obtained from bits, etc. may be interchanged.
  • model/non-AI based beam reporting may be interchanged with model, beam reporting method, beam reporting scheme, etc.
  • the relevant entities are the UE and the BS in order to explain an AI model regarding communication between the UE and the BS, but the application of each embodiment of the present disclosure is not limited to this.
  • the UE and BS in the embodiment below may be replaced with a first UE and a second UE.
  • the UE, BS, etc. of the present disclosure may be replaced with any UE/BS.
  • FIG. 3 is a diagram illustrating an example of a lifecycle management framework for performance monitoring in a UE according to an embodiment.
  • the UE measures RS for prediction (CSI-RS/SSB) and RS for performance monitoring.
  • the UE monitors the performance of the model and fallback scheme (non-AI based beam reporting).
  • the UE evaluates the performance of the monitored/reported model and fallback scheme (non-AI based beam reporting).
  • the UE reports the monitored performance to the NW.
  • the UE sends a request to the NW regarding which model should be applied or whether a fallback scheme should be applied.
  • the UE may be instructed which scheme (model) is to be activated.
  • the UE may activate some model or fallback scheme.
  • FIG. 4 is a diagram illustrating an example of a life cycle management framework for performance monitoring in a BS according to an embodiment.
  • the UE measures RS for prediction (CSI-RS/SSB) and RS for performance monitoring.
  • the UE reports information for performance monitoring at the NW (BS).
  • the NW monitors the performance of the model and fallback scheme (non-AI based beam reporting).
  • the NW evaluates the performance of the model and the fallback scheme.
  • the UE may be instructed which scheme (model) is to be activated.
  • the UE may activate some model or fallback scheme.
  • the zeroth embodiment relates to RS measurement.
  • the set A described above may correspond to a set referred to for performance monitoring, and may be called a performance monitoring reference.
  • the RSs corresponding to set A may correspond to RSs whose quality is predicted by the AI model.
  • the set B described above may correspond to a set used to generate input for AI prediction.
  • the RS corresponding to set B may correspond to the RS used for actual channel measurement in beam prediction, and may be used for target CSI calculation.
  • the target CSI may be, for example, a CSI (calculated CSI) calculated based on channel measurements, or an ideal CSI (ideal CSI).
  • ideal CSI may mean an ideal CSI without error output from an AI model, or may mean CSI output from an AI model in response to a specific input (or any input). It can also mean The ideal CSI may also be called fixed CSI.
  • the L1-RSRP of set A and the L1-RSRP of set B can be calculated.
  • the UE may also input the L1-RSRP of Set B into the AI model to calculate the predicted L1-RSRP of Set A (which may be referred to as L1-RSRP').
  • the upper limit of the gain by the AI model can be evaluated. Furthermore, by comparing the L1-RSRP of set A and the L1-RSRP' of set A, the performance of the AI model can be evaluated. Also, by comparing L1-RSRP of set B and L1-RSRP' of set A, the gain by the AI model can be evaluated.
  • the UE In performance monitoring at the BS, the UE notifies the NW of L1-RSRP of set A, L1-RSRP of set B, L1-RSRP' of set A, etc., so that the same evaluation as performance monitoring at the UE can be performed. It is possible to do this.
  • the UE may be informed of CSI-RS resource/CSI-RS resource set/CSI resource configuration/CSI report configuration for beam prediction and CSI-RS resource/CSI-RS resource set for performance monitoring reference. /CSI resource settings/CSI report settings may be notified.
  • the mapping (correspondence) between CSI-RS resources for beam prediction and CSI-RS resources for performance monitoring reference may correspond to at least one of the following: - Both (CSI-RS resource for beam prediction and CSI-RS resource for performance monitoring reference) correspond to different CSI resource configurations (CSI-ResourceConfig), - Both correspond to a common CSI resource configuration (CSI-ResourceConfig), but correspond to different CSI-RS resource sets, - Both correspond to a common CSI-RS resource set, but correspond to different CSI-RS resources. - One CSI-RS resource includes both.
  • mapping for example, associated CSI-RS resources, associated CSI-RS resource sets
  • information regarding the mapping may be notified from the NW to the UE, or may be included in the AI model information.
  • the CSI-RS resource set may be mutually read as at least one of the NZP-CSI-RS-ResourceSet information element, the CSI-SSB-ResourceSet information element, and the CSI-IM-ResourceSet information element of RRC.
  • the CSI-RS resource may be interchanged with the NZP-CSI-RS-Resource information element of RRC.
  • CSI-RS Opportunity A method for determining a combination of a CSI-RS occasion for beam prediction and a CSI-RS occasion for performance monitoring reference used during performance monitoring (performance comparison) will be described. Note that the CSI-RS opportunity for performance monitoring may mean a CSI-RS opportunity for beam prediction/CSI-RS opportunity for performance monitoring reference.
  • the UE may determine CSI-RS opportunities for performance monitoring based on CSI reference resources. For example, the UE determines a CSI-RS opportunity for a most recent beam prediction/a CSI-RS opportunity for a performance monitoring reference no later than a CSI reference resource. It's okay.
  • a CSI-RS opportunity for beam prediction may correspond to a CSI-RS opportunity in which a predicted CSI time based on the CSI opportunity is not later than the CSI reference resource.
  • the predicted CSI time is at least one of a symbol, a slot, a subslot, a subframe, a second (millisecond), etc., corresponding to the output predicted by the AI model (the prediction is valid). may be expressed).
  • predicted CSI time may be read as predicted time.
  • the UE may determine the CSI-RS opportunity for beam prediction based on the relationship between the predicted CSI time and the CSI-RS opportunity for performance monitoring reference.
  • the UE first determines a CSI-RS opportunity to be referenced for performance monitoring reference that is no later than the CSI reference resource. The UE may then calculate the performance based on the CSI-RS opportunity for beam prediction that corresponds to a recent predicted CSI time that is not later than the CSI-RS opportunity referenced for the performance monitoring reference.
  • the UE first determines a CSI-RS opportunity for beam prediction that corresponds to a predicted CSI time that is not later than the CSI reference resource. The UE may then calculate the performance based on the CSI-RS opportunity for a recent performance monitoring reference that is no later than the predicted CSI time corresponding to the CSI-RS opportunity referenced for beam prediction.
  • time offset time difference between the CSI-RS opportunity for beam prediction and the corresponding predicted CSI time
  • information regarding the time offset (time difference) between the CSI-RS opportunity for beam prediction and the corresponding predicted CSI time may be specified in advance in the standard, or may be determined based on the UE capability, It may be notified from the NW to the UE, or it may be included in the AI model information (it may be determined based on the model).
  • the UE first determines a CSI-RS opportunity to be referenced for performance monitoring reference that is no later than the CSI reference resource. and the UE is not later than X time units or earlier than the CSI-RS opportunity referenced for the performance monitoring reference (in other words, within the range of X time units with respect to the CSI-RS opportunity). ) Performance may be calculated based on the CSI-RS opportunity for beam prediction corresponding to the predicted CSI time.
  • the UE first determines a CSI-RS opportunity for beam prediction that corresponds to a predicted CSI time that is no later than the CSI reference resource. and the UE is not later than X units of time or earlier than the predicted CSI time corresponding to the CSI-RS opportunity referred to for beam prediction (in other words, X units of time with respect to the CSI-RS opportunity)
  • the performance may be calculated based on the CSI-RS opportunities for performance monitoring reference (within a range of time).
  • the CSI-RS opportunity referred to for the performance monitoring reference that is first determined in Methods 2-1 to 2-4/CSI-RS opportunity for beam prediction may be determined based on Method 1. .
  • FIG. 5 is a diagram illustrating an example of CSI-RS opportunities for performance monitoring determined based on method 2-1 or 2-2.
  • opportunities #1 and #2 are shown as CSI-RS opportunities to be referenced for beam prediction
  • opportunities #a to #c are shown as CSI-RS opportunities to be referenced for performance monitoring reference.
  • opportunities #1 and #2 in the predicted CSI time instance candidates are shown as predicted CSI times corresponding to opportunities #1 and #2, respectively.
  • the CSI-RS opportunity referred to for performance monitoring reference is determined to be opportunity #c. Also, the CSI-RS opportunity referred to for beam prediction is determined to be opportunity #1.
  • the CSI-RS opportunity referred to for beam prediction is determined to be opportunity #2.
  • the CSI-RS opportunity referenced for performance monitoring reference is determined to be opportunity #c.
  • the CSI-RS opportunity referred to for performance monitoring reference is determined to be opportunity #e. Also, the CSI-RS opportunity referred to for beam prediction is determined to be opportunity #2.
  • the CSI-RS opportunity referred to for beam prediction is determined to be opportunity #2.
  • the CSI-RS opportunity referred to for performance monitoring reference is determined to be opportunity #e.
  • the description that the UE calculates the performance may be read as the BS calculates the performance.
  • the UE can appropriately perform performance monitoring.
  • the first embodiment relates to performance monitoring at the UE.
  • the UE may be informed by the network about which AI models/fallback schemes to monitor performance. Determination of the AI model to be monitored will be described later in a modification of the fifth embodiment.
  • fallback scheme may be interchanged with non-AI based beam reporting, beam reporting without prediction, etc.
  • the UE may monitor performance for certain models/fallback schemes.
  • a model whose performance is monitored may be referred to as a monitored model.
  • a model to be registered (to which registration is applied)/a model to be set may correspond to a model to be monitored/a model to be activated.
  • the monitored performance may be at least one of the following: (1) Actual L1-RSRP (no prediction) for performance monitoring reference; (2) Actual L1-RSRP used for input of AI model prediction (no prediction); (3) Predicted L1-RSRP by AI model, (4) L1-RSRP difference between the predicted L1-RSRP of the first place and the L1-RSRP of the first place for the performance monitoring reference; (5) the minimum L1-RSRP difference between one of the top K predicted L1-RSRPs and the first-ranked L1-RSRP for the performance monitoring reference; (6) whether the CRI/SSBRI associated with one of the top K predicted L1-RSRPs corresponds to the CRI/SSBRI corresponding to the first-ranked L1-RSRP for the performance monitoring reference; (7) whether the CRI/SSBRI associated with the first predicted L1-RSRP corresponds to one of the CRI/SSBRIs corresponding to the top K L1-RSRPs for the performance monitoring reference; (8) whether all CRI/SSBRIs
  • L1-RSRP, L1-SINR, and other metrics may be read interchangeably.
  • the X% percentile may mean the value located at the X% position of the entire data arranged from the smallest.
  • Values such as K may be included in the AI model information (may be determined based on the model).
  • the UE can appropriately perform performance monitoring.
  • the second embodiment relates to model evaluation at the UE.
  • the UE evaluates the performance of the model performance/fallback scheme mentioned in the first embodiment, which performance to report, which model/fallback scheme to request, and which model/fallback scheme to activate. At least one of the following may be determined.
  • the UE may check (evaluate) whether at least one of the following conditions is met for one or more monitored performances:
  • Condition 1 The monitored performance of the active/registered/configured model or fallback scheme is less/greater than the monitored performance of one of the inactive models or fallback schemes.
  • Condition 2 The monitored performance of the model to be registered/set is greater/less than the monitored performance of one of the fallback schemes.
  • Condition 3 The monitored performance of a certain monitored model (e.g., active model) or fallback scheme is less than a threshold;
  • Condition 4 The monitored performance of a certain monitored model (e.g., a passive model) or fallback scheme is greater than a threshold;
  • - Condition 5 The monitored performance of a certain monitored model or fallback scheme has changed more than Y times since the last performance report (sending).
  • - Condition 6 The monitored performance of a certain monitored model or fallback scheme falls below a threshold a certain number of times or more over a certain period of time.
  • the monitored performance may be read as the performance obtained by adding an offset X (X is, for example, a real number) to the monitored performance.
  • Offset X may be determined based on factors other than pure performance (reproducibility performance) (eg, unmonitored/monitor-free performance). By introducing an offset, it is possible to evaluate a model that comprehensively considers other factors.
  • unmonitored/unmonitored performance includes the overhead for channel measurements, the reliability (of the model/calculated value), the complexity of the model, the power consumption of the calculation, and the difference between prediction time and measurement time. It may correspond to at least one of offset and the like.
  • Values such as X, Y, threshold values may be specified in advance in the standard, may be determined based on the UE capabilities, may be notified to the UE from the NW, It may be included in the AI model information (may be determined based on the model). Information regarding values such as or may be specified/notified for non-AI based beam reporting.
  • Which (or which combination) of conditions 1 to 6 the UE should check may be specified/notified for each model/fallback scheme, or may be specified/notified for each model/group. , may be defined/notified for AI-based beam reporting or non-AI-based beam reporting.
  • Condition 6 may include, for example, the following steps: - starting a timer when the first counter counts that the monitored performance is less than the first value for a first number of times or more; - Stop the timer when the second counter counts a second number of times or more that the monitored performance is greater than a second value while the timer is running; - resetting the second counter if the monitored performance is less than the first value while the timer is running; - resetting the first counter if the monitored performance is greater than the first value; - When the timer expires, the performance of the monitored model is evaluated as poor.
  • the first value may be a first threshold value (threshold out ) or a first offset (offset out ) from a reference value (baseline value) for a specific model/non-AI based beam report. may be as low as the value.
  • the second value may also be a second threshold or a second offset from a baseline value for the particular model/non-AI based beam report. It may be just as big.
  • resetting the counter may mean setting the counter to a specific value (for example, 0).
  • values such as the first/second threshold, baseline value, first/second offset, first/second counter, counter granularity, timer time length, etc. may be defined in advance in the standard, may be determined based on the UE capabilities, may be notified from the NW to the UE, or may be included in the AI model information (determined based on the model). ). Information regarding these values may be specified/notified for each model/non-AI based beam report, for each group of model/non-AI based beam reports, or for each AI-based beam report or May be specified/notified for non-AI based beam reporting.
  • FIG. 7 is a diagram showing an example of model evaluation in the second embodiment.
  • the monitored performance is initially good, but when the first counter counts that it is less than a first value a first number of times, a timer is started. Thereafter, the second counter counts a number of times that the monitored performance is greater than the second value while the timer is running, but the second counter does not exceed the second number of times, and the timer has expired, and the performance of this model is evaluated as low.
  • the UE evaluates the performance of one or more models/fallback schemes and selects the top K (K is an integer) performance for reporting/model request/model activation/model deactivation. You may select (determine).
  • K performances may be selected from all of the performances of AI-based beam reports, all of them may be selected from the performances of non-AI-based beam reports, or all of the performances of AI-based beam reports and non-AI-based beam reports. It may be selected based on performance.
  • the UE evaluates the performance of one or more CSI feedback methods, determines the top K (K is an integer) performance from the performance of the AI-based beam report, and determines the top K performance from the performance of the non-AI based beam report.
  • '(K' is an integer) performances may be determined.
  • Values such as K and K' may be specified in advance in the standard, may be determined based on the UE capabilities, may be notified to the UE from the NW, or may be notified to the UE from the NW. It may be included in model information (may be determined based on the model).
  • the UE may derive performance based on one or more monitored performances and one or more unmonitored/unmonitored performances.
  • monitored performance may be averaged/weighted over a period of time when evaluated/compared.
  • Information regarding the period, averaging/weighting method, etc. may be specified in advance in the standard, may be determined based on the UE capability, may be notified from the NW to the UE, or may be based on AI model information. (may be determined based on the model).
  • the UE can appropriately perform model evaluation.
  • the third embodiment relates to performance reporting.
  • the UE may transmit a performance report based on information notified from the NW. For example, the UE may transmit performance reports in periodically/semi-persistent/aperiodically scheduled uplink resources based on RRC/MAC CE/DCI. In this case, the performance report may be included in the UCI. At this time, the UE may determine the reporting period/offset based on the RRC/MAC CE/DCI.
  • the UE may itself determine the trigger related to the performance report and transmit the performance report when the trigger is triggered. For example, the UE may transmit a performance report if the conditions described in the second embodiment (eg, at least one of conditions 1-6) are met. In this case, the performance report may be included in the MAC CE (because it can be transmitted if the PUSCH is scheduled).
  • the UE may send performance reports when a new model is activated/registered/configured.
  • the UE may also transmit a performance report when a timer based on the configured/specified parameters (eg, the timer shown in condition 6) expires.
  • the performance report may include information indicating one or more of the monitored performances shown in the first embodiment.
  • the number of pieces of information indicating the monitored performance included in the performance report may be determined based on K (/K') shown in the second embodiment.
  • the performance report may include information (eg, model ID, registered model ID, CSI report setting ID) indicating the model/non-AI based beam report corresponding to the reported performance.
  • information eg, model ID, registered model ID, CSI report setting ID
  • the UE may determine the reported performance based on at least one of the following: - Performance evaluated under the conditions described in the second embodiment (for example, at least one of conditions 1-6), - Performance selected based on K (/K') shown in the second embodiment, -Performance determined to be reported based on notification from NW.
  • the above notification may be, for example, an activation command for model/non-AI based beam reporting, or may be a notification including information indicating the reporting/monitoring target.
  • the UE may report the performance of activated/monitored models.
  • the UE may report the performance of model #2.
  • the UE may not report the performance of model #1.
  • the UE can appropriately transmit a performance report.
  • the fourth embodiment relates to model requests.
  • Model request transmission timing The transmission timing of the model request may be determined based on the performance report replaced with the model request in the description of the timing of the performance report in the third embodiment.
  • the model request may include information indicating the model/non-AI based beam report to be applied (eg, model ID, registered model ID, CSI report configuration ID).
  • the model/non-AI based beam report to be applied may correspond to the model/non-AI based beam report corresponding to the reported performance described in the third embodiment (note that the performance may not be reported). good).
  • the model/non-AI based beam report to be applied may be referred to as the recommended model/non-AI based beam report.
  • the UE may select a non-AI based beam report as the recommended model/non-AI based beam report after evaluating the performance of these model/non-AI based beam reports.
  • the UE may report a model request to the NW indicating non-AI based beam reporting.
  • the UE may transmit the information included in the performance report and the information included in the model request at the same time (for example, using one UCI/MAC CE).
  • the UE can appropriately transmit a model request.
  • the fifth embodiment relates to model activation/deactivation.
  • the UE may perform model activation/deactivation based on information notified from the NW. This information may be called a model activation/deactivation command and may be transmitted using RRC/MAC CE/DCI.
  • the model activation/deactivation command may include information indicating the model/non-AI based beam report to be activated/deactivated (e.g., model ID, registered model ID, CSI report configuration ID). .
  • the model activation/deactivation command may correspond to information indicating whether or not the model request described in the fourth embodiment has been accepted (for example, it may be called a model response).
  • the UE Upon sending a model request and receiving a corresponding model response, the UE activates the model indicated by the model request if the model response indicates acceptance of the model request, otherwise activates the model. May be deactivated.
  • the UE may decide to activate/deactivate model/non-AI based beam reporting on its own. For example, the UE may determine which model/non-AI based beam report to activate/deactivate if the conditions mentioned in the second embodiment (e.g., at least one of conditions 1-6) are met. .
  • the model/non-AI based beam report to activate/deactivate may correspond to the model/non-AI based beam report corresponding to the reported performance described in the third embodiment (note that the performance is not reported). ).
  • the UE may report information regarding the activated model or information indicating that the active model is changed to the NW. In this case, model requests/performance reports are no longer necessary, so a reduction in communication overhead can be expected.
  • the UE may expect only one model/non-AI based beam report for a certain function to be active, or may expect multiple model/non-AI based beam reports to be active.
  • the UE may apply the model to calculate the predicted L1-RSRP. Also, if a model is activated, the UE may not perform L1-RSRP calculation/reporting based on configured non-AI based beam reporting that is not for performance monitoring purposes.
  • the UE may apply a non-AI based beam reporting scheme to the L1-RSRP calculation.
  • the UE may be configured with information regarding the non-AI based beam reporting scheme to be applied if all models are not active (eg, using the RRC CSI report configuration information element).
  • the UE does not need to perform beam reporting.
  • Model application time active/deactive time
  • the UE may activate/deactivate a model during the model application time.
  • the model application time may correspond to the period from the start time (start point) to the end time (end point).
  • the start time may be at least one of the following: ⁇ The last symbol for which a model activation/deactivation command is received or X units of time after the last symbol, - The last symbol that transmits HARQ information (eg, HARQ-ACK) corresponding to the model activation/deactivation command or X units of time after the last symbol.
  • HARQ information eg, HARQ-ACK
  • unit time may be read as at least one of a symbol, slot, subslot, subframe, second (millisecond), etc.
  • the end time may be at least one of the following: ⁇ Y units of time after the corresponding start time, Y units of time after the corresponding model activation/deactivation command is applied/received, - Until a new model activation/deactivation command is applied/received.
  • Values such as X and Y may be specified in advance in the standard, may be determined based on the UE capabilities, may be notified to the UE from the NW, or may be specified in the model. It may be linked information (may be determined based on a model).
  • the model activation/deactivation command may include information regarding model application time (eg, information indicating X/Y).
  • the UE may restart the start time of the model application time (in response to the new model activation/deactivation command). (may be updated to the above start time based on the start time).
  • the start time of the model application time may be restarted in at least one of the following cases: ⁇ If the model information specified by the above new model activation/deactivation command is the same as the model being applied, - If the above new model activation/deactivation command does not include information regarding model information/application time.
  • the UE may use different methods for determining (or updating) the start time/end time in the case of activation and the case of deactivation.
  • the UE may use different methods for determining (or updating) the start time/end time in the case of model activation/deactivation and in the case of fallback scheme activation/deactivation.
  • the UE can appropriately control model activation/deactivation.
  • the UE uses the AI model to be monitored as activation (activation)/deactivation (deactivation) and monitor activation (activation)/deactivation. The determination may be made based on the content replaced by deactivation.
  • the UE may monitor the configured AI model only if at least one of the following is satisfied regarding the configured AI model: ⁇ Receive activation for the model; ⁇ Determine the activation of the model; ⁇ During the application period of the model, ⁇ Until the performance of the model is determined (in other words, the performance has not yet been determined), ⁇ Until the performance of the model is reported (in other words, the performance has not been reported yet). ⁇ During a certain period of time after receiving the activation command (monitor triggering signal) for the monitor of the relevant model.
  • the above-mentioned certain period may correspond to a period in which the above-mentioned model application period is read as a model monitoring application period. That is, model activation/deactivation and model monitor activation/deactivation may be controlled separately or simultaneously.
  • the UE can appropriately control the model monitor.
  • the sixth embodiment relates to reporting for performance monitoring from the UE and performance monitoring at the BS.
  • the UE may be informed of CSI-RS resources/CSI-RS resource sets/CSI resource configurations/CSI report configurations for performance monitoring (reporting).
  • the UE may report the following for performance monitoring for certain models at the BS: ⁇ L1-RSRP predicted by the model, - measured L1-RSRP for the corresponding performance monitoring reference; - CRI/SSBRI for performance monitoring.
  • the L1-RSRP to be reported may be selected (determined) based on at least one of the following: - All predicted L1-RSRPs configured/designated to be reported; A predicted CSI time that is not later than or earlier than the CSI-RS opportunity referenced for performance monitoring reference (in other words, within X time units relative to the CSI-RS opportunity) The predicted L1-RSRP corresponding to .
  • the latter may correspond to judgment based on the same criteria as method 2-3 of the zeroth embodiment.
  • the reported CRI/SSBRI may be determined independently or jointly for the predicted L1-RSRP and the performance monitoring reference (L1-RSRP). In the latter case, the UE may determine the reported CRI/SSBRI based on the predicted L1-RSRP or based on the performance monitoring reference.
  • the UE may report the predicted L1-RSRP and the L1-RSRP for the corresponding performance monitoring reference using different CSI reports or using the same CSI report. It's okay.
  • one of the predicted L1-RSRP and the L1-RSRP for the corresponding performance monitoring reference may be expressed as a difference value (differential RSRP) from the other.
  • FIG. 8 is a diagram showing an example of a CSI report according to the sixth embodiment.
  • the highest value among the predicted L1-RSRPs is expressed as #1 as an absolute value, and the other predicted L1-RSRPs are expressed as differences from the highest value.
  • the L1-RSRP for the performance monitoring reference is also expressed as a difference from the maximum value.
  • the UE may use a different (eg, finer, more, expanded) granularity for L1-RSRP reporting for performance monitoring than the existing standard definition.
  • the UE may use different granularity than existing standards for at least one of the following parameters: - Step size for L1-RSRP (e.g. in dB), ⁇ The range of L1-RSRP that can be expressed (minimum value dBm, maximum value dBm), - Number that can be reported in one report (eg nrofReportedRS).
  • the UE may apply the different granularity above only if some AI models for beam prediction are registered/configured in the UE.
  • the BS can appropriately perform performance monitoring.
  • AI model information may mean information including at least one of the following: ⁇ AI model input/output information, ⁇ Pre-processing/post-processing information for AI model input/output, ⁇ Information on AI model parameters, ⁇ Training information for AI models (training information), ⁇ Inference information for AI models, ⁇ Performance information regarding AI models.
  • the input/output information of the AI model may include information regarding at least one of the following: - Contents of input/output data (e.g. RSRP, SINR, amplitude/phase information in channel matrix (or precoding matrix), information on angle of arrival (AoA), angle of departure (AoD)) ), location information), ⁇ Data auxiliary information (may be called meta information), - type of input/output data (e.g. immutable value, floating point number), - bit width of input/output data (e.g. 64 bits for each input value), - Quantization interval (quantization step size) of input/output data (for example, 1 dBm for L1-RSRP), - The range that input/output data can take (for example, [0, 1]).
  • - Contents of input/output data e.g. RSRP, SINR, amplitude/phase information in channel matrix (or precoding matrix), information on angle of arrival (AoA), angle of departure (AoD))
  • the information regarding AoA may include information regarding at least one of the azimuth angle of arrival and the zenith angle of arrival (ZoA). Further, the information regarding the AoD may include, for example, information regarding at least one of a radial azimuth angle of departure and a radial zenith angle of depth (ZoD).
  • the location information may be location information regarding the UE/NW.
  • Location information includes information (e.g., latitude, longitude, altitude) obtained using a positioning system (e.g., Global Navigation Satellite System (GNSS), Global Positioning System (GPS), etc.), and information (e.g., latitude, longitude, altitude) adjacent to the UE.
  • GNSS Global Navigation Satellite System
  • GPS Global Positioning System
  • Information on the serving (or serving) BS e.g., BS/cell identifier (ID), BS-UE distance, direction/angle of the BS (UE) as seen from the UE (BS),
  • the information may include at least one of the coordinates of the BS (UE) as seen from the BS (e.g., X/Y/Z axis coordinates, etc.), the specific address of the UE (e.g., Internet Protocol (IP) address), etc.
  • IP Internet Protocol
  • the location information of the UE is not limited to information based on the location of the BS, but may be information based on a specific point.
  • the location information may include information regarding its own implementation (for example, location/position/orientation of antennas, location/orientation of antenna panels, number of antennas, number of antenna panels, etc.).
  • the location information may include mobility information.
  • the mobility information may include information indicating at least one of the mobility type, the moving speed of the UE, the acceleration of the UE, the moving direction of the UE, and the like.
  • the mobility types are fixed location UE, movable/moving UE, no mobility UE, low mobility UE, and medium mobility UE.
  • environmental information may be information regarding the environment in which the data is acquired/used, such as frequency information (band ID, etc.), environment type information (indoor, etc.). , outdoor, Urban Macro (UMa), Urban Micro (Umi), etc.), Line Of Site (LOS)/Non-Line Of Site (NLOS), etc. Good too.
  • frequency information band ID, etc.
  • environment type information indoor, etc.
  • outdoor Urban Macro (UMa), Urban Micro (Umi), etc.
  • LOS Line Of Site
  • NLOS Non-Line Of Site
  • LOS may mean that the UE and BS are in an environment where they can see each other (or there is no shielding), and NLOS may mean that the UE and BS are not in an environment where they can see each other (or there is a shield). It can also mean The information indicating LOS/NLOS may indicate a soft value (for example, probability of LOS/NLOS) or may indicate a hard value (for example, either LOS/NLOS).
  • meta information may mean, for example, information regarding input/output information suitable for an AI model, information regarding acquired/obtainable data, etc.
  • the meta information includes information regarding beams of RS (for example, CSI-RS/SRS/SSB, etc.) (for example, the pointing angle of each beam, 3 dB beam width, the shape of the pointing beam, (number of beams), gNB/UE antenna layout information, frequency information, environment information, meta information ID, etc.
  • RS for example, CSI-RS/SRS/SSB, etc.
  • the meta information may be used as input/output of the AI model.
  • the pre-processing/post-processing information for the input/output of the AI model may include information regarding at least one of the following: - whether to apply normalization (e.g., Z-score normalization, min-max normalization); - Parameters for normalization (e.g. mean/variance for Z-score normalization, minimum/maximum for min-max normalization), - Whether to apply a specific numerical conversion method (e.g. one hot encoding, label encoding, etc.); - Selection rules for whether or not to be used as training data.
  • normalization e.g., Z-score normalization, min-max normalization
  • Parameters for normalization e.g. mean/variance for Z-score normalization, minimum/maximum for min-max normalization
  • a specific numerical conversion method e.g. one hot encoding, label encoding, etc.
  • Selection rules for whether or not to be used as training data.
  • the information on the parameters of the AI model may include information regarding at least one of the following: ⁇ Weight (e.g. neuron coefficient (coupling coefficient)) information in the AI model, ⁇ Structure of the AI model, ⁇ Type of AI model as model component (e.g. ResNet, DenseNet, RefineNet, Transformer model, CRBlock, Recurrent Neural Network (RNN), Long Short Memory -Term Memory (LSTM)), Gated Recurrent Unit (GRU)), - Functions of the AI model as a model component (e.g. decoder, encoder).
  • ⁇ Weight e.g. neuron coefficient (coupling coefficient)
  • ⁇ Structure of the AI model e.g. ResNet, DenseNet, RefineNet, Transformer model, CRBlock, Recurrent Neural Network (RNN), Long Short Memory -Term Memory (LSTM)), Gated Recurrent Unit (GRU)
  • - Functions of the AI model as a model component e.g. decoder, encode
  • the weight information in the AI model may include information regarding at least one of the following: ⁇ Bit width (size) of weight information, ⁇ Quantization interval of weight information, - Granularity of weight information, ⁇ The range that weight information can take, ⁇ Weight parameters in the AI model, ⁇ Difference information from the AI model before update (if updating), ⁇ Weight initialization methods (e.g. zero initialization, random initialization (based on normal distribution/uniform distribution/truncated normal distribution), Xavier initialization (for sigmoid functions), He initialization (rectified) For Rectified Linear Units (ReLU)).
  • ⁇ Bit width (size) of weight information e.g. zero initialization, random initialization (based on normal distribution/uniform distribution/truncated normal distribution), Xavier initialization (for sigmoid functions), He initialization (rectified) For Rectified Linear Units (ReLU)).
  • the structure of the AI model may include information regarding at least one of the following: ⁇ Number of layers, - Type of layer (e.g. convolution layer, activation layer, dense layer, normalization layer, pooling layer, attention layer), ⁇ Layer information, - Time series specific parameters (e.g. bidirectionality, time step), - Parameters for training (e.g. type of function (L2 regularization, dropout function, etc.), where to put this function (e.g. after which layer)).
  • ⁇ Number of layers e.g. convolution layer, activation layer, dense layer, normalization layer, pooling layer, attention layer
  • ⁇ Layer information e.g. bidirectionality, time step
  • Parameters for training e.g. type of function (L2 regularization, dropout function, etc.), where to put this function (e.g. after which layer)).
  • the layer information may include information regarding at least one of the following: ⁇ Number of neurons in each layer, ⁇ Kernel size, ⁇ Stride for pooling layer/convolution layer, ⁇ Pooling method (MaxPooling, AveragePooling, etc.), ⁇ Residual block information, ⁇ Number of heads, ⁇ Normalization methods (batch normalization, instance normalization, layer normalization, etc.), - Activation function (sigmoid, tanh function, ReLU, leaky ReLU information, Maxout, Softmax).
  • an AI model may be included as a component of another AI model.
  • an AI model may be an AI model in which processing proceeds in the following order: ResNet as model component #1, a transformer model as model component #2, a dense layer, and a normalization layer.
  • the training information for the AI model may include information regarding at least one of the following: ⁇ Information for the optimization algorithm (e.g., optimization type (Stochastic Gradient Descent (SGD), AdaGrad, Adam, etc.), optimization parameters (learning rate, momentum, etc.) information, etc.), ⁇ Information on the loss function (for example, information on the metrics of the loss function (Mean Absolute Error (MAE), Mean Square Error (MSE)), cross entropy loss, NLLLoss, Kullback- Leibler (KL) divergence, etc.), parameters to be frozen for training (e.g. layers, weights), - parameters to be updated (e.g.
  • optimization type Stochastic Gradient Descent (SGD), AdaGrad, Adam, etc.
  • optimization parameters learning rate, momentum, etc.
  • ⁇ Information on the loss function for example, information on the metrics of the loss function (Mean Absolute Error (MAE), Mean Square Error (MSE)),
  • ⁇ Parameters e.g., layers, weights
  • How to train/update the AI model e.g. (recommended) number of epochs, batch size, number of data used for training.
  • the inference information for the AI model may include information regarding branch pruning of a decision tree, parameter quantization, functions of the AI model, and the like.
  • the function of the AI model may correspond to at least one of, for example, time domain beam prediction, spatial domain beam prediction, an autoencoder for CSI feedback, an autoencoder for beam management, etc.
  • An autoencoder for CSI feedback may be used as follows: - The UE inputs the CSI/channel matrix/precoding matrix into the encoder's AI model and transmits the output encoded bits as CSI feedback (CSI report). - The BS inputs the received encoded bits into the decoder's AI model and reconstructs the output CSI/channel matrix/precoding matrix.
  • the UE/BS inputs sparse (or thick) beam-based measurements (beam quality, e.g. RSRP) into an AI model and outputs dense (or thin) beam quality. It's okay.
  • beam quality e.g. RSRP
  • the UE/BS may input time-series (past, current, etc.) measurement results (beam quality, e.g. RSRP) to the AI model and output future beam quality.
  • time-series past, current, etc.
  • beam quality e.g. RSRP
  • the performance information regarding the AI model may include information regarding the expected value of a loss function defined for the AI model.
  • the AI model information in the present disclosure may include information regarding the applicable range (applicable range) of the AI model.
  • the applicable range may be indicated by a physical cell ID, a serving cell index, etc.
  • Information regarding the scope of application may be included in the above-mentioned environmental information.
  • AI model information regarding a specific AI model may be predefined in a standard, or may be notified to the UE from a network (NW).
  • the AI model defined in the standard may be called a reference AI model.
  • AI model information regarding the reference AI model may be referred to as reference AI model information.
  • the AI model information in the present disclosure may include an index (for example, may be referred to as an AI model index, AI model ID, model ID, etc.) for identifying an AI model.
  • the AI model information in the present disclosure may include an AI model index in addition to/instead of the input/output information of the AI model described above.
  • the association between the AI model index and the AI model information (for example, input/output information of the AI model) may be predetermined in the standard, or may be notified from the NW to the UE.
  • the AI model information in the present disclosure may be associated with the AI model, and may also be referred to as AI model relevant information, simply related information, or the like.
  • the AI model related information does not need to explicitly include information for identifying the AI model.
  • the AI model related information may be information containing only meta information, for example.
  • a model ID may be mutually read as an ID corresponding to a set of AI models (model set ID). Further, in the present disclosure, the model ID may be interchanged with the meta information ID.
  • the meta information (or meta information ID) may be associated with beam-related information (beam settings) as described above. For example, the meta information (or meta information ID) may be used by the UE to select an AI model considering which beams the BS is using, or the UE may apply the deployed AI model. It may be used to inform the BS which beam to use for this purpose.
  • the meta information ID may be interchanged with an ID corresponding to a set of meta information (meta information set ID).
  • the notification of any information to the UE is performed using physical layer signaling (e.g., DCI), upper layer signaling (e.g., RRC).
  • MAC CE MAC CE
  • specific signals/channels eg, PDCCH, PDSCH, reference signals
  • the MAC CE may be identified by including a new logical channel ID (LCID), which is not specified in the existing standard, in the MAC subheader.
  • LCID logical channel ID
  • the above notification When the above notification is performed by a DCI, the above notification includes a specific field of the DCI, a radio network temporary identifier (Radio Network Temporary Identifier (RNTI)), the format of the DCI, etc.
  • RNTI Radio Network Temporary Identifier
  • notification of any information to the UE in the above embodiments may be performed periodically, semi-persistently, or aperiodically.
  • notification of any information from the UE is performed using physical layer signaling (e.g., UCI), upper layer signaling (e.g., RRC).
  • MAC CE MAC CE
  • specific signals/channels eg, PUCCH, PUSCH, reference signals
  • the MAC CE may be identified by including a new LCID that is not defined in the existing standard in the MAC subheader.
  • the above notification may be transmitted using PUCCH or PUSCH.
  • notification of arbitrary information from the UE in the above embodiments may be performed periodically, semi-persistently, or aperiodically.
  • At least one of the embodiments described above may be applied if certain conditions are met.
  • the specific conditions may be specified in the standard, or may be notified to the UE/BS using upper layer signaling/physical layer signaling.
  • At least one of the embodiments described above may be applied only to UEs that have reported or support a particular UE capability.
  • the particular UE capability may indicate at least one of the following: - supporting specific processing/operation/control/information for at least one of the above embodiments; ⁇ Number of AI models to be monitored (may be a value for each function), ⁇ Maximum floating point operations (FLOPs (note that s is lowercase)) of the AI model that the UE can deploy (this means the amount of floating point operations), ⁇ Maximum number of parameters of AI model that UE can deploy, ⁇ Layers/algorithms/functions supported by the UE, ⁇ Calculating ability, ⁇ Data collection ability.
  • the specific UE capability may be a capability that is applied across all frequencies (commonly regardless of frequency) or a capability that is applied across all frequencies (e.g., cell, band, band combination, BWP, component carrier, etc.). or a combination thereof), or it may be a capability for each frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2). Alternatively, it may be a capability for each subcarrier spacing (SCS), or a capability for each Feature Set (FS) or Feature Set Per Component-carrier (FSPC).
  • SCS subcarrier spacing
  • FS Feature Set
  • FSPC Feature Set Per Component-carrier
  • the above-mentioned specific UE capability may be a capability that is applied across all duplex schemes (commonly regardless of the duplex scheme), or may be a capability that is applied across all duplex schemes (for example, Time Division Duplex).
  • the capability may be for each frequency division duplex (TDD)) or frequency division duplex (FDD)).
  • the UE configures/activates specific information related to the embodiment described above (or performs the operation of the embodiment described above) by upper layer signaling/physical layer signaling. / May be applied when triggered.
  • the specific information may be information indicating that the use of the AI model is enabled, arbitrary RRC parameters for a specific release (for example, Rel. 18), or the like.
  • the UE does not support at least one of the specific UE capabilities or is not configured with the specific information, for example, Rel. 15/16 operations may be applied.
  • a receiving unit that receives configuration information for performance monitoring regarding artificial intelligence (AI) based beam management (BM);
  • a terminal comprising: a control unit that controls the performance monitoring.
  • a terminal comprising: a control unit that controls the performance monitoring.
  • the terminal according to supplementary note 1, wherein the control unit monitors the performance of the AI-based BM based on a reference signal resource for beam prediction and a reference signal for performance monitoring reference that have a specific correspondence relationship.
  • the control unit determines the other based on one of a CSI-RS opportunity for beam prediction and a CSI-RS opportunity for performance monitoring reference, and determines the performance of the AI-based BM based on these CSI opportunities.
  • the terminal described in Appendix 1 or 2 to be monitored.
  • a receiving unit that receives configuration information for reporting for performance monitoring regarding artificial intelligence (AI) based beam management (BM);
  • a terminal comprising: a control unit that controls transmission of a report regarding Layer 1 Reference Signal Received Power (L1-RSRP) measured based on the configuration information.
  • L1-RSRP Layer 1 Reference Signal Received Power
  • the control unit includes in the report a predicted L1-RSRP corresponding to a predicted time that is not later than a certain unit time or is not earlier than a certain unit time than a reference signal opportunity referred to for a performance monitoring reference. The device listed.
  • wireless communication system The configuration of a wireless communication system according to an embodiment of the present disclosure will be described below.
  • communication is performed using any one of the wireless communication methods according to the above-described embodiments of the present disclosure or a combination thereof.
  • FIG. 9 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment.
  • 5G NR 5th generation mobile communication system New Radio
  • 3GPP Third Generation Partnership Project
  • the wireless communication system 1 may support dual connectivity between multiple Radio Access Technologies (RATs) (Multi-RAT Dual Connectivity (MR-DC)).
  • MR-DC has dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (EN-DC)), and dual connectivity between NR and LTE (NR-E -UTRA Dual Connectivity (NE-DC)).
  • RATs Radio Access Technologies
  • MR-DC has dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (EN-DC)), and dual connectivity between NR and LTE (NR-E -UTRA Dual Connectivity (NE-DC)).
  • E-UTRA Evolved Universal Terrestrial Radio Access
  • EN-DC E-UTRA-NR Dual Connectivity
  • NE-DC NR-E -UTRA Dual Connectivity
  • the LTE (E-UTRA) base station (eNB) is the master node (Master Node (MN)), and the NR base station (gNB) is the secondary node (Secondary Node (SN)).
  • the NR base station (gNB) is the MN
  • the LTE (E-UTRA) base station (eNB) is the SN.
  • the wireless communication system 1 has dual connectivity between multiple base stations within the same RAT (for example, dual connectivity (NR-NR Dual Connectivity (NN-DC) where both the MN and SN are NR base stations (gNB)). )) may be supported.
  • dual connectivity NR-NR Dual Connectivity (NN-DC) where both the MN and SN are NR base stations (gNB)).
  • the wireless communication system 1 includes a base station 11 that forms a macro cell C1 with relatively wide coverage, and base stations 12 (12a-12c) that are located within the macro cell C1 and form a small cell C2 that is narrower than the macro cell C1. You may prepare.
  • User terminal 20 may be located within at least one cell. The arrangement, number, etc. of each cell and user terminal 20 are not limited to the embodiment shown in the figure. Hereinafter, when base stations 11 and 12 are not distinguished, they will be collectively referred to as base station 10.
  • the user terminal 20 may be connected to at least one of the plurality of base stations 10.
  • the user terminal 20 may utilize at least one of carrier aggregation (CA) using a plurality of component carriers (CC) and dual connectivity (DC).
  • CA carrier aggregation
  • CC component carriers
  • DC dual connectivity
  • Each CC may be included in at least one of a first frequency band (Frequency Range 1 (FR1)) and a second frequency band (Frequency Range 2 (FR2)).
  • Macro cell C1 may be included in FR1
  • small cell C2 may be included in FR2.
  • FR1 may be a frequency band below 6 GHz (sub-6 GHz)
  • FR2 may be a frequency band above 24 GHz (above-24 GHz). Note that the frequency bands and definitions of FR1 and FR2 are not limited to these, and FR1 may correspond to a higher frequency band than FR2, for example.
  • the user terminal 20 may communicate using at least one of time division duplex (TDD) and frequency division duplex (FDD) in each CC.
  • TDD time division duplex
  • FDD frequency division duplex
  • the plurality of base stations 10 may be connected by wire (for example, optical fiber, X2 interface, etc. compliant with Common Public Radio Interface (CPRI)) or wirelessly (for example, NR communication).
  • wire for example, optical fiber, X2 interface, etc. compliant with Common Public Radio Interface (CPRI)
  • NR communication for example, when NR communication is used as a backhaul between base stations 11 and 12, base station 11, which is an upper station, is an Integrated Access Backhaul (IAB) donor, and base station 12, which is a relay station, is an IAB donor. May also be called a node.
  • IAB Integrated Access Backhaul
  • the base station 10 may be connected to the core network 30 via another base station 10 or directly.
  • the core network 30 may include, for example, at least one of Evolved Packet Core (EPC), 5G Core Network (5GCN), Next Generation Core (NGC), and the like.
  • EPC Evolved Packet Core
  • 5GCN 5G Core Network
  • NGC Next Generation Core
  • Core Network 30 is, for example, User Plane Function (UPF), Access and Mobility Management Function (AMF), Session Management (SMF), Unified Data Management. T (UDM), ApplicationFunction (AF), Data Network (DN), Location Management Network Functions (NF) such as Function (LMF) and Operation, Administration and Maintenance (Management) (OAM) may also be included.
  • UPF User Plane Function
  • AMF Access and Mobility Management Function
  • SMF Session Management
  • UDM Unified Data Management.
  • AF ApplicationFunction
  • DN Data Network
  • NF Location Management Network Functions
  • NF Location Management Network Functions
  • LMF Location Management Network Functions
  • OAM Operation, Administration and Maintenance
  • the user terminal 20 may be a terminal compatible with at least one of communication systems such as LTE, LTE-A, and 5G.
  • an orthogonal frequency division multiplexing (OFDM)-based wireless access method may be used.
  • OFDM orthogonal frequency division multiplexing
  • CP-OFDM Cyclic Prefix OFDM
  • DFT-s-OFDM Discrete Fourier Transform Spread OFDM
  • OFDMA Orthogonal Frequency Division Multiple Access
  • SC-FDMA Single Carrier Frequency Division Multiple Access
  • a wireless access method may also be called a waveform.
  • other wireless access methods for example, other single carrier transmission methods, other multicarrier transmission methods
  • the UL and DL radio access methods may be used as the UL and DL radio access methods.
  • the downlink channels include a physical downlink shared channel (PDSCH) shared by each user terminal 20, a broadcast channel (physical broadcast channel (PBCH)), and a downlink control channel (physical downlink control). Channel (PDCCH)) or the like may be used.
  • PDSCH physical downlink shared channel
  • PBCH physical broadcast channel
  • PDCCH downlink control channel
  • uplink channels include a physical uplink shared channel (PUSCH) shared by each user terminal 20, a physical uplink control channel (PUCCH), and a random access channel. (Physical Random Access Channel (PRACH)) or the like may be used.
  • PUSCH physical uplink shared channel
  • PUCCH physical uplink control channel
  • PRACH Physical Random Access Channel
  • User data, upper layer control information, System Information Block (SIB), etc. are transmitted by the PDSCH.
  • User data, upper layer control information, etc. may be transmitted by PUSCH.
  • a Master Information Block (MIB) may be transmitted by PBCH.
  • Lower layer control information may be transmitted by PDCCH.
  • the lower layer control information may include, for example, downlink control information (DCI) that includes scheduling information for at least one of PDSCH and PUSCH.
  • DCI downlink control information
  • DCI that schedules PDSCH may be called DL assignment, DL DCI, etc.
  • DCI that schedules PUSCH may be called UL grant, UL DCI, etc.
  • PDSCH may be replaced with DL data
  • PUSCH may be replaced with UL data.
  • a control resource set (CONtrol REsource SET (CORESET)) and a search space may be used to detect the PDCCH.
  • CORESET corresponds to a resource for searching DCI.
  • the search space corresponds to a search area and a search method for PDCCH candidates (PDCCH candidates).
  • PDCCH candidates PDCCH candidates
  • One CORESET may be associated with one or more search spaces. The UE may monitor the CORESET associated with a certain search space based on the search space configuration.
  • One search space may correspond to PDCCH candidates corresponding to one or more aggregation levels.
  • One or more search spaces may be referred to as a search space set. Note that “search space”, “search space set”, “search space setting”, “search space set setting”, “CORESET”, “CORESET setting”, etc. in the present disclosure may be read interchangeably.
  • the PUCCH allows channel state information (CSI), delivery confirmation information (for example, may be called Hybrid Automatic Repeat Request ACKnowledgement (HARQ-ACK), ACK/NACK, etc.), and scheduling request ( Uplink Control Information (UCI) including at least one of SR)) may be transmitted.
  • CSI channel state information
  • delivery confirmation information for example, may be called Hybrid Automatic Repeat Request ACKnowledgement (HARQ-ACK), ACK/NACK, etc.
  • UCI Uplink Control Information including at least one of SR
  • a random access preamble for establishing a connection with a cell may be transmitted by PRACH.
  • downlinks, uplinks, etc. may be expressed without adding "link”.
  • various channels may be expressed without adding "Physical” at the beginning.
  • a synchronization signal (SS), a downlink reference signal (DL-RS), and the like may be transmitted.
  • the DL-RS includes a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS), and a demodulation reference signal (DeModulation).
  • Reference Signal (DMRS)), Positioning Reference Signal (PRS), Phase Tracking Reference Signal (PTRS), etc. may be transmitted.
  • the synchronization signal may be, for example, at least one of a primary synchronization signal (PSS) and a secondary synchronization signal (SSS).
  • a signal block including SS (PSS, SSS) and PBCH (and DMRS for PBCH) may be called an SS/PBCH block, SS Block (SSB), etc. Note that SS, SSB, etc. may also be called reference signals.
  • DMRS Downlink Reference Signal
  • UL-RS uplink reference signals
  • SRS Sounding Reference Signal
  • DMRS demodulation reference signals
  • UE-specific reference signal user terminal-specific reference signal
  • FIG. 10 is a diagram illustrating an example of the configuration of a base station according to an embodiment.
  • the base station 10 includes a control section 110, a transmitting/receiving section 120, a transmitting/receiving antenna 130, and a transmission line interface 140. Note that one or more of each of the control unit 110, the transmitting/receiving unit 120, the transmitting/receiving antenna 130, and the transmission path interface 140 may be provided.
  • this example mainly shows functional blocks that are characteristic of the present embodiment, and it may be assumed that the base station 10 also has other functional blocks necessary for wireless communication. A part of the processing of each unit described below may be omitted.
  • the control unit 110 controls the entire base station 10.
  • the control unit 110 can be configured from a controller, a control circuit, etc., which will be explained based on common recognition in the technical field related to the present disclosure.
  • the control unit 110 may control signal generation, scheduling (e.g., resource allocation, mapping), and the like.
  • the control unit 110 may control transmission and reception, measurement, etc. using the transmitting/receiving unit 120, the transmitting/receiving antenna 130, and the transmission path interface 140.
  • the control unit 110 may generate data, control information, a sequence, etc. to be transmitted as a signal, and may transfer the generated data to the transmitting/receiving unit 120.
  • the control unit 110 may perform communication channel call processing (setting, release, etc.), status management of the base station 10, radio resource management, and the like.
  • the transmitting/receiving section 120 may include a baseband section 121, a radio frequency (RF) section 122, and a measuring section 123.
  • the baseband section 121 may include a transmission processing section 1211 and a reception processing section 1212.
  • the transmitter/receiver unit 120 includes a transmitter/receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transmitter/receiver circuit, etc., which are explained based on common understanding in the technical field related to the present disclosure. be able to.
  • the transmitting/receiving section 120 may be configured as an integrated transmitting/receiving section, or may be configured from a transmitting section and a receiving section.
  • the transmitting section may include a transmitting processing section 1211 and an RF section 122.
  • the reception section may include a reception processing section 1212, an RF section 122, and a measurement section 123.
  • the transmitting/receiving antenna 130 can be configured from an antenna described based on common recognition in the technical field related to the present disclosure, such as an array antenna.
  • the transmitter/receiver 120 may transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc.
  • the transmitter/receiver 120 may receive the above-mentioned uplink channel, uplink reference signal, and the like.
  • the transmitting/receiving unit 120 may form at least one of a transmitting beam and a receiving beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.
  • digital beamforming e.g., precoding
  • analog beamforming e.g., phase rotation
  • the transmitting/receiving unit 120 (transmission processing unit 1211) performs Packet Data Convergence Protocol (PDCP) layer processing, Radio Link Control (RLC) layer processing (for example, RLC retransmission control), Medium Access Control (MAC) layer processing (for example, HARQ retransmission control), etc. may be performed to generate a bit string to be transmitted.
  • PDCP Packet Data Convergence Protocol
  • RLC Radio Link Control
  • MAC Medium Access Control
  • HARQ retransmission control for example, HARQ retransmission control
  • the transmitting/receiving unit 120 performs channel encoding (which may include error correction encoding), modulation, mapping, filter processing, and discrete Fourier transform (DFT) on the bit string to be transmitted.
  • a baseband signal may be output by performing transmission processing such as processing (if necessary), Inverse Fast Fourier Transform (IFFT) processing, precoding, and digital-to-analog conversion.
  • IFFT Inverse Fast Fourier Transform
  • the transmitting/receiving unit 120 may perform modulation, filter processing, amplification, etc. on the baseband signal in a radio frequency band, and may transmit the signal in the radio frequency band via the transmitting/receiving antenna 130. .
  • the transmitting/receiving section 120 may perform amplification, filter processing, demodulation into a baseband signal, etc. on the radio frequency band signal received by the transmitting/receiving antenna 130.
  • the transmitting/receiving unit 120 (reception processing unit 1212) performs analog-to-digital conversion, fast Fourier transform (FFT) processing, and inverse discrete Fourier transform (IDFT) on the acquired baseband signal. )) processing (if necessary), applying reception processing such as filter processing, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing and PDCP layer processing, User data etc. may also be acquired.
  • FFT fast Fourier transform
  • IDFT inverse discrete Fourier transform
  • the transmitting/receiving unit 120 may perform measurements regarding the received signal.
  • the measurement unit 123 may perform Radio Resource Management (RRM) measurement, Channel State Information (CSI) measurement, etc. based on the received signal.
  • the measurement unit 123 is the receiving power (for example, Reference Signal Received Power (RSRP)), Receive Quality (eg, Reference Signal Received Quality (RSRQ), Signal To InterfERENCE PLUS NOI. SE RATIO (SINR), Signal to Noise Ratio (SNR) , signal strength (for example, Received Signal Strength Indicator (RSSI)), propagation path information (for example, CSI), etc. may be measured.
  • the measurement results may be output to the control unit 110.
  • the transmission path interface 140 transmits and receives signals (backhaul signaling) between devices included in the core network 30 (for example, network nodes providing NF), other base stations 10, etc., and provides information for the user terminal 20.
  • signals backhaul signaling
  • devices included in the core network 30 for example, network nodes providing NF, other base stations 10, etc.
  • User data user plane data
  • control plane data etc. may be acquired and transmitted.
  • the transmitting unit and receiving unit of the base station 10 in the present disclosure may be configured by at least one of the transmitting/receiving unit 120, the transmitting/receiving antenna 130, and the transmission path interface 140.
  • the transmitting/receiving unit 120 may transmit setting information for performance monitoring regarding artificial intelligence (AI) based beam management (BM).
  • the transmitting/receiving unit 120 may receive a report or a model request transmitted based on the performance monitoring.
  • AI artificial intelligence
  • BM beam management
  • the transmitting/receiving unit 120 may transmit setting information for reporting for performance monitoring regarding artificial intelligence (AI) based beam management (BM).
  • the transmitting/receiving unit 120 may receive a report regarding Layer 1 Reference Signal Received Power (L1-RSRP) measured based on the configuration information.
  • L1-RSRP Layer 1 Reference Signal Received Power
  • FIG. 11 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment.
  • the user terminal 20 includes a control section 210, a transmitting/receiving section 220, and a transmitting/receiving antenna 230. Note that one or more of each of the control unit 210, the transmitting/receiving unit 220, and the transmitting/receiving antenna 230 may be provided.
  • this example mainly shows functional blocks that are characteristic of the present embodiment, and it may be assumed that the user terminal 20 also has other functional blocks necessary for wireless communication. A part of the processing of each unit described below may be omitted.
  • the control unit 210 controls the entire user terminal 20.
  • the control unit 210 can be configured from a controller, a control circuit, etc., which will be explained based on common recognition in the technical field related to the present disclosure.
  • the control unit 210 may control signal generation, mapping, etc.
  • the control unit 210 may control transmission and reception using the transmitting/receiving unit 220 and the transmitting/receiving antenna 230, measurement, and the like.
  • the control unit 210 may generate data, control information, sequences, etc. to be transmitted as a signal, and may transfer the generated data to the transmitting/receiving unit 220.
  • the transmitting/receiving section 220 may include a baseband section 221, an RF section 222, and a measuring section 223.
  • the baseband section 221 may include a transmission processing section 2211 and a reception processing section 2212.
  • the transmitting/receiving unit 220 can be configured from a transmitter/receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measuring circuit, a transmitting/receiving circuit, etc., which are explained based on common recognition in the technical field related to the present disclosure.
  • the transmitting/receiving section 220 may be configured as an integrated transmitting/receiving section, or may be configured from a transmitting section and a receiving section.
  • the transmitting section may include a transmitting processing section 2211 and an RF section 222.
  • the reception section may include a reception processing section 2212, an RF section 222, and a measurement section 223.
  • the transmitting/receiving antenna 230 can be configured from an antenna, such as an array antenna, as described based on common recognition in the technical field related to the present disclosure.
  • the transmitter/receiver 220 may receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc.
  • the transmitter/receiver 220 may transmit the above-mentioned uplink channel, uplink reference signal, and the like.
  • the transmitting/receiving unit 220 may form at least one of a transmitting beam and a receiving beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.
  • digital beamforming e.g., precoding
  • analog beamforming e.g., phase rotation
  • the transmission/reception unit 220 (transmission processing unit 2211) performs PDCP layer processing, RLC layer processing (e.g. RLC retransmission control), MAC layer processing (e.g. , HARQ retransmission control), etc., to generate a bit string to be transmitted.
  • RLC layer processing e.g. RLC retransmission control
  • MAC layer processing e.g. , HARQ retransmission control
  • the transmitting/receiving unit 220 (transmission processing unit 2211) performs channel encoding (which may include error correction encoding), modulation, mapping, filter processing, DFT processing (as necessary), and IFFT processing on the bit string to be transmitted. , precoding, digital-to-analog conversion, etc., and output a baseband signal.
  • DFT processing may be based on the settings of transform precoding.
  • the transmitting/receiving unit 220 transmits the above processing in order to transmit the channel using the DFT-s-OFDM waveform.
  • DFT processing may be performed as the transmission processing, or if not, DFT processing may not be performed as the transmission processing.
  • the transmitting/receiving unit 220 may perform modulation, filter processing, amplification, etc. on the baseband signal in a radio frequency band, and may transmit the signal in the radio frequency band via the transmitting/receiving antenna 230. .
  • the transmitting/receiving section 220 may perform amplification, filter processing, demodulation into a baseband signal, etc. on the radio frequency band signal received by the transmitting/receiving antenna 230.
  • the transmission/reception unit 220 (reception processing unit 2212) performs analog-to-digital conversion, FFT processing, IDFT processing (if necessary), filter processing, demapping, demodulation, and decoding (error correction) on the acquired baseband signal. (which may include decoding), MAC layer processing, RLC layer processing, and PDCP layer processing may be applied to obtain user data and the like.
  • the transmitting/receiving unit 220 may perform measurements regarding the received signal.
  • the measurement unit 223 may perform RRM measurement, CSI measurement, etc. based on the received signal.
  • the measurement unit 223 may measure received power (for example, RSRP), reception quality (for example, RSRQ, SINR, SNR), signal strength (for example, RSSI), propagation path information (for example, CSI), and the like.
  • the measurement results may be output to the control unit 210.
  • the transmitting unit and receiving unit of the user terminal 20 in the present disclosure may be configured by at least one of the transmitting/receiving unit 220 and the transmitting/receiving antenna 230.
  • the transmitting/receiving unit 220 may receive setting information for performance monitoring regarding artificial intelligence (AI) based beam management (BM).
  • the control unit 210 may control the performance monitoring.
  • the control unit 210 may monitor the performance of the AI-based BM based on a reference signal resource for beam prediction and a reference signal for performance monitoring reference that have a specific correspondence relationship.
  • the control unit 210 determines the CSI-RS opportunity for beam prediction and the CSI-RS opportunity for performance monitoring reference based on the other, and determines the performance of the AI-based BM based on these CSI opportunities. May be monitored.
  • the transmitting/receiving unit 220 may also receive setting information for reporting for performance monitoring regarding artificial intelligence (AI) based beam management (BM).
  • the control unit 210 may control the transmission of a report regarding Layer 1 Reference Signal Received Power (L1-RSRP) measured based on the setting information.
  • L1-RSRP Layer 1 Reference Signal Received Power
  • the control unit 210 may include in the report a predicted L1-RSRP corresponding to a predicted time that is not later than a predetermined unit time or not earlier than a predetermined unit time than a reference signal occasion referenced for the performance monitoring reference. .
  • each functional block may be realized using one physically or logically coupled device, or may be realized using two or more physically or logically separated devices directly or indirectly (e.g. , wired, wireless, etc.) and may be realized using a plurality of these devices.
  • the functional block may be realized by combining software with the one device or the plurality of devices.
  • functions include judgment, decision, judgement, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, solution, selection, selection, establishment, comparison, assumption, expectation, and consideration. , broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, assigning, etc.
  • a functional block (configuration unit) that performs transmission may be called a transmitting unit, a transmitter, or the like. In either case, as described above, the implementation method is not particularly limited.
  • a base station, a user terminal, etc. in an embodiment of the present disclosure may function as a computer that performs processing of the wireless communication method of the present disclosure.
  • FIG. 12 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment.
  • the base station 10 and user terminal 20 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc. .
  • the hardware configuration of the base station 10 and the user terminal 20 may be configured to include one or more of each device shown in the figure, or may be configured not to include some of the devices.
  • processor 1001 may be implemented using one or more chips.
  • Each function in the base station 10 and the user terminal 20 is performed by, for example, loading predetermined software (program) onto hardware such as a processor 1001 and a memory 1002, so that the processor 1001 performs calculations and communicates via the communication device 1004. This is achieved by controlling at least one of reading and writing data in the memory 1002 and storage 1003.
  • predetermined software program
  • the processor 1001 operates an operating system to control the entire computer.
  • the processor 1001 may be configured by a central processing unit (CPU) that includes interfaces with peripheral devices, a control device, an arithmetic unit, registers, and the like.
  • CPU central processing unit
  • the above-mentioned control unit 110 (210), transmitting/receiving unit 120 (220), etc. may be realized by the processor 1001.
  • the processor 1001 reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 to the memory 1002, and executes various processes in accordance with these.
  • programs program codes
  • software modules software modules
  • data etc.
  • the control unit 110 may be realized by a control program stored in the memory 1002 and operated in the processor 1001, and other functional blocks may also be realized in the same way.
  • the memory 1002 is a computer-readable recording medium, and includes at least one of Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically EPROM (EEPROM), Random Access Memory (RAM), and other suitable storage media. It may be composed of one. Memory 1002 may be called a register, cache, main memory, or the like.
  • the memory 1002 can store executable programs (program codes), software modules, and the like to implement a wireless communication method according to an embodiment of the present disclosure.
  • the storage 1003 is a computer-readable recording medium, such as a flexible disk, a floppy (registered trademark) disk, a magneto-optical disk (for example, a compact disk (CD-ROM), etc.), a digital versatile disk, removable disk, hard disk drive, smart card, flash memory device (e.g., card, stick, key drive), magnetic stripe, database, server, or other suitable storage medium. It may be configured by Storage 1003 may also be called an auxiliary storage device.
  • a computer-readable recording medium such as a flexible disk, a floppy (registered trademark) disk, a magneto-optical disk (for example, a compact disk (CD-ROM), etc.), a digital versatile disk, removable disk, hard disk drive, smart card, flash memory device (e.g., card, stick, key drive), magnetic stripe, database, server, or other suitable storage medium. It may be configured by Storage 1003 may also be called an auxiliary storage device.
  • the communication device 1004 is hardware (transmission/reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc., for example.
  • the communication device 1004 includes, for example, a high frequency switch, a duplexer, a filter, a frequency synthesizer, etc. in order to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). It may be configured to include.
  • FDD frequency division duplex
  • TDD time division duplex
  • the transmitter/receiver 120 (220) may be physically or logically separated into a transmitter 120a (220a) and a receiver 120b (220b).
  • the input device 1005 is an input device (eg, keyboard, mouse, microphone, switch, button, sensor, etc.) that accepts input from the outside.
  • the output device 1006 is an output device (for example, a display, a speaker, a light emitting diode (LED) lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may have an integrated configuration (for example, a touch panel).
  • each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information.
  • the bus 1007 may be configured using a single bus, or may be configured using different buses for each device.
  • the base station 10 and user terminal 20 also include a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. It may be configured to include hardware, and a part or all of each functional block may be realized using the hardware. For example, processor 1001 may be implemented using at least one of these hardwares.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • PLD programmable logic device
  • FPGA field programmable gate array
  • channel, symbol and signal may be interchanged.
  • the signal may be a message.
  • the reference signal may also be abbreviated as RS, and may be called a pilot, pilot signal, etc. depending on the applicable standard.
  • a component carrier CC may be called a cell, a frequency carrier, a carrier frequency, or the like.
  • a radio frame may be composed of one or more periods (frames) in the time domain.
  • Each of the one or more periods (frames) constituting a radio frame may be called a subframe.
  • a subframe may be composed of one or more slots in the time domain.
  • a subframe may have a fixed time length (eg, 1 ms) that does not depend on numerology.
  • the numerology may be a communication parameter applied to at least one of transmission and reception of a certain signal or channel.
  • Numerology includes, for example, subcarrier spacing (SCS), bandwidth, symbol length, cyclic prefix length, transmission time interval (TTI), number of symbols per TTI, and radio frame structure. , a specific filtering process performed by the transceiver in the frequency domain, a specific windowing process performed by the transceiver in the time domain, etc.
  • a slot may be composed of one or more symbols (Orthogonal Frequency Division Multiplexing (OFDM) symbols, Single Carrier Frequency Division Multiple Access (SC-FDMA) symbols, etc.) in the time domain. Furthermore, a slot may be a time unit based on numerology.
  • OFDM Orthogonal Frequency Division Multiplexing
  • SC-FDMA Single Carrier Frequency Division Multiple Access
  • a slot may include multiple mini-slots. Each minislot may be made up of one or more symbols in the time domain. Furthermore, a mini-slot may also be called a sub-slot. A minislot may be made up of fewer symbols than a slot.
  • PDSCH (or PUSCH) transmitted in time units larger than minislots may be referred to as PDSCH (PUSCH) mapping type A.
  • PDSCH (or PUSCH) transmitted using minislots may be referred to as PDSCH (PUSCH) mapping type B.
  • Radio frames, subframes, slots, minislots, and symbols all represent time units when transmitting signals. Other names may be used for the radio frame, subframe, slot, minislot, and symbol. Note that time units such as frames, subframes, slots, minislots, and symbols in the present disclosure may be read interchangeably.
  • one subframe may be called a TTI
  • a plurality of consecutive subframes may be called a TTI
  • one slot or one minislot may be called a TTI.
  • at least one of the subframe and TTI may be a subframe (1ms) in existing LTE, a period shorter than 1ms (for example, 1-13 symbols), or a period longer than 1ms. It may be.
  • the unit representing the TTI may be called a slot, minislot, etc. instead of a subframe.
  • TTI refers to, for example, the minimum time unit for scheduling in wireless communication.
  • a base station performs scheduling to allocate radio resources (frequency bandwidth, transmission power, etc. that can be used by each user terminal) to each user terminal on a TTI basis.
  • radio resources frequency bandwidth, transmission power, etc. that can be used by each user terminal
  • the TTI may be a transmission time unit of a channel-coded data packet (transport block), a code block, a codeword, etc., or may be a processing unit of scheduling, link adaptation, etc. Note that when a TTI is given, the time interval (for example, the number of symbols) to which transport blocks, code blocks, code words, etc. are actually mapped may be shorter than the TTI.
  • one slot or one minislot is called a TTI
  • one or more TTIs may be the minimum time unit for scheduling.
  • the number of slots (minislot number) that constitutes the minimum time unit of the scheduling may be controlled.
  • a TTI having a time length of 1 ms may be called a normal TTI (TTI in 3GPP Rel. 8-12), normal TTI, long TTI, normal subframe, normal subframe, long subframe, slot, etc.
  • TTI TTI in 3GPP Rel. 8-12
  • normal TTI long TTI
  • normal subframe normal subframe
  • long subframe slot
  • TTI that is shorter than the normal TTI may be referred to as an abbreviated TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, minislot, subslot, slot, etc.
  • long TTI for example, normal TTI, subframe, etc.
  • short TTI for example, short TTI, etc. It may also be read as a TTI having the above TTI length.
  • a resource block is a resource allocation unit in the time domain and frequency domain, and may include one or more continuous subcarriers (subcarriers) in the frequency domain.
  • the number of subcarriers included in an RB may be the same regardless of the numerology, and may be 12, for example.
  • the number of subcarriers included in an RB may be determined based on numerology.
  • an RB may include one or more symbols in the time domain, and may have a length of one slot, one minislot, one subframe, or one TTI.
  • One TTI, one subframe, etc. may each be composed of one or more resource blocks.
  • one or more RBs include a physical resource block (Physical RB (PRB)), a sub-carrier group (SCG), a resource element group (REG), a PRB pair, and an RB. They may also be called pairs.
  • PRB Physical RB
  • SCG sub-carrier group
  • REG resource element group
  • PRB pair an RB. They may also be called pairs.
  • a resource block may be configured by one or more resource elements (REs).
  • REs resource elements
  • 1 RE may be a radio resource region of 1 subcarrier and 1 symbol.
  • Bandwidth Part (also called partial bandwidth, etc.) refers to a subset of consecutive common resource blocks (RB) for a certain numerology in a certain carrier.
  • the common RB may be specified by an RB index based on a common reference point of the carrier.
  • PRBs may be defined in a BWP and numbered within that BWP.
  • BWP may include UL BWP (BWP for UL) and DL BWP (BWP for DL).
  • BWP UL BWP
  • BWP for DL DL BWP
  • One or more BWPs may be configured within one carrier for a UE.
  • At least one of the configured BWPs may be active and the UE may not expect to transmit or receive a given signal/channel outside of the active BWP.
  • “cell”, “carrier”, etc. in the present disclosure may be replaced with "BWP”.
  • the structures of the radio frame, subframe, slot, minislot, symbol, etc. described above are merely examples.
  • the number of subframes included in a radio frame, the number of slots per subframe or radio frame, the number of minislots included in a slot, the number of symbols and RBs included in a slot or minislot, the number of symbols included in an RB The number of subcarriers, the number of symbols within a TTI, the symbol length, the cyclic prefix (CP) length, and other configurations can be changed in various ways.
  • radio resources may be indicated by a predetermined index.
  • data, instructions, commands, information, signals, bits, symbols, chips, etc. which may be referred to throughout the above description, may refer to voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any of these. It may also be represented by a combination of
  • information, signals, etc. may be output from the upper layer to the lower layer and from the lower layer to at least one of the upper layer.
  • Information, signals, etc. may be input and output via multiple network nodes.
  • Input/output information, signals, etc. may be stored in a specific location (for example, memory) or may be managed using a management table. Information, signals, etc. that are input and output can be overwritten, updated, or added. The output information, signals, etc. may be deleted. The input information, signals, etc. may be transmitted to other devices.
  • Notification of information is not limited to the aspects/embodiments described in this disclosure, and may be performed using other methods.
  • the notification of information in this disclosure may be physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), upper layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB), etc.), Medium Access Control (MAC) signaling), other signals, or a combination thereof It may be carried out by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), upper layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB), etc.), Medium Access Control (MAC) signaling), other signals, or a combination thereof It may be carried out by
  • the physical layer signaling may also be called Layer 1/Layer 2 (L1/L2) control information (L1/L2 control signal), L1 control information (L1 control signal), etc.
  • RRC signaling may be called an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.
  • MAC signaling may be notified using, for example, a MAC Control Element (CE).
  • CE MAC Control Element
  • notification of prescribed information is not limited to explicit notification, but may be made implicitly (for example, by not notifying the prescribed information or by providing other information) (by notification).
  • the determination may be made by a value expressed by 1 bit (0 or 1), or by a boolean value expressed by true or false. , may be performed by numerical comparison (for example, comparison with a predetermined value).
  • Software includes instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, whether referred to as software, firmware, middleware, microcode, hardware description language, or by any other name. , should be broadly construed to mean an application, software application, software package, routine, subroutine, object, executable, thread of execution, procedure, function, etc.
  • software, instructions, information, etc. may be sent and received via a transmission medium.
  • a transmission medium such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.
  • wired technology such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.
  • wireless technology such as infrared, microwave, etc.
  • Network may refer to devices (eg, base stations) included in the network.
  • precoding "precoding weight”
  • QCL quadsi-co-location
  • TCI state "Transmission Configuration Indication state
  • space space
  • spatial relation "spatial domain filter”
  • transmission power "phase rotation”
  • antenna port "antenna port group”
  • layer "number of layers”
  • Terms such as “rank”, “resource”, “resource set”, “resource group”, “beam”, “beam width”, “beam angle”, “antenna”, “antenna element”, and “panel” are interchangeable.
  • Base Station BS
  • Wireless base station Wireless base station
  • Fixed station NodeB
  • eNB eNodeB
  • gNB gNodeB
  • Access point "Transmission Point (TP)”, “Reception Point (RP)”, “Transmission/Reception Point (TRP)”, “Panel”
  • cell “sector,” “cell group,” “carrier,” “component carrier,” and the like
  • a base station is sometimes referred to by terms such as macrocell, small cell, femtocell, and picocell.
  • a base station can accommodate one or more (eg, three) cells. If a base station accommodates multiple cells, the overall coverage area of the base station can be partitioned into multiple smaller areas, and each smaller area is connected to a base station subsystem (e.g., an indoor small base station (Remote Radio Communication services can also be provided by the Head (RRH)).
  • a base station subsystem e.g., an indoor small base station (Remote Radio Communication services can also be provided by the Head (RRH)
  • RRH Remote Radio Communication services
  • the term “cell” or “sector” refers to part or all of the coverage area of a base station and/or base station subsystem that provides communication services in this coverage.
  • a base station transmitting information to a terminal may be interchanged with the base station instructing the terminal to control/operate based on the information.
  • MS Mobile Station
  • UE User Equipment
  • a mobile station is a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal. , handset, user agent, mobile client, client, or some other suitable terminology.
  • At least one of a base station and a mobile station may be called a transmitting device, a receiving device, a wireless communication device, etc.
  • a transmitting device may be called a transmitting device, a receiving device, a wireless communication device, etc.
  • the base station and the mobile station may be a device mounted on a moving object, the moving object itself, or the like.
  • the moving body refers to a movable object, and the moving speed is arbitrary, and naturally includes cases where the moving body is stopped.
  • the mobile objects include, for example, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, carts, rickshaws, and ships (ships and other watercraft). , including, but not limited to, airplanes, rockets, artificial satellites, drones, multicopters, quadcopters, balloons, and items mounted thereon.
  • the mobile object may be a mobile object that autonomously travels based on a travel command.
  • the moving object may be a vehicle (for example, a car, an airplane, etc.), an unmanned moving object (for example, a drone, a self-driving car, etc.), or a robot (manned or unmanned). ).
  • a vehicle for example, a car, an airplane, etc.
  • an unmanned moving object for example, a drone, a self-driving car, etc.
  • a robot manned or unmanned.
  • at least one of the base station and the mobile station includes devices that do not necessarily move during communication operations.
  • at least one of the base station and the mobile station may be an Internet of Things (IoT) device such as a sensor.
  • IoT Internet of Things
  • FIG. 13 is a diagram illustrating an example of a vehicle according to an embodiment.
  • the vehicle 40 includes a drive unit 41, a steering unit 42, an accelerator pedal 43, a brake pedal 44, a shift lever 45, left and right front wheels 46, left and right rear wheels 47, an axle 48, an electronic control unit 49, various sensors (current sensor 50, (including a rotation speed sensor 51, an air pressure sensor 52, a vehicle speed sensor 53, an acceleration sensor 54, an accelerator pedal sensor 55, a brake pedal sensor 56, a shift lever sensor 57, and an object detection sensor 58), an information service section 59, and a communication module 60. Be prepared.
  • the drive unit 41 is composed of, for example, at least one of an engine, a motor, and a hybrid of an engine and a motor.
  • the steering unit 42 includes at least a steering wheel (also referred to as a steering wheel), and is configured to steer at least one of the front wheels 46 and the rear wheels 47 based on the operation of the steering wheel operated by the user.
  • the electronic control unit 49 includes a microprocessor 61, a memory (ROM, RAM) 62, and a communication port (for example, an input/output (IO) port) 63. Signals from various sensors 50-58 provided in the vehicle are input to the electronic control unit 49.
  • the electronic control section 49 may be called an electronic control unit (ECU).
  • the signals from the various sensors 50 to 58 include a current signal from the current sensor 50 that senses the current of the motor, a rotation speed signal of the front wheel 46/rear wheel 47 obtained by the rotation speed sensor 51, and a signal obtained by the air pressure sensor 52.
  • air pressure signals of the front wheels 46/rear wheels 47 a vehicle speed signal acquired by the vehicle speed sensor 53, an acceleration signal acquired by the acceleration sensor 54, a depression amount signal of the accelerator pedal 43 acquired by the accelerator pedal sensor 55, and a brake pedal sensor.
  • 56 a shift lever 45 operation signal obtained by the shift lever sensor 57, and an object detection sensor 58 for detecting obstacles, vehicles, pedestrians, etc. There are signals etc.
  • the information service department 59 includes various devices such as car navigation systems, audio systems, speakers, displays, televisions, and radios that provide (output) various information such as driving information, traffic information, and entertainment information, and these devices. It consists of one or more ECUs that control the The information service unit 59 provides various information/services (for example, multimedia information/multimedia services) to the occupants of the vehicle 40 using information acquired from an external device via the communication module 60 or the like.
  • various information/services for example, multimedia information/multimedia services
  • the information service unit 59 may include an input device (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, a touch panel, etc.) that accepts input from the outside, and an output device that performs output to the outside (for example, display, speaker, LED lamp, touch panel, etc.).
  • an input device for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, a touch panel, etc.
  • an output device that performs output to the outside (for example, display, speaker, LED lamp, touch panel, etc.).
  • the driving support system unit 64 includes millimeter wave radar, Light Detection and Ranging (LiDAR), a camera, a positioning locator (for example, Global Navigation Satellite System (GNSS), etc.), and map information (for example, High Definition (HD)). maps, autonomous vehicle (AV) maps, etc.), gyro systems (e.g., inertial measurement units (IMUs), inertial navigation systems (INS), etc.), artificial intelligence ( Artificial Intelligence (AI) chips, AI processors, and other devices that provide functions to prevent accidents and reduce the driver's driving burden, as well as one or more devices that control these devices. It consists of an ECU. Further, the driving support system section 64 transmits and receives various information via the communication module 60, and realizes a driving support function or an automatic driving function.
  • LiDAR Light Detection and Ranging
  • GNSS Global Navigation Satellite System
  • HD High Definition
  • maps for example, autonomous vehicle (AV) maps, etc.
  • gyro systems e.g.,
  • the communication module 60 can communicate with the microprocessor 61 and components of the vehicle 40 via the communication port 63.
  • the communication module 60 communicates via the communication port 63 with a drive unit 41, a steering unit 42, an accelerator pedal 43, a brake pedal 44, a shift lever 45, left and right front wheels 46, left and right rear wheels 47, which are included in the vehicle 40.
  • Data (information) is transmitted and received between the axle 48, the microprocessor 61 and memory (ROM, RAM) 62 in the electronic control unit 49, and various sensors 50-58.
  • the communication module 60 is a communication device that can be controlled by the microprocessor 61 of the electronic control unit 49 and can communicate with external devices. For example, various information is transmitted and received with an external device via wireless communication.
  • the communication module 60 may be located either inside or outside the electronic control unit 49.
  • the external device may be, for example, the base station 10, user terminal 20, etc. described above.
  • the communication module 60 may be, for example, at least one of the base station 10 and the user terminal 20 described above (it may function as at least one of the base station 10 and the user terminal 20).
  • the communication module 60 receives signals from the various sensors 50 to 58 described above that are input to the electronic control unit 49, information obtained based on the signals, and input from the outside (user) obtained via the information service unit 59. At least one of the information based on the information may be transmitted to an external device via wireless communication.
  • the electronic control unit 49, various sensors 50-58, information service unit 59, etc. may be called an input unit that receives input.
  • the PUSCH transmitted by the communication module 60 may include information based on the above input.
  • the communication module 60 receives various information (traffic information, signal information, inter-vehicle information, etc.) transmitted from an external device, and displays it on the information service section 59 provided in the vehicle.
  • the information service unit 59 is an output unit that outputs information (for example, outputs information to devices such as a display and a speaker based on the PDSCH (or data/information decoded from the PDSCH) received by the communication module 60). may be called.
  • the communication module 60 also stores various information received from external devices into a memory 62 that can be used by the microprocessor 61. Based on the information stored in the memory 62, the microprocessor 61 controls the drive section 41, steering section 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, and left and right rear wheels provided in the vehicle 40. 47, axle 48, various sensors 50-58, etc. may be controlled.
  • the base station in the present disclosure may be replaced by a user terminal.
  • communication between a base station and a user terminal is replaced with communication between multiple user terminals (for example, it may be called Device-to-Device (D2D), Vehicle-to-Everything (V2X), etc.).
  • D2D Device-to-Device
  • V2X Vehicle-to-Everything
  • each aspect/embodiment of the present disclosure may be applied.
  • the user terminal 20 may have the functions that the base station 10 described above has.
  • words such as "uplink” and “downlink” may be replaced with words corresponding to inter-terminal communication (for example, "sidelink”).
  • uplink channels, downlink channels, etc. may be replaced with sidelink channels.
  • the user terminal in the present disclosure may be replaced with a base station.
  • the base station 10 may have the functions that the user terminal 20 described above has.
  • the operations performed by the base station may be performed by its upper node in some cases.
  • various operations performed for communication with a terminal may be performed by the base station, one or more network nodes other than the base station (e.g. It is clear that this can be performed by a Mobility Management Entity (MME), a Serving-Gateway (S-GW), etc. (though not limited thereto), or a combination thereof.
  • MME Mobility Management Entity
  • S-GW Serving-Gateway
  • Each aspect/embodiment described in this disclosure may be used alone, in combination, or may be switched and used in accordance with execution. Furthermore, the order of the processing procedures, sequences, flowcharts, etc. of each aspect/embodiment described in this disclosure may be changed as long as there is no contradiction. For example, the methods described in this disclosure use an example order to present elements of the various steps and are not limited to the particular order presented.
  • LTE Long Term Evolution
  • LTE-A LTE-Advanced
  • LTE-B LTE-Beyond
  • SUPER 3G IMT-Advanced
  • 4G 4th generation mobile communication system
  • 5G 5th generation mobile communication system
  • 6G 6th generation mobile communication system
  • xG x is an integer or decimal number, for example
  • Future Radio Access FAA
  • RAT New-Radio Access Technology
  • NR New Radio
  • NX New radio access
  • FX Future generation radio access
  • GSM registered trademark
  • CDMA2000 Code Division Multiple Access
  • UMB Ultra Mobile Broadband
  • IEEE 802 .11 Wi-Fi (registered trademark)
  • IEEE 802.16 WiMAX (registered trademark)
  • IEEE 802.20 Ultra-WideBand (UWB), Bluetooth (registered trademark), and other appropriate wireless communication methods.
  • the present invention may be applied to systems to be used, next-generation systems expanded, modified,
  • the phrase “based on” does not mean “based solely on” unless explicitly stated otherwise. In other words, the phrase “based on” means both “based only on” and “based at least on.”
  • any reference to elements using the designations "first,” “second,” etc. does not generally limit the amount or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, reference to a first and second element does not imply that only two elements may be employed or that the first element must precede the second element in any way.
  • determining may encompass a wide variety of actions. For example, “judgment” can mean judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry ( For example, searching in a table, database, or other data structure), ascertaining, etc. may be considered to be “determining.”
  • judgment (decision) includes receiving (e.g., receiving information), transmitting (e.g., sending information), input (input), output (output), access ( may be considered to be “determining”, such as accessing data in memory (eg, accessing data in memory).
  • judgment is considered to mean “judging” resolving, selecting, choosing, establishing, comparing, etc. Good too.
  • judgment (decision) may be considered to be “judgment (decision)” of some action.
  • the "maximum transmit power" described in this disclosure may mean the maximum value of transmit power, the nominal maximum transmit power (the nominal UE maximum transmit power), or the rated maximum transmit power (the It may also mean rated UE maximum transmit power).
  • connection refers to any connection or coupling, direct or indirect, between two or more elements.
  • the coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connection” may be replaced with "access.”
  • microwave when two elements are connected, they may be connected using one or more electrical wires, cables, printed electrical connections, etc., as well as in the radio frequency domain, microwave can be considered to be “connected” or “coupled” to each other using electromagnetic energy having wavelengths in the light (both visible and invisible) range.
  • a and B are different may mean “A and B are different from each other.” Note that the term may also mean that "A and B are each different from C”. Terms such as “separate” and “coupled” may also be interpreted similarly to “different.”
  • words meaning "good”, “bad”, “large”, “small”, “high”, “low”, “early”, “slow”, etc. may be read interchangeably. (Not limited to original, comparative, and superlative).
  • words meaning "good”, “bad”, “large”, “small”, “high”, “low”, “early”, “slow”, etc. are replaced with “i-th”. They may be interchanged as expressions (not limited to the original, comparative, and superlative) (for example, “the highest” may be interchanged with “the i-th highest”).

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Abstract

A terminal according to one embodiment of the present disclosure comprises: a receiving unit that receives configuration information for reporting intended for performance monitoring with regard to an Artificial Intelligence (AI)-based Beam Management (BM); and a control unit that controls transmission of reports related to Layer 1 Reference Signal Received Power (L1-RSRP) measured on the basis of the configuration information. The embodiment of the present disclosure makes it possible to achieve suitable overhead reduction, channel estimation, and resource utilization.

Description

端末、無線通信方法及び基地局Terminal, wireless communication method and base station
　本開示は、次世代移動通信システムにおける端末、無線通信方法及び基地局に関する。 The present disclosure relates to a terminal, a wireless communication method, and a base station in a next-generation mobile communication system.
　Universal　Mobile　Telecommunications　System（ＵＭＴＳ）ネットワークにおいて、更なる高速データレート、低遅延などを目的としてLong　Term　Evolution（ＬＴＥ）が仕様化された（非特許文献１）。また、ＬＴＥ（Third　Generation　Partnership　Project（３ＧＰＰ（登録商標））　Release（Ｒｅｌ．）８、９）の更なる大容量、高度化などを目的として、ＬＴＥ－Ａｄｖａｎｃｅｄ（３ＧＰＰ　Ｒｅｌ．１０－１４）が仕様化された。 In the Universal Mobile Telecommunications System (UMTS) network, Long Term Evolution (LTE) has been specified for the purpose of higher data rates, lower delays, etc. (Non-Patent Document 1). In addition, LTE-Advanced (3GPP Rel. 10-14) is a specification for the purpose of further increasing capacity and sophistication of LTE (Third Generation Partnership Project (3GPP (registered trademark)) Release (Rel. 8, 9). was made into
　ＬＴＥの後継システム（例えば、5th　generation　mobile　communication　system（５Ｇ）、５Ｇ＋（plus）、6th　generation　mobile　communication　system（６Ｇ）、New　Radio（ＮＲ）、３ＧＰＰ　Ｒｅｌ．１５以降などともいう）も検討されている。 Successor systems to LTE (for example, also referred to as 5th generation mobile communication system (5G), 5G+ (plus), 6th generation mobile communication system (6G), New Radio (NR), 3GPP Rel. 15 or later) are also being considered. .
　将来の無線通信技術について、ネットワーク／デバイスの制御、管理などに、機械学習（Machine　Learning（ＭＬ））のような人工知能（Artificial　Intelligence（ＡＩ））技術を活用することが検討されている。 Regarding future wireless communication technology, the use of artificial intelligence (AI) technology such as machine learning (ML) is being considered for network/device control and management.
　ＡＩモデルの活用のユースケースとして、ＵＥ又はＮＷにおける片側ＡＩモデルを用いる空間ドメイン下りリンク（Downlink（ＤＬ））ビーム予測又は時間的ＤＬビーム予測が検討されている。このようなビーム予測方法は、ＡＩベースドビーム予測（ビーム報告）、ＡＩベースドビーム管理（Beam　Management（ＢＭ））などと呼ばれてもよい。 As a use case for utilizing AI models, spatial domain downlink (DL) beam prediction or temporal DL beam prediction using a one-sided AI model in the UE or NW is being considered. Such a beam prediction method may be called AI-based beam prediction (beam reporting), AI-based beam management (BM), or the like.
　ところで、ＡＩモデルの性能モニタリングが検討されている。ＡＩモデルの性能モニタリングは、端末（terminal、ユーザ端末（user　terminal）、User　Equipment（ＵＥ））において行われてもよいし、基地局（Base　Station（ＢＳ））において行われてもよい。しかしながら、ＡＩベースドビーム報告に関して、ＵＥ／ＢＳにおける性能モニタリングの具体的なライフサイクル管理についてはまだ検討が進んでいない。 By the way, performance monitoring of AI models is being considered. Performance monitoring of the AI model may be performed at a terminal (terminal, user terminal, user equipment (UE)), or at a base station (BS). However, regarding AI-based beam reporting, no progress has been made in considering specific lifecycle management of performance monitoring in the UE/BS.
　性能モニタリングの実現方法を適切に規定しなければ、適切なオーバーヘッド低減／高精度なチャネル推定／高効率なリソースの利用が達成できず、通信スループット／通信品質の向上が抑制されるおそれがある。 If the method for implementing performance monitoring is not appropriately defined, appropriate overhead reduction, highly accurate channel estimation, and highly efficient resource utilization may not be achieved, and improvements in communication throughput/communication quality may be suppressed.
　そこで、本開示は、好適なオーバーヘッド低減／チャネル推定／リソースの利用を実現できる端末、無線通信方法及び基地局を提供することを目的の１つとする。 Therefore, one of the purposes of the present disclosure is to provide a terminal, a wireless communication method, and a base station that can realize suitable overhead reduction/channel estimation/resource utilization.
　本開示の一態様に係る端末は、人工知能（Artificial　Intelligence（ＡＩ））ベースドビーム管理（Beam　Management（ＢＭ））に関して、性能モニタリング向けの報告のための設定情報を受信する受信部と、前記設定情報に基づいて測定されるLayer　1　Reference　Signal　Received　Power（Ｌ１－ＲＳＲＰ）に関する報告の送信を制御する制御部と、を有する。 A terminal according to an aspect of the present disclosure includes a receiving unit that receives setting information for reporting for performance monitoring regarding artificial intelligence (AI) based beam management (BM), and a receiving unit that receives setting information for reporting for performance monitoring; and a control unit that controls transmission of a report regarding Layer 1 Reference Signal Received Power (L1-RSRP) measured based on the information.
　本開示の一態様によれば、好適なオーバーヘッド低減／チャネル推定／リソースの利用を実現できる。 According to one aspect of the present disclosure, suitable overhead reduction/channel estimation/resource utilization can be achieved.
図１は、ＡＩモデルの管理のフレームワークの一例を示す図である。FIG. 1 is a diagram illustrating an example of an AI model management framework. 図２Ａ及び２Ｂは、ＡＩベースドビーム報告の一例を示す図である。2A and 2B are diagrams illustrating an example of an AI-based beam report. 図３は、一実施形態に係るＵＥにおける性能モニタリングのライフサイクル管理フレームワークの一例を示す図である。FIG. 3 is a diagram illustrating an example of a lifecycle management framework for performance monitoring in a UE according to an embodiment. 図４は、一実施形態に係るＢＳにおける性能モニタリングのライフサイクル管理フレームワークの一例を示す図である。FIG. 4 is a diagram illustrating an example of a lifecycle management framework for performance monitoring in a BS according to an embodiment. 図５は、方法２－１又は２－２に基づいて決定される性能モニタリングのためのＣＳＩ－ＲＳ機会の一例を示す図である。FIG. 5 is a diagram illustrating an example of CSI-RS opportunities for performance monitoring determined based on method 2-1 or 2-2. 図６は、方法２－３又は２－４に基づいて決定される性能モニタリングのためのＣＳＩ－ＲＳ機会の一例を示す図である。FIG. 6 is a diagram illustrating an example of CSI-RS opportunities for performance monitoring determined based on method 2-3 or 2-4. 図７は、第２の実施形態におけるモデル評価の一例を示す図である。FIG. 7 is a diagram showing an example of model evaluation in the second embodiment. 図８は、第６の実施形態にかかるＣＳＩレポートの一例を示す図である。FIG. 8 is a diagram illustrating an example of a CSI report according to the sixth embodiment. 図９は、一実施形態に係る無線通信システムの概略構成の一例を示す図である。FIG. 9 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment. 図１０は、一実施形態に係る基地局の構成の一例を示す図である。FIG. 10 is a diagram illustrating an example of the configuration of a base station according to an embodiment. 図１１は、一実施形態に係るユーザ端末の構成の一例を示す図である。FIG. 11 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment. 図１２は、一実施形態に係る基地局及びユーザ端末のハードウェア構成の一例を示す図である。FIG. 12 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment. 図１３は、一実施形態に係る車両の一例を示す図である。FIG. 13 is a diagram illustrating an example of a vehicle according to an embodiment.
（無線通信への人工知能（Artificial　Intelligence（ＡＩ））技術の適用）
　将来の無線通信技術について、ネットワーク／デバイスの制御、管理などに、機械学習（Machine　Learning（ＭＬ））のようなＡＩ技術を活用することが検討されている。 (Application of Artificial Intelligence (AI) technology to wireless communications)
Regarding future wireless communication technology, the use of AI technology such as machine learning (ML) is being considered for network/device control and management.
　例えば、チャネル状態情報（Channel　State　Information　Reference　Signal（ＣＳＩ））フィードバックの向上（例えば、オーバーヘッド低減、正確度改善、予測）、ビームマネジメントの改善（例えば、正確度改善、時間／空間領域での予測）、位置測定の改善（例えば、位置推定／予測の改善）などのために、端末（terminal、ユーザ端末（user　terminal）、User　Equipment（ＵＥ））／基地局（Base　Station（ＢＳ））がＡＩ技術を活用することが検討されている。 For example, improved Channel State Information Reference Signal (CSI) feedback (e.g., reduced overhead, improved accuracy, prediction), improved beam management (e.g., improved accuracy, prediction in the time/spatial domain) , to improve positioning (e.g., improve position estimation/prediction), terminals (user terminals, user equipment (UE))/Base Stations (BS) use AI technology. The use of
　ＡＩモデルは、入力される情報に基づいて、推定値、予測値、選択される動作、分類、などの少なくとも１つの情報を出力してもよい。ＵＥ／ＢＳは、ＡＩモデルに対して、チャネル状態情報、参照信号測定値などを入力して、高精度なチャネル状態情報／測定値／ビーム選択／位置、将来のチャネル状態情報／無線リンク品質などを出力してもよい。 Based on the input information, the AI model may output at least one information such as an estimated value, a predicted value, a selected action, a classification, etc. The UE/BS inputs channel state information, reference signal measurements, etc. to the AI model, and provides highly accurate channel state information/measurements/beam selection/position, future channel state information/radio link quality, etc. may be output.
　なお、本開示において、ＡＩは、以下の少なくとも１つの特徴を有する（実施する）オブジェクト（対象、客体、データ、関数、プログラムなどとも呼ばれる）で読み替えられてもよい：
・観測又は収集される情報に基づく推定、
・観測又は収集される情報に基づく選択、
・観測又は収集される情報に基づく予測。 Note that in this disclosure, AI may be read as an object (also referred to as a target, object, data, function, program, etc.) that has (implements) at least one of the following characteristics:
・Estimation based on observed or collected information;
- Selection based on observed or collected information;
- Predictions based on observed or collected information.
　本開示において、推定（estimation）、予測（prediction）、推論（inference）は、互いに読み替えられてもよい。また、本開示において、推定する（estimate）、予測する（predict）、推論する（infer）は、互いに読み替えられてもよい。 In the present disclosure, estimation, prediction, and inference may be used interchangeably. Furthermore, in the present disclosure, the terms "estimate," "predict," and "infer" may be used interchangeably.
　本開示において、オブジェクトは、例えば、ＵＥ、ＢＳなどの装置、デバイスなどであってもよい。また、本開示において、オブジェクトは、当該装置において動作するプログラム／モデル／エンティティに該当してもよい。 In the present disclosure, an object may be, for example, an apparatus, a device, etc., such as a UE or a BS. Furthermore, in the present disclosure, an object may correspond to a program/model/entity that operates on the device.
　また、本開示において、ＡＩモデルは、以下の少なくとも１つの特徴を有する（実施する）オブジェクトで読み替えられてもよい：
・情報を与えること（feeding）によって、推定値を生み出す、
・情報を与えることによって、推定値を予測する、
・情報を与えることによって、特徴を発見する、
・情報を与えることによって、動作を選択する。 Furthermore, in this disclosure, the AI model may be replaced by an object that has (implements) at least one of the following characteristics:
・Produce estimates by feeding information,
・Predict the estimated value by giving information,
・Discover characteristics by providing information,
・Select an action by providing information.
　また、本開示において、ＡＩモデルは、ＡＩ技術を適用し、入力のセットに基づいて出力のセットを生成するデータドリブンアルゴリズムを意味してもよい。 Additionally, in this disclosure, an AI model may refer to a data-driven algorithm that applies AI technology and generates a set of outputs based on a set of inputs.
　また、本開示において、ＡＩモデル、モデル、ＭＬモデル、予測分析（predictive　analytics）、予測分析モデル、ツール、自己符号化器（オートエンコーダ（autoencoder））、エンコーダ、デコーダ、ニューラルネットワークモデル、ＡＩアルゴリズム、スキームなどは、互いに読み替えられてもよい。また、ＡＩモデルは、回帰分析（例えば、線形回帰分析、重回帰分析、ロジスティック回帰分析）、サポートベクターマシン、ランダムフォレスト、ニューラルネットワーク、ディープラーニングなどの少なくとも１つを用いて導出されてもよい。 In addition, in this disclosure, AI models, models, ML models, predictive analytics, predictive analysis models, tools, autoencoders (autoencoders), encoders, decoders, neural network models, AI algorithms, Schemes etc. may be read interchangeably. Further, the AI model may be derived using at least one of regression analysis (eg, linear regression analysis, multiple regression analysis, logistic regression analysis), support vector machine, random forest, neural network, deep learning, etc.
　本開示において、オートエンコーダは、積層オートエンコーダ、畳み込みオートエンコーダなど任意のオートエンコーダと互いに読み替えられてもよい。本開示のエンコーダ／デコーダは、Residual　Network（ResNet）、DenseNet、RefineNetなどのモデルを採用してもよい。 In the present disclosure, the autoencoder may be interchanged with any autoencoder such as a stacked autoencoder or a convolutional autoencoder. The encoder/decoder of the present disclosure may adopt models such as Residual Network (ResNet), DenseNet, RefineNet, etc.
　また、本開示において、エンコーダ、エンコーディング（encoding）、エンコードする／される（encode/encoded）、エンコーダによる修正／変更／制御、圧縮（compressing）、圧縮する／される（compress/compressed）、生成（generating）、生成する／される（generate/generated）などは、互いに読み替えられてもよい。 In addition, in this disclosure, an encoder, encoding, encode/encoded, modification/change/control by an encoder, compressing, compress/compressed, generation ( "generate", "generate/generated", etc. may be used interchangeably.
　また、本開示において、デコーダ、デコーディング（decoding）、デコードする／される（decode/decoded）、デコーダによる修正／変更／制御、展開（decompressing）、展開する／される（decompress/decompressed）、再構成（reconstructing）、再構成する／される（reconstruct/reconstructed）などは、互いに読み替えられてもよい。 In addition, in this disclosure, a decoder, decoding, decode/decoded, modification/change/control by a decoder, decompressing, decompress/decompressed, re- Reconstructing, reconstruct/reconstructed, etc. may be used interchangeably.
　本開示において、（ＡＩモデルについての）レイヤは、ＡＩモデルにおいて利用されるレイヤ（入力層、中間層など）と互いに読み替えられてもよい。本開示のレイヤ（層）は、入力層、中間層、出力層、バッチ正規化層、畳み込み層、活性化層、デンス（dense）層、正規化層、プーリング層、アテンション層、ドロップアウト層、全結合層などの少なくとも１つに該当してもよい。 In this disclosure, layers (with respect to the AI model) may be interchanged with layers (input layer, intermediate layer, etc.) used in the AI model. The layers of the present disclosure include an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolution layer, an activation layer, a dense layer, a normalization layer, a pooling layer, an attention layer, a dropout layer, It may correspond to at least one of the fully connected layers.
　本開示において、ＡＩモデルの訓練方法には、教師あり学習（supervised　learning）、教師なし学習（unsupervised　learning）、強化学習（Reinforcement　learning）、連合学習（federated　learning）などが含まれてもよい。教師あり学習は、入力及び対応するラベルからモデルを訓練する処理を意味してもよい。教師なし学習は、ラベル付きデータなしでモデルを訓練する処理を意味してもよい。強化学習は、モデルが相互作用している環境において、入力（言い換えると、状態）と、モデルの出力（言い換えると、アクション）から生じるフィードバック信号（言い換えると、報酬）と、からモデルを訓練する処理を意味してもよい。 In the present disclosure, AI model training methods may include supervised learning, unsupervised learning, reinforcement learning, federated learning, and the like. Supervised learning may refer to the process of training a model from input and corresponding labels. Unsupervised learning may refer to the process of training a model without labeled data. Reinforcement learning is the process of training a model from inputs (in other words, states) and feedback signals (in other words, rewards) resulting from the model's outputs (in other words, actions) in the environment in which the models are interacting. It can also mean
　本開示において、生成、算出、導出などは、互いに読み替えられてもよい。本開示において、実施、運用、動作、実行などは、互いに読み替えられてもよい。本開示において、訓練、学習、更新、再訓練などは、互いに読み替えられてもよい。本開示において、推論、訓練後（after-training）、本番の利用、実際の利用、などは互いに読み替えられてもよい。本開示において、信号は、信号／チャネルと互いに読み替えられてもよい。 In this disclosure, generation, calculation, derivation, etc. may be read interchangeably. In this disclosure, implementation, operation, operation, execution, etc. may be read interchangeably. In this disclosure, training, learning, updating, retraining, etc. may be used interchangeably. In this disclosure, inference, after-training, production use, actual use, etc. may be read interchangeably. In this disclosure, a signal may be interchanged with a signal/channel.
　図１は、ＡＩモデルの管理のフレームワークの一例を示す図である。本例では、ＡＩモデルに関連する各ステージがブロックで示されている。本例は、ＡＩモデルのライフサイクル管理とも表現される。 FIG. 1 is a diagram illustrating an example of an AI model management framework. In this example, each stage related to the AI model is shown as a block. This example is also expressed as AI model life cycle management.
　データ収集ステージは、ＡＩモデルの生成／更新のためのデータを収集する段階に該当する。データ収集ステージは、データ整理（例えば、どのデータをモデル訓練／モデル推論のために転送するかの決定）、データ転送（例えば、モデル訓練／モデル推論を行うエンティティ（例えば、ＵＥ、ｇＮＢ）に対して、データを転送）などを含んでもよい。 The data collection stage corresponds to the stage of collecting data for generating/updating an AI model. The data collection stage includes data reduction (e.g., deciding which data to transfer for model training/model inference), data transfer (e.g., to entities performing model training/model inference (e.g., UE, gNB)), and transfer data).
　なお、データ収集は、ＡＩモデル訓練／データ分析／推論を目的として、ネットワークノード、管理エンティティ又はＵＥによってデータが収集される処理を意味してもよい。本開示において、処理、手順は互いに読み替えられてもよい。 Note that data collection may refer to a process in which data is collected by a network node, management entity, or UE for the purpose of AI model training/data analysis/inference. In the present disclosure, the terms "process" and "procedure" may be interchanged with each other.
　モデル訓練ステージでは、収集ステージから転送されるデータ（訓練用データ）に基づいてモデル訓練が行われる。このステージは、データ準備（例えば、データの前処理、クリーニング、フォーマット化、変換などの実施）、モデル訓練／バリデーション（検証）、モデルテスティング（例えば、訓練されたモデルが性能の閾値を満たすかの確認）、モデル交換（例えば、分散学習のためのモデルの転送）、モデルデプロイメント／更新（モデル推論を行うエンティティに対してモデルをデプロイ／更新）などを含んでもよい。 In the model training stage, model training is performed based on the data (training data) transferred from the collection stage. This stage includes data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model training/validation, and model testing (e.g., whether the trained model meets performance thresholds). verification), model exchange (e.g., transferring a model for distributed learning), model deployment/updating (deploying/updating a model to an entity that performs model inference), etc.
　なお、ＡＩモデル訓練（AI　model　training）は、データドリブンな方法でＡＩモデルを訓練し、推論のための訓練されたＡＩモデルを取得するための処理を意味してもよい。 Note that AI model training may refer to processing for training an AI model in a data-driven manner and obtaining a trained AI model for inference.
　また、ＡＩモデルバリデーション（AI　model　validation）は、モデル訓練に使用したデータセットとは異なるデータセットを用いてＡＩモデルの品質を評価するための訓練のサブ処理を意味してもよい。当該サブ処理は、モデル訓練に使用したデータセットを超えて汎化するモデルパラメータの選択に役立つ。 Furthermore, AI model validation may refer to a training sub-process for evaluating the quality of an AI model using a data set different from the data set used for model training. This sub-processing helps select model parameters that generalize beyond the dataset used to train the model.
　また、ＡＩモデルテスティング（AI　model　testing）は、モデル訓練／バリデーションに使用したデータセットとは異なるデータセットを使用して、最終的なＡＩモデルの性能を評価するための訓練のサブ処理を意味してもよい。なお、テスティングは、バリデーションとは異なり、その後のモデルチューニングを前提としなくてもよい。 Also, AI model testing refers to a sub-process of training to evaluate the performance of the final AI model using a dataset different from the dataset used for model training/validation. You may. Note that unlike validation, testing does not have to be based on subsequent model tuning.
　モデル推論ステージでは、収集ステージから転送されるデータ（推論用データ）に基づいてモデル推論が行われる。このステージは、データ準備（例えば、データの前処理、クリーニング、フォーマット化、変換などの実施）、モデル推論、モデルモニタリング（例えば、モデル推論の性能をモニタ）、モデル性能フィードバック（モデル訓練を行うエンティティに対してモデル性能をフィードバック）、出力（アクターに対してモデルの出力を提供）などを含んでもよい。 At the model inference stage, model inference is performed based on the data (inference data) transferred from the collection stage. This stage includes data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model inference, model monitoring (e.g., monitoring the performance of model inference), and model performance feedback (the entity performing model training). (feedback of model performance to actors), output (provide model output to actors), etc.
　なお、ＡＩモデル推論（AI　model　inference）は、訓練されたＡＩモデルを用いて入力のセットから出力のセットを産み出すための処理を意味してもよい。 Note that AI model inference may refer to processing for producing a set of outputs from a set of inputs using a trained AI model.
　また、ＵＥ側（UE　side）モデルは、その推論が完全にＵＥにおいて実施されるＡＩモデルを意味してもよい。ネットワーク側（Network　side）モデルは、その推論が完全にネットワーク（例えば、ｇＮＢ）において実施されるＡＩモデルを意味してもよい。 Additionally, a UE side model may refer to an AI model whose inference is completely performed in the UE. A network side model may refer to an AI model whose inference is performed entirely in the network (eg, gNB).
　また、片側（one-sided）モデルは、ＵＥ側モデル又はネットワーク側モデルを意味してもよい。両側（two-sided）モデルは、共同推論（joint　inference）が行われるペアのＡＩモデルを意味してもよい。ここで、共同推論は、その推論がＵＥとネットワークにわたって共同で行われるＡＩ推論を含んでもよく、例えば、推論の第１の部分がＵＥによって最初に行われ、残りの部分がｇＮＢによって行われてもよい（又はその逆が行われてもよい）。 Also, the one-sided model may mean a UE-side model or a network-side model. A two-sided model may refer to a pair of AI models in which joint inference is performed. Here, joint inference may include AI inference where the inference is performed jointly across the UE and the network, e.g., the first part of the inference is performed by the UE first and the remaining part is performed by the gNB. (or vice versa).
　また、ＡＩモデルモニタリング（AI　model　monitoring）は、ＡＩモデルの推論性能をモニタするための処理を意味してもよく、モデル性能モニタリング、性能モニタリングなどと互いに読み替えられてもよい。 Furthermore, AI model monitoring may mean processing for monitoring the inference performance of an AI model, and may be interchanged with model performance monitoring, performance monitoring, etc.
　なお、モデルレジストレーション（model　registration）は、モデルにバージョン識別子を付与し、推論段階において利用される特定のハードウェアにコンパイルすることを介して当該モデルを実行可能にすることを意味してもよい。また、モデルデプロイメント（model　deployment）は、完全に開発されテストされたモデルのランタイムイメージ（又は実行環境のイメージ）を、推論が実施されるターゲット（例えば、ＵＥ／ｇＮＢ）に配信する（又は当該ターゲットにおいて有効化する）ことを意味してもよい。 Note that model registration may mean making the model executable by assigning a version identifier to the model and compiling it on specific hardware used in the inference stage. . Model deployment also refers to delivering (or distributing) a fully developed and tested model runtime image (or image of an execution environment) to a target (e.g., UE/gNB) on which inference is performed. It may also mean ``enabled''.
　アクターステージは、アクショントリガ（例えば、他のエンティティに対してアクションをトリガするか否かの決定）、フィードバック（例えば、訓練用データ／推論用データ／性能フィードバックのために必要な情報をフィードバック）などを含んでもよい。 The actor stage includes action triggers (e.g., deciding whether to trigger an action on other entities), feedback (e.g., feeding back information necessary for training data/inference data/performance feedback), etc. May include.
　なお、例えばモビリティ最適化のためのモデルの訓練は、例えば、ネットワーク（Network（ＮＷ））における保守運用管理（Operation、Administration　and　Maintenance（Management）（ＯＡＭ））／gNodeB（ｇＮＢ）において行われてもよい。前者の場合、相互運用、大容量ストレージ、オペレータの管理性、モデルの柔軟性（フィーチャーエンジニアリングなど）が有利である。後者の場合、モデル更新のレイテンシ、モデル展開のためのデータ交換などが不要な点が有利である。上記モデルの推論は、例えば、ｇＮＢにおいて行われてもよい。 Note that, for example, training of a model for mobility optimization may be performed, for example, in Operation, Administration and Maintenance (Management) (OAM) in a network (Network (NW)) / gNodeB (gNB). good. The former has advantages in interoperability, large storage capacity, operator manageability, and model flexibility (e.g., feature engineering). In the latter case, the advantage is that there is no need for model update latency or data exchange for model development. Inference of the above model may be performed in the gNB, for example.
　ユースケース（言い換えると、ＡＩモデルの機能）に応じて、訓練／推論を行うエンティティは異なってもよい。ＡＩモデルの機能（function）は、ビーム管理、ビーム予測、オートエンコーダ（又は情報圧縮）、ＣＳＩフィードバック、位置測位などを含んでもよい。 Depending on the use case (in other words, the functionality of the AI model), the entity that performs the training/inference may be different. Functions of the AI model may include beam management, beam prediction, autoencoder (or information compression), CSI feedback, position positioning, etc.
　例えば、メジャメントレポートに基づくＡＩ支援ビーム管理については、ＯＡＭ／ｇＮＢがモデル訓練を行い、ｇＮＢがモデル推論を行ってもよい。 For example, for AI-assisted beam management based on measurement reports, the OAM/gNB may perform model training and the gNB may perform model inference.
　ＡＩ支援ＵＥアシステッドポジショニングについては、Location　Management　Function（ＬＭＦ）がモデル訓練を行い、当該ＬＭＦがモデル推論を行ってもよい。 For AI-assisted UE assisted positioning, a Location Management Function (LMF) may perform model training, and the LMF may perform model inference.
　オートエンコーダを用いるＣＳＩフィードバック／チャネル推定については、ＯＡＭ／ｇＮＢ／ＵＥがモデル訓練を行い、ｇＮＢ／ＵＥが（ジョイントで）モデル推論を行ってもよい。 For CSI feedback/channel estimation using an autoencoder, the OAM/gNB/UE may perform model training and the gNB/UE (jointly) may perform model inference.
　ビーム測定に基づくＡＩ支援ビーム管理又はＡＩ支援ＵＥベースドポジショニングについては、ＯＡＭ／ｇＮＢ／ＵＥがモデル訓練を行い、ＵＥがモデル推論を行ってもよい。 For AI-assisted beam management based on beam measurements or AI-assisted UE-based positioning, the OAM/gNB/UE may perform model training and the UE may perform model inference.
　なお、モデルアクティベーションは、特定の機能のためのＡＩモデルを有効化することを意味してもよい。モデルディアクティベーションは、特定の機能のためのＡＩモデルを無効化することを意味してもよい。モデルスイッチングは、特定の機能のための現在アクティブなＡＩモデルをディアクティベートし、異なるＡＩモデルをアクティベートうることを意味してもよい。 Note that model activation may mean activating an AI model for a specific function. Model deactivation may mean disabling an AI model for a particular function. Model switching may mean deactivating the currently active AI model for a particular function and activating a different AI model.
　また、モデル転送（model　transfer）は、エアインターフェース上でＡＩモデルを配信することを意味してもよい。この配信は、受信側において既知のモデル構造のパラメータ、又はパラメータを有する新しいモデルの一方又は両方を配信することを含んでもよい。また、この配信は、完全なモデル又は部分的なモデルを含んでもよい。モデルダウンロードは、ネットワークからＵＥへのモデル転送を意味してもよい。モデルアップロードは、ＵＥからネットワークへのモデル転送を意味してもよい。 Additionally, model transfer may mean distributing the AI model over the air interface. This distribution may include distributing one or both of the parameters of the model structure known at the receiving end, or a new model with the parameters. This distribution may also include complete models or partial models. Model download may refer to model transfer from the network to the UE. Model upload may refer to model transfer from the UE to the network.
（ＡＩベースドビーム報告）
　ＡＩモデルの活用のユースケースとして、ＵＥ又はＮＷにおける片側ＡＩモデルを用いる空間ドメイン下りリンク（Downlink（ＤＬ））ビーム予測又は時間的ＤＬビーム予測が検討されている。このようなビーム予測方法は、ＡＩベースドビーム予測（ビーム報告）、ＡＩベースドビーム管理（Beam　Management（ＢＭ））などと呼ばれてもよい。 (AI based beam report)
As a use case for utilizing AI models, spatial domain downlink (DL) beam prediction or temporal DL beam prediction using a one-sided AI model in the UE or NW is being considered. Such a beam prediction method may be called AI-based beam prediction (beam reporting), AI-based beam management (BM), or the like.
　図２Ａ及び２Ｂは、ＡＩベースドビーム報告の一例を示す図である。図２Ａは、空間ドメインＤＬビーム予測を示す。ＵＥは、空間的に疎な（又は太い）ビームを測定して、測定結果などをＡＩモデルに入力し、空間的に密な（又は細い）ビームのビーム品質の予測結果を出力してもよい。 FIGS. 2A and 2B are diagrams showing an example of an AI-based beam report. FIG. 2A shows spatial domain DL beam prediction. The UE may measure a spatially sparse (or thick) beam, input the measurement results etc. to an AI model, and output a prediction result of the beam quality of a spatially dense (or thin) beam. .
　図２Ｂは、時間的ＤＬビーム予測を示す。ＵＥは、時系列のビームを測定して、測定結果などをＡＩモデルに入力し、将来のビームのビーム品質の予測結果を出力してもよい。 Figure 2B shows temporal DL beam prediction. The UE may measure a time-series beam, input the measurement results and the like into an AI model, and output a prediction result of the beam quality of a future beam.
　なお、空間ドメインＤＬビーム予測は、ＢＭケース１と呼ばれてもよいし、時間的ＤＬビーム予測は、ＢＭケース２と呼ばれてもよい。 Note that spatial domain DL beam prediction may be referred to as BM case 1, and temporal DL beam prediction may be referred to as BM case 2.
　また、ＡＩモデルの出力（予測結果）に関連するビームは、ビームのセットＡと呼ばれてもよい。ＡＩモデルの入力に関連するビームは、ビームのセットＢと呼ばれてもよい。 Also, the beams related to the output (prediction results) of the AI model may be referred to as a set A of beams. The beams associated with the input of the AI model may be referred to as set B of beams.
　ＢＭケース１／２のＡＩモデルの入力の候補は、Ｌ１－ＲＳＲＰ（レイヤ１における参照信号受信電力（Layer　1　Reference　Signal　Received　Power））、アシスタンス情報（例えば、ビーム形状情報、ＵＥ位置／方向情報、送信ビーム用途情報）、チャネルインパルス応答（Channel　Impulse　Response（ＣＩＲ））の情報、対応するＤＬ送信／受信ビームＩＤなどが挙げられる。 The input candidates for the AI model for BM case 1/2 are L1-RSRP (Layer 1 Reference Signal Received Power), assistance information (for example, beam shape information, UE position/direction information, Examples include transmit beam usage information), channel impulse response (CIR) information, and corresponding DL transmit/receive beam ID.
　ＢＭケース１のＡＩモデルの出力の候補は、上位Ｋ個（Ｋは整数）の送信／受信ビームのＩＤ、これらのビームの予測Ｌ１－ＲＳＲＰ（predicted　L1-RSRP）、各ビームが上位Ｋ個に入る確率、これらのビームの角度などが挙げられる。 The output candidates of the AI model for BM case 1 are the IDs of the top K transmit/receive beams (K is an integer), the predicted L1-RSRP of these beams, and the predicted L1-RSRP of each beam. These include the probability of entry, the angle of these beams, etc.
　ＢＭケース２のＡＩモデルの出力の候補は、ＢＭケース１のＡＩモデルの出力の候補以外に、予測されるビーム障害が挙げられる。 Candidates for the output of the AI model in BM case 2 include predicted beam failures in addition to candidates for the output of the AI model in BM case 1.
　ところで、図１で示した性能モニタリングは、ＵＥにおいて行われてもよいし、ＢＳにおいて行われてもよい。しかしながら、ＡＩベースドビーム報告に関して、ＵＥ／ＢＳにおける性能モニタリングの具体的なライフサイクル管理についてはまだ検討が進んでいない。 By the way, the performance monitoring shown in FIG. 1 may be performed at the UE or at the BS. However, regarding AI-based beam reporting, no progress has been made in considering specific lifecycle management of performance monitoring in the UE/BS.
　性能モニタリングの実現方法を適切に規定しなければ、適切なオーバーヘッド低減／高精度なチャネル推定／高効率なリソースの利用が達成できず、通信スループット／通信品質の向上が抑制されるおそれがある。 If the method for implementing performance monitoring is not appropriately defined, appropriate overhead reduction, highly accurate channel estimation, and highly efficient resource utilization may not be achieved, and improvements in communication throughput/communication quality may be suppressed.
　そこで、本発明者らは、ＵＥ／ＢＳにおける性能モニタリングの好適な実現方法を着想した。 Therefore, the present inventors conceived of a suitable method for implementing performance monitoring in the UE/BS.
　以下、本開示に係る実施形態について、図面を参照して詳細に説明する。各実施形態に係る無線通信方法は、それぞれ単独で適用されてもよいし、組み合わせて適用されてもよい。 Hereinafter, embodiments according to the present disclosure will be described in detail with reference to the drawings. The wireless communication methods according to each embodiment may be applied singly or in combination.
　本開示において、「Ａ／Ｂ」及び「Ａ及びＢの少なくとも一方」は、互いに読み替えられてもよい。また、本開示において、「Ａ／Ｂ／Ｃ」は、「Ａ、Ｂ及びＣの少なくとも１つ」を意味してもよい。 In the present disclosure, "A/B" and "at least one of A and B" may be read interchangeably. Furthermore, in the present disclosure, "A/B/C" may mean "at least one of A, B, and C."
　本開示において、アクティベート、ディアクティベート、指示（又は指定（indicate））、選択（select）、設定（configure）、更新（update）、決定（determine）などは、互いに読み替えられてもよい。本開示において、サポートする、制御する、制御できる、動作する、動作できるなどは、互いに読み替えられてもよい。 In the present disclosure, "activate", "deactivate", "indicate", "select", "configure", "update", "determine", etc. may be read interchangeably. In this disclosure, supporting, controlling, being able to control, operating, capable of operating, etc. may be read interchangeably.
　本開示において、無線リソース制御（Radio　Resource　Control（ＲＲＣ））、ＲＲＣパラメータ、ＲＲＣメッセージ、上位レイヤパラメータ、フィールド、情報要素（Information　Element（ＩＥ））、設定などは、互いに読み替えられてもよい。本開示において、Medium　Access　Control制御要素（ＭＡＣ　Control　Element（ＣＥ））、更新コマンド、アクティベーション／ディアクティベーションコマンドなどは、互いに読み替えられてもよい。 In the present disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, upper layer parameters, fields, Information Elements (IEs), settings, etc. may be read interchangeably. In the present disclosure, the terms Medium Access Control Element (CE), update command, activation/deactivation command, etc. may be read interchangeably.
　本開示において、上位レイヤシグナリングは、例えば、Radio　Resource　Control（ＲＲＣ）シグナリング、Medium　Access　Control（ＭＡＣ）シグナリング、ブロードキャスト情報などのいずれか、又はこれらの組み合わせであってもよい。 In the present disclosure, the upper layer signaling may be, for example, Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information, etc., or a combination thereof.
　本開示において、ＭＡＣシグナリングは、例えば、ＭＡＣ制御要素（MAC　Control　Element（ＭＡＣ　ＣＥ））、ＭＡＣ　Protocol　Data　Unit（ＰＤＵ）などを用いてもよい。ブロードキャスト情報は、例えば、マスタ情報ブロック（Master　Information　Block（ＭＩＢ））、システム情報ブロック（System　Information　Block（ＳＩＢ））、最低限のシステム情報（Remaining　Minimum　System　Information（ＲＭＳＩ））、その他のシステム情報（Other　System　Information（ＯＳＩ））などであってもよい。 In the present disclosure, MAC signaling may use, for example, a MAC Control Element (MAC CE), a MAC Protocol Data Unit (PDU), or the like. Broadcast information includes, for example, a master information block (MIB), a system information block (SIB), a minimum system information (RMSI), and other system information ( Other System Information (OSI)) may also be used.
　本開示において、物理レイヤシグナリングは、例えば、下りリンク制御情報（Downlink　Control　Information（ＤＣＩ））、上りリンク制御情報（Uplink　Control　Information（ＵＣＩ））などであってもよい。 In the present disclosure, the physical layer signaling may be, for example, downlink control information (DCI), uplink control information (UCI), etc.
　本開示において、インデックス、識別子（Identifier（ＩＤ））、インディケーター、リソースＩＤなどは、互いに読み替えられてもよい。本開示において、シーケンス、リスト、セット、グループ、群、クラスター、サブセットなどは、互いに読み替えられてもよい。 In this disclosure, an index, an identifier (ID), an indicator, a resource ID, etc. may be read interchangeably. In this disclosure, sequences, lists, sets, groups, groups, clusters, subsets, etc. may be used interchangeably.
　本開示において、パネル、ＵＥパネル、パネルグループ、ビーム、ビームグループ、プリコーダ、Uplink（ＵＬ）送信エンティティ、送受信ポイント（Transmission/Reception　Point（ＴＲＰ））、基地局、空間関係情報（Spatial　Relation　Information（ＳＲＩ））、空間関係、ＳＲＳリソースインディケーター（SRS　Resource　Indicator（ＳＲＩ））、制御リソースセット（COntrol　REsource　SET（ＣＯＲＥＳＥＴ））、Physical　Downlink　Shared　Channel（ＰＤＳＣＨ）、コードワード（Codeword（ＣＷ））、トランスポートブロック（Transport　Block（ＴＢ））、参照信号（Reference　Signal（ＲＳ））、アンテナポート（例えば、復調用参照信号（DeModulation　Reference　Signal（ＤＭＲＳ））ポート）、アンテナポートグループ（例えば、ＤＭＲＳポートグループ）、グループ（例えば、空間関係グループ、符号分割多重（Code　Division　Multiplexing（ＣＤＭ））グループ、参照信号グループ、ＣＯＲＥＳＥＴグループ、Physical　Uplink　Control　Channel（ＰＵＣＣＨ）グループ、ＰＵＣＣＨリソースグループ）、リソース（例えば、参照信号リソース、ＳＲＳリソース）、リソースセット（例えば、参照信号リソースセット）、ＣＯＲＥＳＥＴプール、下りリンクのTransmission　Configuration　Indication　state（ＴＣＩ状態）（ＤＬ　ＴＣＩ状態）、上りリンクのＴＣＩ状態（ＵＬ　ＴＣＩ状態）、統一されたＴＣＩ状態（unified　TCI　state）、共通ＴＣＩ状態（common　TCI　state）、擬似コロケーション（Quasi-Co-Location（ＱＣＬ））、ＱＣＬ想定などは、互いに読み替えられてもよい。 In this disclosure, a panel, a UE panel, a panel group, a beam, a beam group, a precoder, an uplink (UL) transmitting entity, a transmission/reception point (TRP), a base station, and a spatial relation information (SRI) are described. )), spatial relationship, SRS resource indicator (SRI), control resource set (CONtrol REsource SET (CORESET)), Physical Downlink Shared Channel (PDSCH), codeword (CW), transport Block (Transport Block (TB)), reference signal (RS), antenna port (e.g. demodulation reference signal (DMRS) port), antenna port group (e.g. DMRS port group), groups (e.g., spatial relationship groups, Code Division Multiplexing (CDM) groups, reference signal groups, CORESET groups, Physical Uplink Control Channel (PUCCH) groups, PUCCH resource groups), resources (e.g., reference signal resources, SRS resource), resource set (for example, reference signal resource set), CORESET pool, downlink Transmission Configuration Indication state (TCI state) (DL TCI state), uplink TCI state (UL TCI state), unified TCI Unified TCI state, common TCI state, quasi-co-location (QCL), QCL assumption, etc. may be read interchangeably.
　本開示において、ＣＳＩ－ＲＳ、ノンゼロパワー（Non　Zero　Power（ＮＺＰ））ＣＳＩ－ＲＳ、ゼロパワー（Zero　Power（ＺＰ））ＣＳＩ－ＲＳ、ＣＳＩ干渉測定（CSI　Interference　Measurement（ＣＳＩ－ＩＭ））、ＣＳＩ－ＳＳＢ及びＳＳＢは、互いに読み替えられてもよい。また、ＣＳＩ－ＲＳは、その他の参照信号を含んでもよい。 In this disclosure, CSI-RS, Non Zero Power (NZP) CSI-RS, Zero Power (ZP) CSI-RS, CSI Interference Measurement (CSI-IM), CSI - SSB and SSB may be read interchangeably. Additionally, the CSI-RS may include other reference signals.
　本開示において、測定／報告されるＲＳは、ＣＳＩレポートのために測定／報告されるＲＳを意味してもよい。 In this disclosure, RS to be measured/reported may mean RS to be measured/reported for CSI reporting.
　本開示において、タイミング、時刻、時間、スロット、サブスロット、シンボル、サブフレームなどは、互いに読み替えられてもよい。 In the present disclosure, timing, time, time, slot, subslot, symbol, subframe, etc. may be read interchangeably.
　本開示において、方向、軸、次元、ドメイン、偏波、偏波成分などは、互いに読み替えられてもよい。 In the present disclosure, direction, axis, dimension, domain, polarization, polarization component, etc. may be read interchangeably.
　本開示において、ＲＳは、例えば、チャネル状態情報参照信号（Channel　State　Information　Reference　Signal（ＣＳＩ－ＲＳ））、ＳＳ／ＰＢＣＨブロック（ＳＳブロック（ＳＳＢ））などと互いに読み替えられてもよい。また、ＲＳインデックスは、ＣＳＩ－ＲＳリソースインディケーター（CSI-RS　Resource　Indicator（ＣＲＩ））、ＳＳ／ＰＢＣＨブロックリソースインディケーター（SS/PBCH　Block　Indicator（ＳＳＢＲＩ））などと互いに読み替えられてもよい。 In the present disclosure, RS may be interchanged with, for example, Channel State Information Reference Signal (CSI-RS), SS/PBCH block (SS block (SSB)), etc. Further, the RS index may be interchanged with a CSI-RS resource indicator (CSI-RS resource indicator (CRI)), an SS/PBCH block resource indicator (SS/PBCH block indicator (SSBRI)), etc.
　本開示において、チャネル測定／推定は、例えば、チャネル状態情報参照信号（Channel　State　Information　Reference　Signal（ＣＳＩ－ＲＳ））、同期信号（Synchronization　Signal（ＳＳ））、同期信号／ブロードキャストチャネル（Synchronization　Signal/Physical　Broadcast　Channel（ＳＳ／ＰＢＣＨ））ブロック、復調用参照信号（DeModulation　Reference　Signal（ＤＭＲＳ））、測定用参照信号（Sounding　Reference　Signal（ＳＲＳ））などの少なくとも１つを用いて行われてもよい。 In the present disclosure, channel measurement/estimation includes, for example, a channel state information reference signal (CSI-RS), a synchronization signal (SS), a synchronization signal/broadcast channel (Synchronization Signal/Physical It may be performed using at least one of a Broadcast Channel (SS/PBCH) block, a demodulation reference signal (DMRS), a measurement reference signal (Sounding Reference Signal (SRS)), and the like.
　本開示において、ＣＳＩは、チャネル品質インディケーター（Channel　Quality　Indicator（ＣＱＩ））、プリコーディング行列インディケーター（Precoding　Matrix　Indicator（ＰＭＩ））、ＣＳＩ－ＲＳリソースインディケーター（CSI-RS　Resource　Indicator（ＣＲＩ））、ＳＳ／ＰＢＣＨブロックリソースインディケーター（SS/PBCH　Block　Resource　Indicator（ＳＳＢＲＩ））、レイヤインディケーター（Layer　Indicator（ＬＩ））、ランクインディケーター（Rank　Indicator（ＲＩ））、Ｌ１－ＲＳＲＰ（レイヤ１における参照信号受信電力（Layer　1　Reference　Signal　Received　Power））、Ｌ１－ＲＳＲＱ（Reference　Signal　Received　Quality）、Ｌ１－ＳＩＮＲ（Signal　to　Interference　plus　Noise　Ratio）、Ｌ１－ＳＮＲ（Signal　to　Noise　Ratio）、チャネル行列（又はチャネル係数）に関する情報、プリコーディング行列（又はプリコーディング係数）に関する情報などの少なくとも１つを含んでもよい。 In this disclosure, CSI includes a channel quality indicator (CQI), a precoding matrix indicator (PMI), and a CSI-RS resource indicator (CRI). , SS/PBCH Block Resource Indicator (SSBRI), Layer Indicator (LI), Rank Indicator (RI), L1-RSRP (Reference in Layer 1) Signal received power (Layer 1 Reference Signal Received Power), L1-RSRQ (Reference Signal Received Quality), L1-SINR (Signal to Interference plus Noise Ratio), L1-SNR (Signal to Noise Ratio), channel matrix (or channel information regarding the precoding matrix (or precoding coefficients), and the like.
　本開示において、ＵＣＩ、ＣＳＩレポート、ＣＳＩフィードバック、フィードバック情報、フィードバックビット、ＣＳＩフィードバック方法、ＣＳＩフィードバックスキーム、ビームレポート、ビーム報告スキームなどは、互いに読み替えられてもよい。また、本開示において、ビット、ビット列、ビット系列、系列、値、情報、ビットから得られる値、ビットから得られる情報などは、互いに読み替えられてもよい。 In this disclosure, UCI, CSI report, CSI feedback, feedback information, feedback bit, CSI feedback method, CSI feedback scheme, beam report, beam report scheme, etc. may be read interchangeably. Furthermore, in the present disclosure, bits, bit strings, bit sequences, sequences, values, information, values obtained from bits, information obtained from bits, etc. may be interchanged.
　本開示において、「モデル／非ＡＩベースドビーム報告」は、モデル、ビーム報告方法、ビーム報告スキームなどと互いに読み替えられてもよい。 In this disclosure, "model/non-AI based beam reporting" may be interchanged with model, beam reporting method, beam reporting scheme, etc.
　以下の実施形態では、ＵＥ－ＢＳ間の通信に関するＡＩモデルを説明するため、関連する主体はＵＥ及びＢＳであるが、本開示の各実施形態の適用は、これに限られない。例えば、別の主体間の通信（例えば、ＵＥ－ＵＥ間の通信）については、下記実施形態のＵＥ及びＢＳを、第１のＵＥ及び第２のＵＥで読み替えてもよい。言い換えると、本開示のＵＥ、ＢＳなどは、いずれも任意のＵＥ／ＢＳで読み替えられてもよい。 In the following embodiments, the relevant entities are the UE and the BS in order to explain an AI model regarding communication between the UE and the BS, but the application of each embodiment of the present disclosure is not limited to this. For example, for communication between different entities (for example, communication between UE and UE), the UE and BS in the embodiment below may be replaced with a first UE and a second UE. In other words, the UE, BS, etc. of the present disclosure may be replaced with any UE/BS.
（無線通信方法）
　以下では、ＡＩベースドビーム報告に関して、ＵＥ／ＢＳにおける性能モニタリングのライフサイクル管理フレームワークにおける各ステップについて説明する。 (Wireless communication method)
In the following, each step in the lifecycle management framework for performance monitoring at the UE/BS with respect to AI-based beam reporting is described.
　図３は、一実施形態に係るＵＥにおける性能モニタリングのライフサイクル管理フレームワークの一例を示す図である。 FIG. 3 is a diagram illustrating an example of a lifecycle management framework for performance monitoring in a UE according to an embodiment.
　ＲＳ測定のステップでは、ＵＥは、予測のためのＲＳ（ＣＳＩ－ＲＳ／ＳＳＢ）と性能モニタリングのためのＲＳとを測定する。 In the RS measurement step, the UE measures RS for prediction (CSI-RS/SSB) and RS for performance monitoring.
　性能モニタリングのステップでは、ＵＥは、モデル及びフォールバックスキーム（非ＡＩベースドビーム報告）の性能をモニタする。 In the performance monitoring step, the UE monitors the performance of the model and fallback scheme (non-AI based beam reporting).
　ＵＥにおけるモデル評価のステップでは、ＵＥは、モニタされる／報告されるモデル及びフォールバックスキーム（非ＡＩベースドビーム報告）の性能を評価する。 In the step of model evaluation at the UE, the UE evaluates the performance of the monitored/reported model and fallback scheme (non-AI based beam reporting).
　性能報告のステップでは、ＵＥは、モニタされた上記性能をＮＷに報告する。 In the performance reporting step, the UE reports the monitored performance to the NW.
　モデル要求のステップでは、ＵＥは、どのモデルを適用すべきであるか、又はフォールバックスキームが適用されるべきか否かに関する要求を、ＮＷに送信する。 In the model request step, the UE sends a request to the NW regarding which model should be applied or whether a fallback scheme should be applied.
　モデルアクティベーション／ディアクティベーションのステップでは、ＵＥは、どのスキーム（モデル）がアクティベートされるかを指示されてもよい。ＵＥは、あるモデル又はフォールバックスキームをアクティベートしてもよい。 In the model activation/deactivation step, the UE may be instructed which scheme (model) is to be activated. The UE may activate some model or fallback scheme.
　なお、図示される一部のステップ（例えば破線で示されるステップ）は、必要に応じて実施されればよい。 Note that some of the illustrated steps (for example, steps indicated by broken lines) may be performed as necessary.
　図４は、一実施形態に係るＢＳにおける性能モニタリングのライフサイクル管理フレームワークの一例を示す図である。 FIG. 4 is a diagram illustrating an example of a life cycle management framework for performance monitoring in a BS according to an embodiment.
　ＲＳ測定のステップでは、ＵＥは、予測のためのＲＳ（ＣＳＩ－ＲＳ／ＳＳＢ）と性能モニタリングのためのＲＳとを測定する。 In the RS measurement step, the UE measures RS for prediction (CSI-RS/SSB) and RS for performance monitoring.
　性能モニタリング向けの報告のステップでは、ＵＥは、ＮＷ（ＢＳ）における性能モニタリングのための情報を報告する。 In the reporting step for performance monitoring, the UE reports information for performance monitoring at the NW (BS).
　ＮＷにおける性能モニタリングのステップでは、ＮＷは、モデル及びフォールバックスキーム（非ＡＩベースドビーム報告）の性能をモニタする。 In the step of performance monitoring in the NW, the NW monitors the performance of the model and fallback scheme (non-AI based beam reporting).
　ＮＷにおけるモデル評価のステップでは、ＮＷは、モデル及びフォールバックスキームの性能を評価する。 In the step of model evaluation in the NW, the NW evaluates the performance of the model and the fallback scheme.
　モデルアクティベーション／ディアクティベーションのステップでは、ＵＥは、どのスキーム（モデル）がアクティベートされるかを指示されてもよい。ＵＥは、あるモデル又はフォールバックスキームをアクティベートしてもよい。 In the model activation/deactivation step, the UE may be instructed which scheme (model) is to be activated. The UE may activate some model or fallback scheme.
　なお、図示される一部のステップは、必要に応じて実施されればよい。 Note that some of the illustrated steps may be performed as necessary.
　以下で説明する各実施形態では、図３及び図４に示したステップの詳細を説明する。 In each embodiment described below, details of the steps shown in FIGS. 3 and 4 will be described.
＜第０の実施形態＞
　第０の実施形態は、ＲＳ測定に関する。 <0th embodiment>
The zeroth embodiment relates to RS measurement.
　上述したセットＡは、性能モニタリングのために参照されるセットに該当してもよく、性能モニタリングリファレンス（performance　monitoring　reference）と呼ばれてもよい。セットＡに対応するＲＳは、その品質がＡＩモデルによって予測されるＲＳに該当してもよい。 The set A described above may correspond to a set referred to for performance monitoring, and may be called a performance monitoring reference. The RSs corresponding to set A may correspond to RSs whose quality is predicted by the AI model.
　上述したセットＢは、ＡＩ予測のための入力の生成に用いられるセットに該当してもよい。セットＢに対応するＲＳは、ビーム予測における実際のチャネル測定のために用いられるＲＳに該当してもよく、ターゲットＣＳＩの計算に用いられてもよい。 The set B described above may correspond to a set used to generate input for AI prediction. The RS corresponding to set B may correspond to the RS used for actual channel measurement in beam prediction, and may be used for target CSI calculation.
　ここで、本開示において、ターゲットＣＳＩは、例えば、チャネル測定に基づいて計算されるＣＳＩ（calculated　CSI）であってもよいし、理想ＣＳＩ（ideal　CSI）であってもよい。本開示において、理想ＣＳＩは、ＡＩモデルから出力される誤差のない理想的なＣＳＩを意味してもよいし、特定の入力（又は任意の入力）に対応してＡＩモデルから出力されるＣＳＩを意味してもよい。理想ＣＳＩは、固定ＣＳＩ（fixed　CSI）などと呼ばれてもよい。 Here, in the present disclosure, the target CSI may be, for example, a CSI (calculated CSI) calculated based on channel measurements, or an ideal CSI (ideal CSI). In this disclosure, ideal CSI may mean an ideal CSI without error output from an AI model, or may mean CSI output from an AI model in response to a specific input (or any input). It can also mean The ideal CSI may also be called fixed CSI.
　ＵＥにおけるＲＳ測定では、セットＡのＬ１－ＲＳＲＰ及びセットＢのＬ１－ＲＳＲＰを算出することができる。また、ＵＥは、セットＢのＬ１－ＲＳＲＰをＡＩモデルに入力して、セットＡの予測Ｌ１－ＲＳＲＰ（Ｌ１－ＲＳＲＰ’と呼ばれてもよい）を算出することができる。 In the RS measurement at the UE, the L1-RSRP of set A and the L1-RSRP of set B can be calculated. The UE may also input the L1-RSRP of Set B into the AI model to calculate the predicted L1-RSRP of Set A (which may be referred to as L1-RSRP').
　なお、後述するＵＥにおける性能モニタリングでは、セットＡのＬ１－ＲＳＲＰ及びセットＢのＬ１－ＲＳＲＰを比較することによって、ＡＩモデルによるゲインの上限を評価できる。また、セットＡのＬ１－ＲＳＲＰ及びセットＡのＬ１－ＲＳＲＰ’を比較することによって、ＡＩモデルの性能を評価できる。また、セットＢのＬ１－ＲＳＲＰ及びセットＡのＬ１－ＲＳＲＰ’を比較することによって、ＡＩモデルによるゲインを評価できる。 Note that in performance monitoring in the UE, which will be described later, by comparing L1-RSRP of set A and L1-RSRP of set B, the upper limit of the gain by the AI model can be evaluated. Furthermore, by comparing the L1-RSRP of set A and the L1-RSRP' of set A, the performance of the AI model can be evaluated. Also, by comparing L1-RSRP of set B and L1-RSRP' of set A, the gain by the AI model can be evaluated.
　ＢＳにおける性能モニタリングでも、ＵＥからＮＷに対して、セットＡのＬ１－ＲＳＲＰ、セットＢのＬ１－ＲＳＲＰ、セットＡのＬ１－ＲＳＲＰ’などが通知されることによって、ＵＥにおける性能モニタリングと同様の評価を行うことが可能である。 In performance monitoring at the BS, the UE notifies the NW of L1-RSRP of set A, L1-RSRP of set B, L1-RSRP' of set A, etc., so that the same evaluation as performance monitoring at the UE can be performed. It is possible to do this.
［ＣＳＩ－ＲＳリソース］
　ＵＥは、ビーム予測のためのＣＳＩ－ＲＳリソース／ＣＳＩ－ＲＳリソースセット／ＣＳＩリソース設定／ＣＳＩレポート設定を通知されてもよいし、性能モニタリングリファレンスのためのＣＳＩ－ＲＳリソース／ＣＳＩ－ＲＳリソースセット／ＣＳＩリソース設定／ＣＳＩレポート設定を通知されてもよい。 [CSI-RS resource]
The UE may be informed of CSI-RS resource/CSI-RS resource set/CSI resource configuration/CSI report configuration for beam prediction and CSI-RS resource/CSI-RS resource set for performance monitoring reference. /CSI resource settings/CSI report settings may be notified.
　ビーム予測のためのＣＳＩ－ＲＳリソースと性能モニタリングリファレンスのためのＣＳＩ－ＲＳリソースとのマッピング（対応関係）は、以下の少なくとも１つに該当してもよい：
　・両者（ビーム予測のためのＣＳＩ－ＲＳリソース及び性能モニタリングリファレンスのためのＣＳＩ－ＲＳリソース）は異なるＣＳＩリソース設定（CSI-ResourceConfig）に対応する、
　・両者は共通のＣＳＩリソース設定（CSI-ResourceConfig）に対応するが、異なるＣＳＩ－ＲＳリソースセットに対応する、
　・両者は共通のＣＳＩ－ＲＳリソースセットに対応するが、異なるＣＳＩ－ＲＳリソースに対応する。
　・１つのＣＳＩ－ＲＳリソースが両者を含む。 The mapping (correspondence) between CSI-RS resources for beam prediction and CSI-RS resources for performance monitoring reference may correspond to at least one of the following:
- Both (CSI-RS resource for beam prediction and CSI-RS resource for performance monitoring reference) correspond to different CSI resource configurations (CSI-ResourceConfig),
- Both correspond to a common CSI resource configuration (CSI-ResourceConfig), but correspond to different CSI-RS resource sets,
- Both correspond to a common CSI-RS resource set, but correspond to different CSI-RS resources.
- One CSI-RS resource includes both.
　なお、当該マッピングに関する情報（例えば、関連付けられるＣＳＩ－ＲＳリソース、関連付けられるＣＳＩ－ＲＳリソースセット）は、ＮＷからＵＥに対して通知されてもよいし、ＡＩモデル情報に含まれてもよい。 Note that information regarding the mapping (for example, associated CSI-RS resources, associated CSI-RS resource sets) may be notified from the NW to the UE, or may be included in the AI model information.
　また、本開示において、ＣＳＩ－ＲＳリソースセットは、ＲＲＣのNZP-CSI-RS-ResourceSet情報要素、CSI-SSB-ResourceSet情報要素及びCSI-IM-ResourceSet情報要素の少なくとも１つと互いに読み替えられてもよい。本開示において、ＣＳＩ－ＲＳリソースは、ＲＲＣのNZP-CSI-RS-Resource情報要素と互いに読み替えられてもよい。 Further, in the present disclosure, the CSI-RS resource set may be mutually read as at least one of the NZP-CSI-RS-ResourceSet information element, the CSI-SSB-ResourceSet information element, and the CSI-IM-ResourceSet information element of RRC. . In this disclosure, the CSI-RS resource may be interchanged with the NZP-CSI-RS-Resource information element of RRC.
［ＣＳＩ－ＲＳ機会］
　性能モニタリング（性能比較）の際に利用されるビーム予測のためのＣＳＩ－ＲＳ機会（occasion）と性能モニタリングリファレンスのためのＣＳＩ－ＲＳ機会との組み合わせの決定方法について説明する。なお、性能モニタリングのためのＣＳＩ－ＲＳ機会は、ビーム予測のためのＣＳＩ－ＲＳ機会／性能モニタリングリファレンスのためのＣＳＩ－ＲＳ機会を意味してもよい。 [CSI-RS Opportunity]
A method for determining a combination of a CSI-RS occasion for beam prediction and a CSI-RS occasion for performance monitoring reference used during performance monitoring (performance comparison) will be described. Note that the CSI-RS opportunity for performance monitoring may mean a CSI-RS opportunity for beam prediction/CSI-RS opportunity for performance monitoring reference.
　方法１では、ＵＥは、ＣＳＩ参照リソースに基づいて性能モニタリングのためのＣＳＩ－ＲＳ機会を決定してもよい。例えば、ＵＥは、ＣＳＩ参照リソース（CSI　reference　resource）より遅くない（no　later　than）最近の（most　recent）ビーム予測のためのＣＳＩ－ＲＳ機会／性能モニタリングリファレンスのためのＣＳＩ－ＲＳ機会を決定してもよい。 In method 1, the UE may determine CSI-RS opportunities for performance monitoring based on CSI reference resources. For example, the UE determines a CSI-RS opportunity for a most recent beam prediction/a CSI-RS opportunity for a performance monitoring reference no later than a CSI reference resource. It's okay.
　なお、本開示において、ビーム予測のためのＣＳＩ－ＲＳ機会は、当該ＣＳＩ機会に基づく予測ＣＳＩ時間（predicted　CSI　time）がＣＳＩ参照リソースより遅くないＣＳＩ－ＲＳ機会に該当してもよい。なお、予測ＣＳＩ時間は、ＡＩモデルによって予測される出力に対応する（当該予測が有効である）時間（例えば、シンボル、スロット、サブスロット、サブフレーム、秒（ミリ秒）などの少なくとも１つで表されてもよい）を意味してもよい。本開示において、予測ＣＳＩ時間は、予測時間と互いに読み替えられてもよい。 Note that in the present disclosure, a CSI-RS opportunity for beam prediction may correspond to a CSI-RS opportunity in which a predicted CSI time based on the CSI opportunity is not later than the CSI reference resource. Note that the predicted CSI time is at least one of a symbol, a slot, a subslot, a subframe, a second (millisecond), etc., corresponding to the output predicted by the AI model (the prediction is valid). may be expressed). In the present disclosure, predicted CSI time may be read as predicted time.
　方法２では、ＵＥは、予測されるＣＳＩ時間と性能モニタリングリファレンスのためのＣＳＩ－ＲＳ機会との関係に基づいて、ビーム予測のためのＣＳＩ－ＲＳ機会を決定してもよい。 In method 2, the UE may determine the CSI-RS opportunity for beam prediction based on the relationship between the predicted CSI time and the CSI-RS opportunity for performance monitoring reference.
　方法２－１では、ＵＥは、まず、ＣＳＩ参照リソースより遅くない、性能モニタリングリファレンスのために参照されるＣＳＩ－ＲＳ機会を決定する。そして、ＵＥは、性能モニタリングリファレンスのために参照されるＣＳＩ－ＲＳ機会より遅くない最近の予測ＣＳＩ時間に対応するビーム予測のためのＣＳＩ－ＲＳ機会に基づいて、性能を算出してもよい。 In method 2-1, the UE first determines a CSI-RS opportunity to be referenced for performance monitoring reference that is no later than the CSI reference resource. The UE may then calculate the performance based on the CSI-RS opportunity for beam prediction that corresponds to a recent predicted CSI time that is not later than the CSI-RS opportunity referenced for the performance monitoring reference.
　方法２－２では、ＵＥは、まず、ＣＳＩ参照リソースより遅くない、予測ＣＳＩ時間に対応するビーム予測のためのＣＳＩ－ＲＳ機会を決定する。そして、ＵＥは、ビーム予測のために参照されるＣＳＩ－ＲＳ機会に対応する予測ＣＳＩ時間より遅くない最近の性能モニタリングリファレンスのためのＣＳＩ－ＲＳ機会に基づいて、性能を算出してもよい。 In method 2-2, the UE first determines a CSI-RS opportunity for beam prediction that corresponds to a predicted CSI time that is not later than the CSI reference resource. The UE may then calculate the performance based on the CSI-RS opportunity for a recent performance monitoring reference that is no later than the predicted CSI time corresponding to the CSI-RS opportunity referenced for beam prediction.
　なお、ビーム予測のためのＣＳＩ－ＲＳ機会と対応する予測ＣＳＩ時間との時間オフセット（時間差）に関する情報は、予め規格において規定されてもよいし、ＵＥ能力に基づいて決定されてもよいし、ＮＷからＵＥに通知されてもよいし、ＡＩモデル情報に含まれてもよい（モデルに基づいて決定されてもよい）。 Note that information regarding the time offset (time difference) between the CSI-RS opportunity for beam prediction and the corresponding predicted CSI time may be specified in advance in the standard, or may be determined based on the UE capability, It may be notified from the NW to the UE, or it may be included in the AI model information (it may be determined based on the model).
　方法２－３では、ＵＥは、まず、ＣＳＩ参照リソースより遅くない、性能モニタリングリファレンスのために参照されるＣＳＩ－ＲＳ機会を決定する。そして、ＵＥは、性能モニタリングリファレンスのために参照されるＣＳＩ－ＲＳ機会よりＸ単位時間遅くなく、又はＸ単位時間早くない（言い換えると、当該ＣＳＩ－ＲＳ機会を基準としてＸ単位時間の範囲にある）予測ＣＳＩ時間に対応するビーム予測のためのＣＳＩ－ＲＳ機会に基づいて、性能を算出してもよい。 In method 2-3, the UE first determines a CSI-RS opportunity to be referenced for performance monitoring reference that is no later than the CSI reference resource. and the UE is not later than X time units or earlier than the CSI-RS opportunity referenced for the performance monitoring reference (in other words, within the range of X time units with respect to the CSI-RS opportunity). ) Performance may be calculated based on the CSI-RS opportunity for beam prediction corresponding to the predicted CSI time.
　方法２－４では、ＵＥは、まず、ＣＳＩ参照リソースより遅くない、予測ＣＳＩ時間に対応するビーム予測のためのＣＳＩ－ＲＳ機会を決定する。そして、ＵＥは、ビーム予測のために参照されるＣＳＩ－ＲＳ機会に対応する予測ＣＳＩ時間よりＸ単位時間遅くなく、又はＸ単位時間早くない（言い換えると、当該ＣＳＩ－ＲＳ機会を基準としてＸ単位時間の範囲にある）性能モニタリングリファレンスのためのＣＳＩ－ＲＳ機会に基づいて、性能を算出してもよい。 In method 2-4, the UE first determines a CSI-RS opportunity for beam prediction that corresponds to a predicted CSI time that is no later than the CSI reference resource. and the UE is not later than X units of time or earlier than the predicted CSI time corresponding to the CSI-RS opportunity referred to for beam prediction (in other words, X units of time with respect to the CSI-RS opportunity) The performance may be calculated based on the CSI-RS opportunities for performance monitoring reference (within a range of time).
　なお、Ｘの値に関する情報は、予め規格において規定されてもよいし、ＵＥ能力に基づいて決定されてもよいし、ＮＷからＵＥに通知されてもよいし、ＡＩモデル情報に含まれてもよい（モデルに基づいて決定されてもよい）。 Note that the information regarding the value of (may be determined based on the model).
　なお、方法２－１から２－４においてまず決定される性能モニタリングリファレンスのために参照されるＣＳＩ－ＲＳ機会／ビーム予測のためのＣＳＩ－ＲＳ機会は、方法１に基づいて決定されてもよい。 Note that the CSI-RS opportunity referred to for the performance monitoring reference that is first determined in Methods 2-1 to 2-4/CSI-RS opportunity for beam prediction may be determined based on Method 1. .
　図５は、方法２－１又は２－２に基づいて決定される性能モニタリングのためのＣＳＩ－ＲＳ機会の一例を示す図である。本例では、ビーム予測のために参照されるＣＳＩ－ＲＳ機会として機会＃１及び＃２が、性能モニタリングリファレンスのために参照されるＣＳＩ－ＲＳ機会として機会＃ａー＃ｃが示されている。また、機会＃１及び＃２に対応する予測ＣＳＩ時間として、それぞれ予測ＣＳＩ時間インスタンス候補における機会＃１及び＃２が示されている。 FIG. 5 is a diagram illustrating an example of CSI-RS opportunities for performance monitoring determined based on method 2-1 or 2-2. In this example, opportunities #1 and #2 are shown as CSI-RS opportunities to be referenced for beam prediction, and opportunities #a to #c are shown as CSI-RS opportunities to be referenced for performance monitoring reference. . Furthermore, opportunities #1 and #2 in the predicted CSI time instance candidates are shown as predicted CSI times corresponding to opportunities #1 and #2, respectively.
　方法２－１の場合、性能モニタリングリファレンスのために参照されるＣＳＩ－ＲＳ機会は機会＃ｃと決定される。また、ビーム予測のために参照されるＣＳＩ－ＲＳ機会は機会＃１と決定される。 In the case of method 2-1, the CSI-RS opportunity referred to for performance monitoring reference is determined to be opportunity #c. Also, the CSI-RS opportunity referred to for beam prediction is determined to be opportunity #1.
　方法２－２の場合、ビーム予測のために参照されるＣＳＩ－ＲＳ機会は機会＃２と決定される。性能モニタリングリファレンスのために参照されるＣＳＩ－ＲＳ機会は機会＃ｃと決定される。 In the case of method 2-2, the CSI-RS opportunity referred to for beam prediction is determined to be opportunity #2. The CSI-RS opportunity referenced for performance monitoring reference is determined to be opportunity #c.
　図６は、方法２－３又は２－４に基づいて決定される性能モニタリングのためのＣＳＩ－ＲＳ機会の一例を示す図である。本例では、性能モニタリングリファレンスのために参照されるＣＳＩ－ＲＳ機会として機会＃ｄ及び＃ｅが示されている点が図５と異なり、他は図５と同じである。本例では、Ｘ＝０と仮定する。 FIG. 6 is a diagram illustrating an example of CSI-RS opportunities for performance monitoring determined based on method 2-3 or 2-4. This example differs from FIG. 5 in that opportunities #d and #e are shown as CSI-RS opportunities to be referred to for performance monitoring reference, but otherwise is the same as FIG. 5. In this example, it is assumed that X=0.
　方法２－３の場合、性能モニタリングリファレンスのために参照されるＣＳＩ－ＲＳ機会は機会＃ｅと決定される。また、ビーム予測のために参照されるＣＳＩ－ＲＳ機会は機会＃２と決定される。 In the case of method 2-3, the CSI-RS opportunity referred to for performance monitoring reference is determined to be opportunity #e. Also, the CSI-RS opportunity referred to for beam prediction is determined to be opportunity #2.
　方法２－４の場合、ビーム予測のために参照されるＣＳＩ－ＲＳ機会は機会＃２と決定される。性能モニタリングリファレンスのために参照されるＣＳＩ－ＲＳ機会は機会＃ｅと決定される。 In the case of method 2-4, the CSI-RS opportunity referred to for beam prediction is determined to be opportunity #2. The CSI-RS opportunity referred to for performance monitoring reference is determined to be opportunity #e.
　なお、第０の実施形態において、ＵＥが性能を算出するという記載は、ＢＳが性能を算出することと互いに読み替えられてもよい。 Note that in the zeroth embodiment, the description that the UE calculates the performance may be read as the BS calculates the performance.
　以上説明した第０の実施形態によれば、ＵＥは適切に性能モニタリングを実施できる。 According to the zeroth embodiment described above, the UE can appropriately perform performance monitoring.
＜第１の実施形態＞
　第１の実施形態は、ＵＥにおける性能モニタリングに関する。 <First embodiment>
The first embodiment relates to performance monitoring at the UE.
　ＵＥは、どのＡＩモデル／フォールバックスキームの性能をモニタするかについて、ネットワークから通知されてもよい。モニタ対象のＡＩモデルの判断については、第５の実施形態の変形例において後述する。 The UE may be informed by the network about which AI models/fallback schemes to monitor performance. Determination of the AI model to be monitored will be described later in a modification of the fifth embodiment.
　本開示において、フォールバックスキームは、非ＡＩベースドビーム報告、予測なしのビームレポートなどと互いに読み替えられてもよい。 In this disclosure, fallback scheme may be interchanged with non-AI based beam reporting, beam reporting without prediction, etc.
　ＵＥはあるモデル／フォールバックスキームについて、性能をモニタしてもよい。本開示において、性能がモニタされるモデルは、モニタされるモデル（monitored　model）と呼ばれてもよい。なお、レジスタされる（レジストレーションが適用される）モデル／設定されるモデルは、モニタされるモデル／アクティベートされるモデルに該当してもよい。 The UE may monitor performance for certain models/fallback schemes. In this disclosure, a model whose performance is monitored may be referred to as a monitored model. Note that a model to be registered (to which registration is applied)/a model to be set may correspond to a model to be monitored/a model to be activated.
　本開示において、モニタされる性能は、以下の少なくとも１つであってもよい：
　（１）性能モニタリングリファレンスについての実際のＬ１－ＲＳＲＰ（予測なし）、
　（２）ＡＩモデル予測の入力のために利用される実際のＬ１－ＲＳＲＰ（予測なし）、
　（３）ＡＩモデルによる予測Ｌ１－ＲＳＲＰ、
　（４）１位の予測Ｌ１－ＲＳＲＰと、性能モニタリングリファレンスについての１位のＬ１－ＲＳＲＰと、のＬ１－ＲＳＲＰ差分、
　（５）上位Ｋ個の予測Ｌ１－ＲＳＲＰの１つと、性能モニタリングリファレンスについての１位のＬ１－ＲＳＲＰと、の最小Ｌ１－ＲＳＲＰ差分、
　（６）上位Ｋ個の予測Ｌ１－ＲＳＲＰの１つに関連付けられるＣＲＩ／ＳＳＢＲＩが、性能モニタリングリファレンスについての１位のＬ１－ＲＳＲＰに対応するＣＲＩ／ＳＳＢＲＩに該当するか否か、
　（７）１位の予測Ｌ１－ＲＳＲＰに関連付けられるＣＲＩ／ＳＳＢＲＩが、性能モニタリングリファレンスについての上位Ｋ個のＬ１－ＲＳＲＰに対応するＣＲＩ／ＳＳＢＲＩの１つに該当するか否か、
　（８）上位Ｋ個の予測Ｌ１－ＲＳＲＰに関連付けられる全てのＣＲＩ／ＳＳＢＲＩが、性能モニタリングリファレンスについての上位Ｋ個のＬ１－ＲＳＲＰに対応するＣＲＩ／ＳＳＢＲＩに該当するか否か、
　（９）ある期間にわたって、上位Ｋ個の予測Ｌ１－ＲＳＲＰの１つに関連付けられるＣＲＩ／ＳＳＢＲＩが、性能モニタリングリファレンスについての１位のＬ１－ＲＳＲＰに対応するＣＲＩ／ＳＳＢＲＩに該当する精度（例えば、割合）、
　（１０）ある期間にわたって、１位の予測Ｌ１－ＲＳＲＰに関連付けられるＣＲＩ／ＳＳＢＲＩが、性能モニタリングリファレンスについての上位Ｋ個のＬ１－ＲＳＲＰに対応するＣＲＩ／ＳＳＢＲＩの１つに該当する精度（例えば、割合）、
　（１１）ある期間にわたって、上位Ｋ個の予測Ｌ１－ＲＳＲＰに関連付けられる全てのＣＲＩ／ＳＳＢＲＩが、性能モニタリングリファレンスについての上位Ｋ個のＬ１－ＲＳＲＰに対応するＣＲＩ／ＳＳＢＲＩに該当する精度（例えば、割合）、
　（１２）ある期間にわたる、１位の予測Ｌ１－ＲＳＲＰと、性能モニタリングリファレンスについての１位のＬ１－ＲＳＲＰと、のＬ１－ＲＳＲＰ差分のＸ％パーセンタイル、
　（１３）ある期間にわたる、上位Ｋ個の予測Ｌ１－ＲＳＲＰの１つと、性能モニタリングリファレンスについての１位のＬ１－ＲＳＲＰと、の最小Ｌ１－ＲＳＲＰ差分のＸ％パーセンタイル、
　（１４）ある期間にわたって、１位の予測Ｌ１－ＲＳＲＰが、性能モニタリングリファレンスについての１位のＬ１－ＲＳＲＰのＹｄＢ（デシベル）内に含まれるか否か、
　（１５）ある期間にわたって、１位の予測Ｌ１－ＲＳＲＰが、性能モニタリングリファレンスについての１位のＬ１－ＲＳＲＰのＹｄＢ内に含まれるパーセンテージ。 In this disclosure, the monitored performance may be at least one of the following:
(1) Actual L1-RSRP (no prediction) for performance monitoring reference;
(2) Actual L1-RSRP used for input of AI model prediction (no prediction);
(3) Predicted L1-RSRP by AI model,
(4) L1-RSRP difference between the predicted L1-RSRP of the first place and the L1-RSRP of the first place for the performance monitoring reference;
(5) the minimum L1-RSRP difference between one of the top K predicted L1-RSRPs and the first-ranked L1-RSRP for the performance monitoring reference;
(6) whether the CRI/SSBRI associated with one of the top K predicted L1-RSRPs corresponds to the CRI/SSBRI corresponding to the first-ranked L1-RSRP for the performance monitoring reference;
(7) whether the CRI/SSBRI associated with the first predicted L1-RSRP corresponds to one of the CRI/SSBRIs corresponding to the top K L1-RSRPs for the performance monitoring reference;
(8) whether all CRI/SSBRIs associated with the top K predicted L1-RSRPs correspond to the CRI/SSBRI corresponding to the top K L1-RSRPs for the performance monitoring reference;
(9) Over a period of time, the accuracy (e.g., ratio),
(10) Over a period of time, the accuracy (e.g., ratio),
(11) Over a period of time, all CRI/SSBRIs associated with the top K predicted L1-RSRPs fall to the CRI/SSBRI corresponding to the top K L1-RSRPs for the performance monitoring reference (e.g., ratio),
(12) X% percentile of the L1-RSRP difference between the first-place predicted L1-RSRP and the first-place L1-RSRP for the performance monitoring reference over a period of time;
(13) X% percentile of the minimum L1-RSRP difference between one of the top K predicted L1-RSRPs and the #1 ranked L1-RSRP for the performance monitoring reference over a period of time;
(14) whether, over a period of time, the first predicted L1-RSRP is within YdB (decibels) of the first L1-RSRP for the performance monitoring reference;
(15) Percentage over a period of time that the predicted L1-RSRP of #1 is within YdB of the L1-RSRP of #1 for the performance monitoring reference.
　なお、本開示において、Ｌ１－ＲＳＲＰ、Ｌ１－ＳＩＮＲ及びその他のメトリック（例えば、ビームに関する品質の指標、ＣＳＩなど）は、互いに読み替えられてもよい。 Note that in the present disclosure, L1-RSRP, L1-SINR, and other metrics (for example, beam-related quality index, CSI, etc.) may be read interchangeably.
　また、Ｘ％パーセンタイルは、小さい方からデータを並べた全体のうちＸ％の位置にある値を意味してもよい。 Furthermore, the X% percentile may mean the value located at the X% position of the entire data arranged from the smallest.
　上記Ｋ、Ｘ、Ｙ、ある期間などの値（又は当該値に関する情報）は、予め規格において規定されてもよいし、ＵＥ能力に基づいて決定されてもよいし、ＮＷからＵＥに通知されてもよいし、ＡＩモデル情報に含まれてもよい（モデルに基づいて決定されてもよい）。 Values such as K, or may be included in the AI model information (may be determined based on the model).
　以上説明した第１の実施形態によれば、ＵＥは適切に性能モニタリングを実施できる。 According to the first embodiment described above, the UE can appropriately perform performance monitoring.
＜第２の実施形態＞
　第２の実施形態は、ＵＥにおけるモデル評価に関する。 <Second embodiment>
The second embodiment relates to model evaluation at the UE.
　ＵＥは、第１の実施形態で述べたモデル性能／フォールバックスキームの性能を評価し、どの性能を報告するか、どのモデル／フォールバックスキームを要求するか、どのモデル／フォールバックスキームをアクティベートするか、などの少なくとも１つを決定してもよい。 The UE evaluates the performance of the model performance/fallback scheme mentioned in the first embodiment, which performance to report, which model/fallback scheme to request, and which model/fallback scheme to activate. At least one of the following may be determined.
　ＵＥは、１つ以上のモニタされる性能について、以下の条件のうち少なくとも１つを満たすかどうかをチェック（評価）してもよい：
　・条件１：アクティブな／レジスタされる／設定されるモデル又はフォールバックスキームのモニタされる性能が、ディアクティブなモデル又はフォールバックスキームの１つのモニタされる性能より小さい／大きい、
　・条件２：レジスタされる／設定されるモデルのモニタされる性能が、フォールバックスキームの１つのモニタされる性能より大きい／小さい、
　・条件３：あるモニタされるモデル（例えば、アクティブなモデル）又はフォールバックスキームのモニタされる性能が、閾値より小さい、
　・条件４：あるモニタされるモデル（例えば、ディアクティブなモデル）又はフォールバックスキームのモニタされる性能が、閾値より大きい、
　・条件５：あるモニタされるモデル又はフォールバックスキームのモニタされる性能が、最後の性能報告（の送信）からＹ回より多く変化した、
　・条件６：あるモニタされるモデル又はフォールバックスキームのモニタされる性能が、一定期間にわたって一定回数以上閾値未満となった。 The UE may check (evaluate) whether at least one of the following conditions is met for one or more monitored performances:
Condition 1: The monitored performance of the active/registered/configured model or fallback scheme is less/greater than the monitored performance of one of the inactive models or fallback schemes.
Condition 2: The monitored performance of the model to be registered/set is greater/less than the monitored performance of one of the fallback schemes.
Condition 3: The monitored performance of a certain monitored model (e.g., active model) or fallback scheme is less than a threshold;
Condition 4: The monitored performance of a certain monitored model (e.g., a passive model) or fallback scheme is greater than a threshold;
- Condition 5: The monitored performance of a certain monitored model or fallback scheme has changed more than Y times since the last performance report (sending).
- Condition 6: The monitored performance of a certain monitored model or fallback scheme falls below a threshold a certain number of times or more over a certain period of time.
　なお、本開示において、モニタされる性能は、モニタされる性能にオフセットＸ（Ｘは例えば実数）を加えた性能と互いに読み替えられてもよい。オフセットＸは、純粋な性能（再現性能）とは別の要因（例えば、モニタされない／モニタ不要な性能）に基づいて決定されてもよい。オフセットを導入することによって、当該別の要因も包括的に考慮したモデル評価が可能である。 Note that in the present disclosure, the monitored performance may be read as the performance obtained by adding an offset X (X is, for example, a real number) to the monitored performance. Offset X may be determined based on factors other than pure performance (reproducibility performance) (eg, unmonitored/monitor-free performance). By introducing an offset, it is possible to evaluate a model that comprehensively considers other factors.
　ここで、モニタされない／モニタ不要な性能は、チャネル測定のためのオーバーヘッド、（モデル／算出される値の）信頼性、モデルの複雑さ、算出にかかる電力消費、予測時間及び測定時間の間のオフセットなどの少なくとも１つに該当してもよい。 Here, unmonitored/unmonitored performance includes the overhead for channel measurements, the reliability (of the model/calculated value), the complexity of the model, the power consumption of the calculation, and the difference between prediction time and measurement time. It may correspond to at least one of offset and the like.
　Ｘ、Ｙ、閾値などの値（又は当該値に関する情報）は、予め規格において規定されてもよいし、ＵＥ能力に基づいて決定されてもよいし、ＮＷからＵＥに通知されてもよいし、ＡＩモデル情報に含まれてもよい（モデルに基づいて決定されてもよい）。Ｘ、Ｙ、閾値などの値に関する情報は、モデル／フォールバックスキームごとに規定／通知されてもよいし、モデル／フォールバックスキームのグループごとに規定／通知されてもよいし、ＡＩベースドビーム報告又は非ＡＩベースドビーム報告のために規定／通知されてもよい。 Values such as X, Y, threshold values (or information regarding the values) may be specified in advance in the standard, may be determined based on the UE capabilities, may be notified to the UE from the NW, It may be included in the AI model information (may be determined based on the model). Information regarding values such as or may be specified/notified for non-AI based beam reporting.
　ＵＥが条件１－６のどれ（又はどの組み合わせ）をチェックするかについては、モデル／フォールバックスキームごとに規定／通知されてもよいし、モデル／のグループごとに規定／通知されてもよいし、ＡＩベースドビーム報告又は非ＡＩベースドビーム報告のために規定／通知されてもよい。 Which (or which combination) of conditions 1 to 6 the UE should check may be specified/notified for each model/fallback scheme, or may be specified/notified for each model/group. , may be defined/notified for AI-based beam reporting or non-AI-based beam reporting.
　条件６をより具体的に説明する。条件６は、例えば以下のステップを含んでもよい：
　・モニタされる性能が第１の値未満であることが第１のカウンタによって第１の回数以上カウントされると、タイマを起動する、
　・タイマが起動中、モニタされる性能が第２の値より大きいことが第２のカウンタによって第２の回数以上カウントされると、上記タイマを停止する、
　・タイマが起動中、モニタされる性能が第１の値未満である場合、第２のカウンタをリセットする、
　・モニタされる性能が第１の値より大きい場合、第１のカウンタをリセットする、
　・タイマが満了すると、モニタされるモデルの性能は低いと評価する。 Condition 6 will be explained in more detail. Condition 6 may include, for example, the following steps:
- starting a timer when the first counter counts that the monitored performance is less than the first value for a first number of times or more;
- Stop the timer when the second counter counts a second number of times or more that the monitored performance is greater than a second value while the timer is running;
- resetting the second counter if the monitored performance is less than the first value while the timer is running;
- resetting the first counter if the monitored performance is greater than the first value;
- When the timer expires, the performance of the monitored model is evaluated as poor.
　なお、第１の値は、第１の閾値（thresholdout）であってもよいし、特定のモデル／非ＡＩベースドビーム報告についての基準値（ベースライン値）から第１のオフセット（offsetout）だけ低い値であってもよい。 Note that the first value may be a first threshold value (threshold out ) or a first offset (offset out ) from a reference value (baseline value) for a specific model/non-AI based beam report. may be as low as the value.
　また、第２の値は、第２の閾値（thresholdin）であってもよいし、特定のモデル／非ＡＩベースドビーム報告についての基準値（ベースライン値）から第２のオフセット（offsetin）だけ大きいであってもよい。 The second value may also be a second threshold or a second offset from a baseline value for the particular model/non-AI based beam report. It may be just as big.
　なお、カウンタのリセットは、カウンタを特定の値（例えば、０）にすることを意味してもよい。 Note that resetting the counter may mean setting the counter to a specific value (for example, 0).
　ここで、第１／第２の閾値、ベースライン値、第１／第２のオフセット、第１／第２のカウンタ、カウンタの粒度、タイマの時間長などの値（又は当該値に関する情報）は、予め規格において規定されてもよいし、ＵＥ能力に基づいて決定されてもよいし、ＮＷからＵＥに通知されてもよいし、ＡＩモデル情報に含まれてもよい（モデルに基づいて決定されてもよい）。これらの値に関する情報は、モデル／非ＡＩベースドビーム報告ごとに規定／通知されてもよいし、モデル／非ＡＩベースドビーム報告のグループごとに規定／通知されてもよいし、ＡＩベースドビーム報告又は非ＡＩベースドビーム報告のために規定／通知されてもよい。 Here, values (or information regarding the values) such as the first/second threshold, baseline value, first/second offset, first/second counter, counter granularity, timer time length, etc. may be defined in advance in the standard, may be determined based on the UE capabilities, may be notified from the NW to the UE, or may be included in the AI model information (determined based on the model). ). Information regarding these values may be specified/notified for each model/non-AI based beam report, for each group of model/non-AI based beam reports, or for each AI-based beam report or May be specified/notified for non-AI based beam reporting.
　図７は、第２の実施形態におけるモデル評価の一例を示す図である。本例では、モニタされる性能は、最初は良いが、第１の値未満であることが第１のカウンタによって第１の回数以上カウントされると、タイマが起動される。その後、タイマが起動中にモニタされる性能が第２の値より大きいことが第２のカウンタによって何回かはカウントされたが、第２のカウンタが第２の回数以上にはならず、タイマが満了し、このモデルの性能は低いと評価される。 FIG. 7 is a diagram showing an example of model evaluation in the second embodiment. In this example, the monitored performance is initially good, but when the first counter counts that it is less than a first value a first number of times, a timer is started. Thereafter, the second counter counts a number of times that the monitored performance is greater than the second value while the timer is running, but the second counter does not exceed the second number of times, and the timer has expired, and the performance of this model is evaluated as low.
　第２の実施形態において、ＵＥは、１つ以上のモデル／フォールバックスキームの性能を評価し、報告／モデル要求／モデルアクティベート／モデルディアクティベートのための上位Ｋ（Ｋは整数）個の性能を選択（決定）してもよい。 In a second embodiment, the UE evaluates the performance of one or more models/fallback schemes and selects the top K (K is an integer) performance for reporting/model request/model activation/model deactivation. You may select (determine).
　この上記Ｋ個の性能は、全てＡＩベースドビーム報告の性能から選択されてもよいし、全て非ＡＩベースドビーム報告の性能から選択されてもよいし、ＡＩベースドビーム報告及び非ＡＩベースドビーム報告の性能から選択されてもよい。 These K performances may be selected from all of the performances of AI-based beam reports, all of them may be selected from the performances of non-AI-based beam reports, or all of the performances of AI-based beam reports and non-AI-based beam reports. It may be selected based on performance.
　言い換えると、ＵＥは、１つ以上のＣＳＩフィードバック方法の性能を評価し、ＡＩベースドビーム報告の性能から上位Ｋ（Ｋは整数）個の性能を決定し、非ＡＩベースドビーム報告の性能から上位Ｋ’（Ｋ’は整数）個の性能を決定してもよい。 In other words, the UE evaluates the performance of one or more CSI feedback methods, determines the top K (K is an integer) performance from the performance of the AI-based beam report, and determines the top K performance from the performance of the non-AI based beam report. '(K' is an integer) performances may be determined.
　Ｋ、Ｋ’などの値（又は当該値に関する情報）は、予め規格において規定されてもよいし、ＵＥ能力に基づいて決定されてもよいし、ＮＷからＵＥに通知されてもよいし、ＡＩモデル情報に含まれてもよい（モデルに基づいて決定されてもよい）。 Values such as K and K' (or information regarding the values) may be specified in advance in the standard, may be determined based on the UE capabilities, may be notified to the UE from the NW, or may be notified to the UE from the NW. It may be included in model information (may be determined based on the model).
　なお、本開示において、ＵＥは、１つ以上のモニタされる性能と、１つ以上のモニタされない／モニタ不要な性能と、に基づいて性能を導出してもよい。また、本開示において、モニタされる性能は、評価／比較の際には、ある期間にわたって平均化／重みづけされてもよい。当該期間、平均化／重みづけ手法などに関する情報は、予め規格において規定されてもよいし、ＵＥ能力に基づいて決定されてもよいし、ＮＷからＵＥに通知されてもよいし、ＡＩモデル情報に含まれてもよい（モデルに基づいて決定されてもよい）。 Note that in the present disclosure, the UE may derive performance based on one or more monitored performances and one or more unmonitored/unmonitored performances. Also, in this disclosure, monitored performance may be averaged/weighted over a period of time when evaluated/compared. Information regarding the period, averaging/weighting method, etc. may be specified in advance in the standard, may be determined based on the UE capability, may be notified from the NW to the UE, or may be based on AI model information. (may be determined based on the model).
　以上説明した第２の実施形態によれば、ＵＥは適切にモデル評価を実施できる。 According to the second embodiment described above, the UE can appropriately perform model evaluation.
＜第３の実施形態＞
　第３の実施形態は、性能報告に関する。 <Third embodiment>
The third embodiment relates to performance reporting.
［報告のタイミング］
　ＵＥは、ＮＷから通知される情報に基づいて性能報告を送信してもよい。例えば、ＵＥは、ＲＲＣ／ＭＡＣ　ＣＥ／ＤＣＩに基づいて、周期的／セミパーシステント／非周期的にスケジューリングされる上りリンクリソースにおいて、性能報告を送信してもよい。この場合、性能報告はＵＣＩに含まれてもよい。このとき、ＵＥは、報告の周期／オフセットを、ＲＲＣ／ＭＡＣ　ＣＥ／ＤＣＩに基づいて決定してもよい。 [Timing of report]
The UE may transmit a performance report based on information notified from the NW. For example, the UE may transmit performance reports in periodically/semi-persistent/aperiodically scheduled uplink resources based on RRC/MAC CE/DCI. In this case, the performance report may be included in the UCI. At this time, the UE may determine the reporting period/offset based on the RRC/MAC CE/DCI.
　ＵＥは、自身が性能報告に関するトリガを判断して、当該トリガがされた場合に性能報告を送信してもよい。例えば、ＵＥは、第２の実施形態で述べた条件（例えば、条件１－６の少なくとも１つ）が満たされる場合に、性能報告を送信してもよい。この場合、性能報告はＭＡＣ　ＣＥに含まれてもよい（ＰＵＳＣＨがスケジューリングされれば送信できるため）。 The UE may itself determine the trigger related to the performance report and transmit the performance report when the trigger is triggered. For example, the UE may transmit a performance report if the conditions described in the second embodiment (eg, at least one of conditions 1-6) are met. In this case, the performance report may be included in the MAC CE (because it can be transmitted if the PUSCH is scheduled).
　ＵＥは、新しいモデルがアクティベート／レジスタ／設定される場合に、性能報告を送信してもよい。また、ＵＥは、設定／指定されるパラメータに基づくタイマ（例えば、条件６に示したタイマ）が満了する場合に、性能報告を送信してもよい。 The UE may send performance reports when a new model is activated/registered/configured. The UE may also transmit a performance report when a timer based on the configured/specified parameters (eg, the timer shown in condition 6) expires.
［報告の内容］
　性能報告には、第１の実施形態で示した、１つ以上のモニタされる性能を示す情報が含まれてもよい。性能報告に含まれるモニタされる性能を示す情報の数は、第２の実施形態で示したＫ（／Ｋ’）に基づいて決定されてもよい。 [Contents of report]
The performance report may include information indicating one or more of the monitored performances shown in the first embodiment. The number of pieces of information indicating the monitored performance included in the performance report may be determined based on K (/K') shown in the second embodiment.
　性能報告には、報告される性能に対応するモデル／非ＡＩベースドビーム報告を示す情報（例えば、モデルＩＤ、レジスタされるモデルＩＤ、ＣＳＩレポート設定ＩＤ）が含まれてもよい。 The performance report may include information (eg, model ID, registered model ID, CSI report setting ID) indicating the model/non-AI based beam report corresponding to the reported performance.
　ＵＥは、報告される性能を、以下の少なくとも１つに基づいて決定してもよい：
　・第２の実施形態で述べた条件（例えば、条件１－６の少なくとも１つ）において評価される性能、
　・第２の実施形態で示したＫ（／Ｋ’）に基づいて選択される性能、
　・ＮＷからの通知に基づいて報告対象として決定される性能。 The UE may determine the reported performance based on at least one of the following:
- Performance evaluated under the conditions described in the second embodiment (for example, at least one of conditions 1-6),
- Performance selected based on K (/K') shown in the second embodiment,
-Performance determined to be reported based on notification from NW.
　上記通知は、例えば、モデル／非ＡＩベースドビーム報告のアクティベーションコマンドであってもよいし、報告／モニタ対象を示す情報を含む通知であってもよい。ＵＥは、アクティベート／モニタされるモデルの性能を報告してもよい。 The above notification may be, for example, an activation command for model/non-AI based beam reporting, or may be a notification including information indicating the reporting/monitoring target. The UE may report the performance of activated/monitored models.
　例えば、ＵＥがモデル＃１及び＃２を有する場合であって、これらのうちモデル＃２のみがアクティベートされる場合、ＵＥは、モデル＃２の性能を報告してもよい。ＵＥは、モデル＃１の性能は報告しなくてもよい。 For example, if the UE has models #1 and #2, and only model #2 is activated, the UE may report the performance of model #2. The UE may not report the performance of model #1.
　以上説明した第３の実施形態によれば、ＵＥは適切に性能報告を送信できる。 According to the third embodiment described above, the UE can appropriately transmit a performance report.
＜第４の実施形態＞
　第４の実施形態は、モデル要求に関する。 <Fourth embodiment>
The fourth embodiment relates to model requests.
［モデル要求の送信タイミング］
　モデル要求の送信タイミングは、第３の実施形態の性能報告のタイミングの説明において、性能報告をモデル要求で読み替えた内容に基づいて判断されてもよい。 [Model request transmission timing]
The transmission timing of the model request may be determined based on the performance report replaced with the model request in the description of the timing of the performance report in the third embodiment.
［モデル要求の内容］
　モデル要求には、適用されるべきモデル／非ＡＩベースドビーム報告を示す情報（例えば、モデルＩＤ、レジスタされるモデルＩＤ、ＣＳＩレポート設定ＩＤ）が含まれてもよい。 [Contents of model request]
The model request may include information indicating the model/non-AI based beam report to be applied (eg, model ID, registered model ID, CSI report configuration ID).
　適用されるべきモデル／非ＡＩベースドビーム報告は、第３の実施形態において説明した報告される性能に対応するモデル／非ＡＩベースドビーム報告に該当してもよい（なお、性能は報告されなくてもよい）。適用されるべきモデル／非ＡＩベースドビーム報告は、推奨される（recommended）モデル／非ＡＩベースドビーム報告と呼ばれてもよい。 The model/non-AI based beam report to be applied may correspond to the model/non-AI based beam report corresponding to the reported performance described in the third embodiment (note that the performance may not be reported). good). The model/non-AI based beam report to be applied may be referred to as the recommended model/non-AI based beam report.
　例えば、ＵＥがモデル＃１及び＃２を有し、非ＡＩベースドビーム報告が利用できる場合であって、これらのうちモデル＃２のみがアクティベートされている場合を考える。ＵＥは、これらのモデル／非ＡＩベースドビーム報告の性能を評価した結果、非ＡＩベースドビーム報告を、推奨されるモデル／非ＡＩベースドビーム報告として選択してもよい。ＵＥは、非ＡＩベースドビーム報告を示すモデル要求をＮＷに報告してもよい。 For example, consider a case where the UE has models #1 and #2 and non-AI based beam reporting is available, and only model #2 of these is activated. The UE may select a non-AI based beam report as the recommended model/non-AI based beam report after evaluating the performance of these model/non-AI based beam reports. The UE may report a model request to the NW indicating non-AI based beam reporting.
　なお、ＵＥは、性能報告に含まれる情報及びモデル要求に含まれる情報を、同時に（例えば、１つのＵＣＩ／ＭＡＣ　ＣＥを用いて）送信してもよい。 Note that the UE may transmit the information included in the performance report and the information included in the model request at the same time (for example, using one UCI/MAC CE).
　以上説明した第４の実施形態によれば、ＵＥは適切にモデル要求を送信できる。 According to the fourth embodiment described above, the UE can appropriately transmit a model request.
＜第５の実施形態＞
　第５の実施形態は、モデルアクティベーション／ディアクティベーションに関する。 <Fifth embodiment>
The fifth embodiment relates to model activation/deactivation.
　ＵＥは、ＮＷから通知される情報に基づいてモデルアクティベーション／ディアクティベーションを実施してもよい。当該情報は、モデルアクティベーション／ディアクティベーションコマンドと呼ばれてもよく、ＲＲＣ／ＭＡＣ　ＣＥ／ＤＣＩを用いて送信されてもよい。 The UE may perform model activation/deactivation based on information notified from the NW. This information may be called a model activation/deactivation command and may be transmitted using RRC/MAC CE/DCI.
　モデルアクティベーション／ディアクティベーションコマンドには、アクティベート／ディアクティベートされるモデル／非ＡＩベースドビーム報告を示す情報（例えば、モデルＩＤ、レジスタされるモデルＩＤ、ＣＳＩレポート設定ＩＤ）が含まれてもよい。 The model activation/deactivation command may include information indicating the model/non-AI based beam report to be activated/deactivated (e.g., model ID, registered model ID, CSI report configuration ID). .
　モデルアクティベーション／ディアクティベーションコマンドは、第４の実施形態で述べたモデル要求を受け入れたか否かを示す情報（例えば、モデル応答と呼ばれてもよい）に該当してもよい。ＵＥは、モデル要求を送信し、対応するモデル応答を受信すると、当該モデル応答がモデル要求を受け入れたことを示す場合にはモデル要求によって示されるモデルをアクティベートし、そうでない場合には当該モデルをディアクティベートしてもよい。 The model activation/deactivation command may correspond to information indicating whether or not the model request described in the fourth embodiment has been accepted (for example, it may be called a model response). Upon sending a model request and receiving a corresponding model response, the UE activates the model indicated by the model request if the model response indicates acceptance of the model request, otherwise activates the model. May be deactivated.
　ＵＥは、自分自身でアクティベート／ディアクティベートするモデル／非ＡＩベースドビーム報告を判断してもよい。例えば、ＵＥは、第２の実施形態で述べた条件（例えば、条件１－６の少なくとも１つ）が満たされる場合に、アクティベート／ディアクティベートするモデル／非ＡＩベースドビーム報告を判断してもよい。アクティベート／ディアクティベートするモデル／非ＡＩベースドビーム報告は、第３の実施形態において説明した報告される性能に対応するモデル／非ＡＩベースドビーム報告に該当してもよい（なお、性能は報告されなくてもよい）。 The UE may decide to activate/deactivate model/non-AI based beam reporting on its own. For example, the UE may determine which model/non-AI based beam report to activate/deactivate if the conditions mentioned in the second embodiment (e.g., at least one of conditions 1-6) are met. . The model/non-AI based beam report to activate/deactivate may correspond to the model/non-AI based beam report corresponding to the reported performance described in the third embodiment (note that the performance is not reported). ).
　ＵＥは、アクティベートされるモデルに関する情報又はアクティブなモデルが変更されることを示す情報を、ＮＷに報告してもよい。この場合、モデル要求／性能報告が不要になるため、通信オーバーヘッドの低減が期待できる。 The UE may report information regarding the activated model or information indicating that the active model is changed to the NW. In this case, model requests/performance reports are no longer necessary, so a reduction in communication overhead can be expected.
　なお、ＵＥは、ある機能のためのモデル／非ＡＩベースドビーム報告は１つだけアクティブであることを予期してもよいし、複数アクティブであることを予期してもよい。 Note that the UE may expect only one model/non-AI based beam report for a certain function to be active, or may expect multiple model/non-AI based beam reports to be active.
　あるモデルがアクティベートされる（アクティブである）場合、ＵＥは、当該モデルを予測Ｌ１－ＲＳＲＰの計算に適用してもよい。また、あるモデルがアクティベートされる場合、ＵＥは、性能モニタリング用途でない設定された非ＡＩベースドビーム報告に基づくＬ１－ＲＳＲＰ計算／報告を行わなくてもよい。 If a model is activated, the UE may apply the model to calculate the predicted L1-RSRP. Also, if a model is activated, the UE may not perform L1-RSRP calculation/reporting based on configured non-AI based beam reporting that is not for performance monitoring purposes.
　モデルが全てアクティブでない場合、ＵＥは、非ＡＩベースドビーム報告スキームをＬ１－ＲＳＲＰの計算に適用してもよい。ＵＥは、モデルが全てアクティブでない場合に適用される非ＡＩベースドビーム報告スキームに関する情報を設定されてもよい（例えば、ＲＲＣのＣＳＩレポート設定情報要素を用いて）。 If all models are not active, the UE may apply a non-AI based beam reporting scheme to the L1-RSRP calculation. The UE may be configured with information regarding the non-AI based beam reporting scheme to be applied if all models are not active (eg, using the RRC CSI report configuration information element).
　また、モデルが全てアクティブでない場合、ＵＥは、ビーム報告を行わなくてもよい。 Also, if all models are not active, the UE does not need to perform beam reporting.
［モデル適用時間（アクティブ／ディアクティブ時間）］
　ＵＥは、モデル適用時間の間、あるモデルをアクティベート／ディアクティベートしてもよい。 [Model application time (active/deactive time)]
The UE may activate/deactivate a model during the model application time.
　モデル適用時間は、開始時間（開始ポイント）から終了時間（終了ポイント）までの期間に該当してもよい。 The model application time may correspond to the period from the start time (start point) to the end time (end point).
　開始時間は、以下の少なくとも１つに該当してもよい：
　・モデルアクティベーション／ディアクティベーションコマンドを受信する最終シンボル又は当該最終シンボルからＸ単位時間後、
　・モデルアクティベーション／ディアクティベーションコマンドに対応するＨＡＲＱ情報（例えば、ＨＡＲＱ－ＡＣＫ）を送信する最終シンボル又は当該最終シンボルからＸ単位時間後。 The start time may be at least one of the following:
・The last symbol for which a model activation/deactivation command is received or X units of time after the last symbol,
- The last symbol that transmits HARQ information (eg, HARQ-ACK) corresponding to the model activation/deactivation command or X units of time after the last symbol.
　本開示において、単位時間は、シンボル、スロット、サブスロット、サブフレーム、秒（ミリ秒）などの少なくとも１つで読み替えられてもよい。 In the present disclosure, unit time may be read as at least one of a symbol, slot, subslot, subframe, second (millisecond), etc.
　終了時間は、以下の少なくとも１つに該当してもよい：
　・対応する開始時間からＹ単位時間後、
　・対応するモデルアクティベーション／ディアクティベーションコマンドが適用／受信されてからＹ単位時間後、
　・新しいモデルアクティベーション／ディアクティベーションコマンドが適用／受信されるまで。 The end time may be at least one of the following:
・Y units of time after the corresponding start time,
Y units of time after the corresponding model activation/deactivation command is applied/received,
- Until a new model activation/deactivation command is applied/received.
　Ｘ、Ｙなどの値（又は当該値に関する情報）は、予め規格において規定されてもよいし、ＵＥ能力に基づいて決定されてもよいし、ＮＷからＵＥに通知されてもよいし、モデルに紐づいた情報でもよい（モデルに基づいて決定されてもよい）。モデルアクティベーション／ディアクティベーションコマンドは、モデル適用時間に関する情報（例えば、Ｘ／Ｙを示す情報）を含んでもよい。 Values such as X and Y (or information regarding the values) may be specified in advance in the standard, may be determined based on the UE capabilities, may be notified to the UE from the NW, or may be specified in the model. It may be linked information (may be determined based on a model). The model activation/deactivation command may include information regarding model application time (eg, information indicating X/Y).
　なお、ＵＥは、モデル適用時間中に新しいモデルアクティベーション／ディアクティベーションコマンドを受信する場合に、当該モデル適用時間の開始時間を再度開始してもよい（新しいモデルアクティベーション／ディアクティベーションコマンドに基づく上述の開始時間に更新してもよい）。モデル適用時間の開始時間を再度開始するのは、以下の少なくとも１つの場合であってもよい：
　・上記新しいモデルアクティベーション／ディアクティベーションコマンドによって指定されるモデル情報が、適用中のモデルと同じ場合、
　・上記新しいモデルアクティベーション／ディアクティベーションコマンドがモデル情報／適用時間に関する情報を含まない場合。 Note that when the UE receives a new model activation/deactivation command during the model application time, the UE may restart the start time of the model application time (in response to the new model activation/deactivation command). (may be updated to the above start time based on the start time). The start time of the model application time may be restarted in at least one of the following cases:
・If the model information specified by the above new model activation/deactivation command is the same as the model being applied,
- If the above new model activation/deactivation command does not include information regarding model information/application time.
　なお、ＵＥは、アクティベーションの場合とディアクティベーションの場合とで、開始時間／終了時間の決定（又は更新）の方法を異ならせてもよい。 Note that the UE may use different methods for determining (or updating) the start time/end time in the case of activation and the case of deactivation.
　また、ＵＥは、モデルアクティベーション／ディアクティベーションの場合と、フォールバックスキームアクティベーション／ディアクティベーションの場合とで、開始時間／終了時間の決定（又は更新）の方法を異ならせてもよい。 Furthermore, the UE may use different methods for determining (or updating) the start time/end time in the case of model activation/deactivation and in the case of fallback scheme activation/deactivation.
　以上説明した第５の実施形態によれば、ＵＥは適切にモデルアクティベーション／ディアクティベーションを制御できる。 According to the fifth embodiment described above, the UE can appropriately control model activation/deactivation.
［第５の実施形態の変形例（モニタ対象のＡＩモデルの判断）］
　ＵＥは、モニタ対象のＡＩモデルを、第５の実施形態のモデルアクティベーション／ディアクティベーションの説明において、アクティベート（アクティベーション）／ディアクティベート（ディアクティベーション）を、モニタのアクティベート（アクティベーション）／ディアクティベート（ディアクティベーション）で読み替えた内容に基づいて判断してもよい。 [Modification of fifth embodiment (determination of AI model to be monitored)]
In the explanation of model activation/deactivation in the fifth embodiment, the UE uses the AI model to be monitored as activation (activation)/deactivation (deactivation) and monitor activation (activation)/deactivation. The determination may be made based on the content replaced by deactivation.
　ＵＥは、設定されるＡＩモデルに関して以下の少なくとも１つが満たされる場合にのみ、当該ＡＩモデルをモニタしてもよい：
　・当該モデルのアクティベーションを受信する、
　・当該モデルのアクティベーションを決定する、
　・当該モデルのモデル適用期間中、
　・当該モデルの性能が決定されるまで（言い換えると、まだ性能が決定されていない）、
　・当該モデルの性能が報告されるまで（言い換えると、まだ性能が報告されていない）、
　・当該モデルのモニタのアクティベーションコマンド（モニタのトリガリング信号）を受信してから一定期間中。 The UE may monitor the configured AI model only if at least one of the following is satisfied regarding the configured AI model:
・Receive activation for the model;
・Determine the activation of the model;
・During the application period of the model,
・Until the performance of the model is determined (in other words, the performance has not yet been determined),
・Until the performance of the model is reported (in other words, the performance has not been reported yet).
・During a certain period of time after receiving the activation command (monitor triggering signal) for the monitor of the relevant model.
　上記一定期間は、上記モデル適用期間をモデルのモニタ適用期間に読み替えた期間に該当してもよい。つまり、モデルアクティベーション／ディアクティベーションと、モデルのモニタのアクティベーション／ディアクティベーションは、別々に制御されてもよいし、同時に制御されてもよい。 The above-mentioned certain period may correspond to a period in which the above-mentioned model application period is read as a model monitoring application period. That is, model activation/deactivation and model monitor activation/deactivation may be controlled separately or simultaneously.
　以上説明した第５の実施形態の変形例によれば、ＵＥは適切にモデルのモニタを制御できる。 According to the modification of the fifth embodiment described above, the UE can appropriately control the model monitor.
＜第６の実施形態＞
　第６の実施形態は、ＵＥからの性能モニタリング向けの報告と、ＢＳにおける性能モニタリングに関する。 <Sixth embodiment>
The sixth embodiment relates to reporting for performance monitoring from the UE and performance monitoring at the BS.
［性能モニタリング向けの報告］
　ＵＥは、性能モニタリング（向けの報告）のためのＣＳＩ－ＲＳリソース／ＣＳＩ－ＲＳリソースセット／ＣＳＩリソース設定／ＣＳＩレポート設定を通知されてもよい。 [Report for performance monitoring]
The UE may be informed of CSI-RS resources/CSI-RS resource sets/CSI resource configurations/CSI report configurations for performance monitoring (reporting).
　ＵＥは、ＢＳにおけるあるモデルについての性能モニタリングのために、以下を報告してもよい：
　・当該モデルによって予測されるＬ１－ＲＳＲＰ、
　・対応する性能モニタリングリファレンスについての測定されるＬ１－ＲＳＲＰ、
　・性能モニタリングのためのＣＲＩ／ＳＳＢＲＩ。 The UE may report the following for performance monitoring for certain models at the BS:
・L1-RSRP predicted by the model,
- measured L1-RSRP for the corresponding performance monitoring reference;
- CRI/SSBRI for performance monitoring.
　報告されるＬ１－ＲＳＲＰは、以下の少なくとも１つに基づいて選択（決定）されてもよい：
　・報告することを設定／指定された全ての予測Ｌ１－ＲＳＲＰ、
　・性能モニタリングリファレンスのために参照されるＣＳＩ－ＲＳ機会よりＸ単位時間遅くなく、又はＸ単位時間早くない（言い換えると、当該ＣＳＩ－ＲＳ機会を基準としてＸ単位時間の範囲にある）予測ＣＳＩ時間に対応する予測Ｌ１－ＲＳＲＰ。 The L1-RSRP to be reported may be selected (determined) based on at least one of the following:
- All predicted L1-RSRPs configured/designated to be reported;
A predicted CSI time that is not later than or earlier than the CSI-RS opportunity referenced for performance monitoring reference (in other words, within X time units relative to the CSI-RS opportunity) The predicted L1-RSRP corresponding to .
　後者は、第０の実施形態の方法２－３と同様の基準での判断に該当してもよい。 The latter may correspond to judgment based on the same criteria as method 2-3 of the zeroth embodiment.
　報告されるＣＲＩ／ＳＳＢＲＩは、予測Ｌ１－ＲＳＲＰについてと、性能モニタリングリファレンス（についてのＬ１－ＲＳＲＰ）と、で独立に決定されてもよいし、共通に決定されてもよい。後者の場合、ＵＥは報告されるＣＲＩ／ＳＳＢＲＩを、予測Ｌ１－ＲＳＲＰに基づいて決定してもよいし、性能モニタリングリファレンスに基づいて決定してもよい。 The reported CRI/SSBRI may be determined independently or jointly for the predicted L1-RSRP and the performance monitoring reference (L1-RSRP). In the latter case, the UE may determine the reported CRI/SSBRI based on the predicted L1-RSRP or based on the performance monitoring reference.
［性能モニタリング向けの報告に含まれるＣＳＩレポート］
　ＵＥは、性能モニタリング向けの報告において、予測Ｌ１－ＲＳＲＰと、対応する性能モニタリングリファレンスについてのＬ１－ＲＳＲＰと、を異なるＣＳＩレポートを用いて報告してもよいし、同じＣＳＩレポートを用いて報告してもよい。同じＣＳＩレポートを用いて報告される場合、予測Ｌ１－ＲＳＲＰと、対応する性能モニタリングリファレンスについてのＬ１－ＲＳＲＰと、の一方は、他方からの差分値（差分ＲＳＲＰ）として表現されてもよい。 [CSI report included in report for performance monitoring]
In reporting for performance monitoring, the UE may report the predicted L1-RSRP and the L1-RSRP for the corresponding performance monitoring reference using different CSI reports or using the same CSI report. It's okay. When reported using the same CSI report, one of the predicted L1-RSRP and the L1-RSRP for the corresponding performance monitoring reference may be expressed as a difference value (differential RSRP) from the other.
　図８は、第６の実施形態にかかるＣＳＩレポートの一例を示す図である。本例では、予測Ｌ１－ＲＳＲＰのうち最高値が＃１として絶対値で表現され、その他の予測Ｌ１－ＲＳＲＰは当該最高値からの差分で表現される。また、性能モニタリングリファレンスについてのＬ１－ＲＳＲＰも、当該最高値からの差分で表現される。 FIG. 8 is a diagram showing an example of a CSI report according to the sixth embodiment. In this example, the highest value among the predicted L1-RSRPs is expressed as #1 as an absolute value, and the other predicted L1-RSRPs are expressed as differences from the highest value. Furthermore, the L1-RSRP for the performance monitoring reference is also expressed as a difference from the maximum value.
［性能モニタリング向けの報告についてのＬ１－ＲＳＲＰの粒度］
　ＵＥは、性能モニタリング向けの報告についてのＬ１－ＲＳＲＰの粒度として、既存の規格の定義とは異なる（例えば、より細かい、より多い、拡張された）粒度を用いてもよい。 [L1-RSRP granularity for reporting for performance monitoring]
The UE may use a different (eg, finer, more, expanded) granularity for L1-RSRP reporting for performance monitoring than the existing standard definition.
　ＵＥは、以下の少なくとも１つのパラメータについて、既存の規格とは異なる粒度を用いてもよい：
　・Ｌ１－ＲＳＲＰのためのステップサイズ（例えば、ｄＢ単位）、
　・表現できるＬ１－ＲＳＲＰの範囲（最小値ｄＢｍ、最大値ｄＢｍ）、
　・１つのレポートにおいて報告できる数（例えば、nrofReportedRS）。 The UE may use different granularity than existing standards for at least one of the following parameters:
- Step size for L1-RSRP (e.g. in dB),
・The range of L1-RSRP that can be expressed (minimum value dBm, maximum value dBm),
- Number that can be reported in one report (eg nrofReportedRS).
　ＵＥは、ビーム予測のためのいくつかのＡＩモデルがＵＥにレジスタ／設定されている場合にのみ、上記異なる粒度を適用してもよい。 The UE may apply the different granularity above only if some AI models for beam prediction are registered/configured in the UE.
　以上説明した第６の実施形態によれば、ＢＳは適切に性能モニタリングを実施できる。 According to the sixth embodiment described above, the BS can appropriately perform performance monitoring.
＜補足＞
［ＡＩモデル情報］
　本開示において、ＡＩモデル情報は、以下の少なくとも１つを含む情報を意味してもよい：
　・ＡＩモデルの入力／出力の情報、
　・ＡＩモデルの入力／出力のための前処理／後処理の情報、
　・ＡＩモデルのパラメータの情報、
　・ＡＩモデルのための訓練情報（トレーニング情報）、
　・ＡＩモデルのための推論情報、
　・ＡＩモデルに関する性能情報。 <Supplement>
[AI model information]
In this disclosure, AI model information may mean information including at least one of the following:
・AI model input/output information,
・Pre-processing/post-processing information for AI model input/output,
・Information on AI model parameters,
・Training information for AI models (training information),
・Inference information for AI models,
・Performance information regarding AI models.
　ここで、上記ＡＩモデルの入力／出力の情報は、以下の少なくとも１つに関する情報を含んでもよい：
　・入力／出力データの内容（例えば、ＲＳＲＰ、ＳＩＮＲ、チャネル行列（又はプリコーディング行列）における振幅／位相情報、到来角度（Angle　of　Arrival（ＡｏＡ））に関する情報、放射角度（Angle　of　Departure（ＡｏＤ））に関する情報、位置情報）、
　・データの補助情報（メタ情報と呼ばれてもよい）、
　・入力／出力データのタイプ（例えば、不変値（immutable　value）、浮動小数点数）、
　・入力／出力データのビット幅（例えば、各入力値について６４ビット）、
　・入力／出力データの量子化間隔（量子化ステップサイズ）（例えば、Ｌ１－ＲＳＲＰについて、１ｄＢｍ）、
　・入力／出力データが取り得る範囲（例えば、［０、１］）。 Here, the input/output information of the AI model may include information regarding at least one of the following:
- Contents of input/output data (e.g. RSRP, SINR, amplitude/phase information in channel matrix (or precoding matrix), information on angle of arrival (AoA), angle of departure (AoD)) ), location information),
・Data auxiliary information (may be called meta information),
- type of input/output data (e.g. immutable value, floating point number),
- bit width of input/output data (e.g. 64 bits for each input value),
- Quantization interval (quantization step size) of input/output data (for example, 1 dBm for L1-RSRP),
- The range that input/output data can take (for example, [0, 1]).
　なお、本開示において、ＡｏＡに関する情報は、到来方位角度（azimuth　angle　of　arrival）及び到来天頂角度（zenith　angle　of　arrival（ＺｏＡ））の少なくとも１つに関する情報を含んでもよい。また、ＡｏＤに関する情報は、例えば、放射方位角度（azimuth　angle　of　departure）及び放射天頂角度（zenith　angle　of　depature（ＺｏＤ））の少なくとも１つに関する情報を含んでもよい。 Note that in the present disclosure, the information regarding AoA may include information regarding at least one of the azimuth angle of arrival and the zenith angle of arrival (ZoA). Further, the information regarding the AoD may include, for example, information regarding at least one of a radial azimuth angle of departure and a radial zenith angle of depth (ZoD).
　本開示において、位置情報は、ＵＥ／ＮＷに関する位置情報であってもよい。位置情報は、測位システム（例えば、衛星測位システム（Global　Navigation　Satellite　System（ＧＮＳＳ）、Global　Positioning　System（ＧＰＳ）など））を用いて得られる情報（例えば、緯度、経度、高度）、当該ＵＥに隣接する（又はサービング中の）ＢＳの情報（例えば、ＢＳ／セルの識別子（Identifier（ＩＤ））、ＢＳ－ＵＥ間の距離、ＵＥ（ＢＳ）から見たＢＳ（ＵＥ）の方向／角度、ＵＥ（ＢＳ）から見たＢＳ（ＵＥ）の座標（例えば、Ｘ／Ｙ／Ｚ軸の座標）など）、ＵＥの特定のアドレス（例えば、Internet　Protocol（ＩＰ）アドレス）などの少なくとも１つを含んでもよい。ＵＥの位置情報は、ＢＳの位置を基準とする情報に限られず、特定のポイントを基準とする情報であってもよい。 In the present disclosure, the location information may be location information regarding the UE/NW. Location information includes information (e.g., latitude, longitude, altitude) obtained using a positioning system (e.g., Global Navigation Satellite System (GNSS), Global Positioning System (GPS), etc.), and information (e.g., latitude, longitude, altitude) adjacent to the UE. Information on the serving (or serving) BS (e.g., BS/cell identifier (ID), BS-UE distance, direction/angle of the BS (UE) as seen from the UE (BS), The information may include at least one of the coordinates of the BS (UE) as seen from the BS (e.g., X/Y/Z axis coordinates, etc.), the specific address of the UE (e.g., Internet Protocol (IP) address), etc. . The location information of the UE is not limited to information based on the location of the BS, but may be information based on a specific point.
　位置情報は、自身の実装に関する情報（例えば、アンテナの位置（location/position）／向き、アンテナパネルの位置／向き、アンテナの数、アンテナパネルの数など）を含んでもよい。 The location information may include information regarding its own implementation (for example, location/position/orientation of antennas, location/orientation of antenna panels, number of antennas, number of antenna panels, etc.).
　位置情報は、モビリティ情報を含んでもよい。モビリティ情報は、モビリティタイプを示す情報、ＵＥの移動速度、ＵＥの加速度、ＵＥの移動方向などの少なくとも１つを示す情報を含んでもよい。 The location information may include mobility information. The mobility information may include information indicating at least one of the mobility type, the moving speed of the UE, the acceleration of the UE, the moving direction of the UE, and the like.
　ここで、モビリティタイプは、固定位置ＵＥ（fixed　location　UE）、移動可能／移動中ＵＥ（movable/moving　UE）、モビリティ無しＵＥ（no　mobility　UE）、低モビリティＵＥ（low　mobility　UE）、中モビリティＵＥ（middle　mobility　UE）、高モビリティＵＥ（high　mobility　UE）、セル端ＵＥ（cell-edge　UE）、非セル端ＵＥ（not-cell-edge　UE）などの少なくとも１つに該当してもよい。 Here, the mobility types are fixed location UE, movable/moving UE, no mobility UE, low mobility UE, and medium mobility UE. (middle mobility UE), high mobility UE (high mobility UE), cell-edge UE (cell-edge UE), and non-cell-edge UE (not-cell-edge UE).
　本開示において、（データのための）環境情報は、データが取得される／利用される環境に関する情報であってもよく、例えば、周波数情報（バンドＩＤなど）、環境タイプ情報（屋内（indoor）、屋外（outdoor）、Urban　Macro（UMa）、Urban　Micro（Umi）などの少なくとも１つを示す情報）、Line　Of　Site（ＬＯＳ）／Non-Line　Of　Site（ＮＬＯＳ）を示す情報などに該当してもよい。 In this disclosure, environmental information (for data) may be information regarding the environment in which the data is acquired/used, such as frequency information (band ID, etc.), environment type information (indoor, etc.). , outdoor, Urban Macro (UMa), Urban Micro (Umi), etc.), Line Of Site (LOS)/Non-Line Of Site (NLOS), etc. Good too.
　ここで、ＬＯＳは、ＵＥ及びＢＳが互いに見通せる環境にある（又は遮蔽物がない）ことを意味してもよく、ＮＬＯＳは、ＵＥ及びＢＳが互いに見通せる環境にない（又は遮蔽物がある）ことを意味してもよい。ＬＯＳ／ＮＬＯＳを示す情報は、ソフト値（例えば、ＬＯＳ／ＮＬＯＳの確率）を示してもよいし、ハード値（例えば、ＬＯＳ／ＮＬＯＳのいずれか）を示してもよい。 Here, LOS may mean that the UE and BS are in an environment where they can see each other (or there is no shielding), and NLOS may mean that the UE and BS are not in an environment where they can see each other (or there is a shield). It can also mean The information indicating LOS/NLOS may indicate a soft value (for example, probability of LOS/NLOS) or may indicate a hard value (for example, either LOS/NLOS).
　本開示において、メタ情報は、例えば、ＡＩモデルに適した入力／出力情報に関する情報、取得した／取得できるデータに関する情報などを意味してもよい。メタ情報は、具体的には、ＲＳ（例えば、ＣＳＩ－ＲＳ／ＳＲＳ／ＳＳＢなど）のビームに関する情報（例えば、各ビームの指向している角度、３ｄＢビーム幅、指向しているビームの形状、ビームの数）、ｇＮＢ／ＵＥのアンテナのレイアウト情報、周波数情報、環境情報、メタ情報ＩＤなどを含んでもよい。なお、メタ情報は、ＡＩモデルの入力／出力として用いられてもよい。 In the present disclosure, meta information may mean, for example, information regarding input/output information suitable for an AI model, information regarding acquired/obtainable data, etc. Specifically, the meta information includes information regarding beams of RS (for example, CSI-RS/SRS/SSB, etc.) (for example, the pointing angle of each beam, 3 dB beam width, the shape of the pointing beam, (number of beams), gNB/UE antenna layout information, frequency information, environment information, meta information ID, etc. Note that the meta information may be used as input/output of the AI model.
　上記ＡＩモデルの入力／出力のための前処理／後処理の情報は、以下の少なくとも１つに関する情報を含んでもよい：
　・正規化（例えば、Ｚスコア正規化（標準化）、最小－最大（min-max）正規化）を適用するか否か、
　・正規化のためのパラメータ（例えば、Ｚスコア正規化については平均／分散、最小－最大正規化については最小値／最大値）、
　・特定の数値変換方法（例えば、ワンホットエンコーディング（one　hot　encoding）、ラベルエンコーディング（label　encoding）など）を適用するか否か、
　・訓練データとして用いられるか否かの選択ルール。 The pre-processing/post-processing information for the input/output of the AI model may include information regarding at least one of the following:
- whether to apply normalization (e.g., Z-score normalization, min-max normalization);
- Parameters for normalization (e.g. mean/variance for Z-score normalization, minimum/maximum for min-max normalization),
- Whether to apply a specific numerical conversion method (e.g. one hot encoding, label encoding, etc.);
- Selection rules for whether or not to be used as training data.
　例えば、入力情報ｘに対して前処理としてＺスコア正規化（ｘｎｅｗ＝（ｘ－μ）／σ。ここで、μはｘの平均、σは標準偏差）を行った正規化済み入力情報ｘｎｅｗをＡＩモデルに入力してもよく、ＡＩモデルからの出力ｙｏｕｔに後処理を掛けて最終的な出力ｙが得られてもよい。 For example, normalized input information x that has been subjected to Z-score normalization (x new = (x - μ)/σ, where μ is the average of x and σ is the standard deviation) as a preprocessing for input information x. new may be input into the AI model, and the output y out from the AI model may be post-processed to obtain the final output y.
　上記ＡＩモデルのパラメータの情報は、以下の少なくとも１つに関する情報を含んでもよい：
　・ＡＩモデルにおける重み（例えば、ニューロンの係数（結合係数））情報、
　・ＡＩモデルの構造（structure）、
　・モデルコンポーネントとしてのＡＩモデルのタイプ（例えば、Residual　Network（ResNet）、DenseNet、RefineNet、トランスフォーマー（Transformer）モデル、CRBlock、回帰型ニューラルネットワーク（Recurrent　Neural　Network（ＲＮＮ））、長・短期記憶（Long　Short-Term　Memory（ＬＳＴＭ））、ゲート付き回帰型ユニット（Gated　Recurrent　Unit（ＧＲＵ）））,
　・モデルコンポーネントとしてのＡＩモデルの機能（例えば、デコーダ、エンコーダ）。 The information on the parameters of the AI model may include information regarding at least one of the following:
・Weight (e.g. neuron coefficient (coupling coefficient)) information in the AI model,
・Structure of the AI model,
・Type of AI model as model component (e.g. ResNet, DenseNet, RefineNet, Transformer model, CRBlock, Recurrent Neural Network (RNN), Long Short Memory -Term Memory (LSTM)), Gated Recurrent Unit (GRU)),
- Functions of the AI model as a model component (e.g. decoder, encoder).
　なお、上記ＡＩモデルにおける重み情報は、以下の少なくとも１つに関する情報を含んでもよい：
　・重み情報のビット幅（サイズ）、
　・重み情報の量子化間隔、
　・重み情報の粒度、
　・重み情報が取り得る範囲、
　・ＡＩモデルにおける重みのパラメータ、
　・更新前のＡＩモデルからの差分の情報（更新する場合）、
　・重み初期化（weight　initialization）の方法（例えば、ゼロ初期化、ランダム初期化（正規分布／一様分布／切断正規分布に基づく）、Xavier初期化（シグモイド関数向け）、He初期化（整流化線形ユニット（Rectified　Linear　Units（ＲｅＬＵ））向け））。 Note that the weight information in the AI model may include information regarding at least one of the following:
・Bit width (size) of weight information,
・Quantization interval of weight information,
- Granularity of weight information,
・The range that weight information can take,
・Weight parameters in the AI model,
・Difference information from the AI model before update (if updating),
・Weight initialization methods (e.g. zero initialization, random initialization (based on normal distribution/uniform distribution/truncated normal distribution), Xavier initialization (for sigmoid functions), He initialization (rectified) For Rectified Linear Units (ReLU)).
　また、上記ＡＩモデルの構造は、以下の少なくとも１つに関する情報を含んでもよい：
　・レイヤ数、
　・レイヤのタイプ（例えば、畳み込み層、活性化層、デンス（dense）層、正規化層、プーリング層、アテンション層）、
　・レイヤ情報、
　・時系列特有のパラメータ（例えば、双方向性、時間ステップ）、
　・訓練のためのパラメータ（例えば、機能のタイプ（Ｌ２正則化、ドロップアウト機能など）、どこに（例えば、どのレイヤの後に）この機能を置くか）。 Further, the structure of the AI model may include information regarding at least one of the following:
・Number of layers,
- Type of layer (e.g. convolution layer, activation layer, dense layer, normalization layer, pooling layer, attention layer),
・Layer information,
- Time series specific parameters (e.g. bidirectionality, time step),
- Parameters for training (e.g. type of function (L2 regularization, dropout function, etc.), where to put this function (e.g. after which layer)).
　上記レイヤ情報は、以下の少なくとも１つに関する情報を含んでもよい：
　・各レイヤにおけるニューロン数、
　・カーネルサイズ、
　・プーリング層／畳み込み層のためのストライド、
　・プーリング方法（MaxPooling、AveragePoolingなど）、
　・残差ブロックの情報、
　・ヘッド（head）数、
　・正規化方法（バッチ正規化、インスタンス正規化、レイヤ正規化など）、
　・活性化関数（シグモイド、tanh関数、ＲｅＬＵ、リーキーＲｅＬＵの情報、Maxout、Softmax）。 The layer information may include information regarding at least one of the following:
・Number of neurons in each layer,
・Kernel size,
・Stride for pooling layer/convolution layer,
・Pooling method (MaxPooling, AveragePooling, etc.),
・Residual block information,
・Number of heads,
・Normalization methods (batch normalization, instance normalization, layer normalization, etc.),
- Activation function (sigmoid, tanh function, ReLU, leaky ReLU information, Maxout, Softmax).
　あるＡＩモデルは、別のＡＩモデルのコンポーネントとして含まれてもよい。例えば、あるＡＩモデルは、モデルコンポーネント＃１であるＲｅｓＮｅｔ、モデルコンポーネント＃２であるトランスフォーマーモデル、デンス層及び正規化層の順に処理が進むＡＩモデルであってもよい。 One AI model may be included as a component of another AI model. For example, an AI model may be an AI model in which processing proceeds in the following order: ResNet as model component #1, a transformer model as model component #2, a dense layer, and a normalization layer.
　上記ＡＩモデルのための訓練情報は、以下の少なくとも１つに関する情報を含んでもよい：
　・最適化アルゴリズムのための情報（例えば、最適化の種類（確率的勾配降下法（Stochastic　Gradient　Descent（ＳＧＤ）））、AdaGrad、Adamなど）、最適化のパラメータ（学習率（learning　rate）、モメンタム情報など）、
　・損失関数の情報（例えば、損失関数の指標（metrics）に関する情報（平均絶対誤差（Mean　Absolute　Error（ＭＡＥ））、平均二乗誤差（Mean　Square　Error（ＭＳＥ））、クロスエントロピーロス、NLLLoss、Kullback-Leibler（ＫＬ）ダイバージェンスなど））、
　・訓練用に凍結されるべきパラメータ（例えば、レイヤ、重み）、
　・更新されるべきパラメータ（例えば、レイヤ、重み）、
　・訓練用の初期パラメータであるべき（初期パラメータとして用いられるべき）パラメータ（例えば、レイヤ、重み）、
　・ＡＩモデルの訓練／更新方法（例えば、（推奨）エポック数、バッチサイズ、訓練に使用するデータ数）。 The training information for the AI model may include information regarding at least one of the following:
・Information for the optimization algorithm (e.g., optimization type (Stochastic Gradient Descent (SGD), AdaGrad, Adam, etc.), optimization parameters (learning rate, momentum, etc.) information, etc.),
・Information on the loss function (for example, information on the metrics of the loss function (Mean Absolute Error (MAE), Mean Square Error (MSE)), cross entropy loss, NLLLoss, Kullback- Leibler (KL) divergence, etc.),
parameters to be frozen for training (e.g. layers, weights),
- parameters to be updated (e.g. layers, weights),
・Parameters (e.g., layers, weights) that should be initial parameters for training (should be used as initial parameters),
- How to train/update the AI model (e.g. (recommended) number of epochs, batch size, number of data used for training).
　上記ＡＩモデルのための推論情報は、決定木の枝剪定（branch　pruning）、パラメータ量子化、ＡＩモデルの機能などに関する情報を含んでもよい。ここで、ＡＩモデルの機能は、例えば、時間ドメインビーム予測、空間ドメインビーム予測、ＣＳＩフィードバック向けのオートエンコーダ、ビーム管理向けのオートエンコーダなどの少なくとも１つに該当してもよい。 The inference information for the AI model may include information regarding branch pruning of a decision tree, parameter quantization, functions of the AI model, and the like. Here, the function of the AI model may correspond to at least one of, for example, time domain beam prediction, spatial domain beam prediction, an autoencoder for CSI feedback, an autoencoder for beam management, etc.
　ＣＳＩフィードバック向けのオートエンコーダは、以下のように用いられてもよい：
　・ＵＥは、エンコーダのＡＩモデルに、ＣＳＩ／チャネル行列／プリコーディング行列を入力して出力される、エンコードされるビットを、ＣＳＩフィードバック（ＣＳＩレポート）として送信する、
　・ＢＳは、デコーダのＡＩモデルに、受信したエンコードされるビットを入力して出力される、ＣＳＩ／チャネル行列／プリコーディング行列を再構成する。 An autoencoder for CSI feedback may be used as follows:
- The UE inputs the CSI/channel matrix/precoding matrix into the encoder's AI model and transmits the output encoded bits as CSI feedback (CSI report).
- The BS inputs the received encoded bits into the decoder's AI model and reconstructs the output CSI/channel matrix/precoding matrix.
　空間ドメインビーム予測では、ＵＥ／ＢＳは、ＡＩモデルに、疎な（又は太い）ビームに基づく測定結果（ビーム品質。例えば、ＲＳＲＰ）を入力して、密な（又は細い）ビーム品質を出力してもよい。 In spatial domain beam prediction, the UE/BS inputs sparse (or thick) beam-based measurements (beam quality, e.g. RSRP) into an AI model and outputs dense (or thin) beam quality. It's okay.
　時間ドメインビーム予測では、ＵＥ／ＢＳは、ＡＩモデルに、時系列（過去、現在などの）測定結果（ビーム品質。例えば、ＲＳＲＰ）を入力して、将来のビーム品質を出力してもよい。 In time-domain beam prediction, the UE/BS may input time-series (past, current, etc.) measurement results (beam quality, e.g. RSRP) to the AI model and output future beam quality.
　上記ＡＩモデルに関する性能情報は、ＡＩモデルのために定義される損失関数の期待値に関する情報を含んでもよい。 The performance information regarding the AI model may include information regarding the expected value of a loss function defined for the AI model.
　本開示におけるＡＩモデル情報は、ＡＩモデルの適用範囲（適用可能範囲）に関する情報を含んでもよい。当該適用範囲は、物理セルＩＤ、サービングセルインデックスなどによって示されてもよい。適用範囲に関する情報は、上述の環境情報に含まれてもよい。 The AI model information in the present disclosure may include information regarding the applicable range (applicable range) of the AI model. The applicable range may be indicated by a physical cell ID, a serving cell index, etc. Information regarding the scope of application may be included in the above-mentioned environmental information.
　特定のＡＩモデルに関するＡＩモデル情報は、規格において予め定められてもよいし、ネットワーク（Network（ＮＷ））からＵＥに通知されてもよい。規格において規定されるＡＩモデルは、参照（reference）ＡＩモデルと呼ばれてもよい。参照ＡＩモデルに関するＡＩモデル情報は、参照ＡＩモデル情報と呼ばれてもよい。 AI model information regarding a specific AI model may be predefined in a standard, or may be notified to the UE from a network (NW). The AI model defined in the standard may be called a reference AI model. AI model information regarding the reference AI model may be referred to as reference AI model information.
　なお、本開示におけるＡＩモデル情報は、ＡＩモデルを特定するためのインデックス（例えば、ＡＩモデルインデックス、ＡＩモデルＩＤ、モデルＩＤなどと呼ばれてもよい）を含んでもよい。本開示におけるＡＩモデル情報は、上述のＡＩモデルの入力／出力の情報などに加えて／の代わりに、ＡＩモデルインデックスを含んでもよい。ＡＩモデルインデックスとＡＩモデル情報（例えば、ＡＩモデルの入力／出力の情報）との関連付けは、規格において予め定められてもよいし、ＮＷからＵＥに通知されてもよい。 Note that the AI model information in the present disclosure may include an index (for example, may be referred to as an AI model index, AI model ID, model ID, etc.) for identifying an AI model. The AI model information in the present disclosure may include an AI model index in addition to/instead of the input/output information of the AI model described above. The association between the AI model index and the AI model information (for example, input/output information of the AI model) may be predetermined in the standard, or may be notified from the NW to the UE.
　本開示におけるＡＩモデル情報は、ＡＩモデルに関連付けられてもよく、ＡＩモデル関連情報（relevant　information）、単に関連情報などと呼ばれてもよい。ＡＩモデル関連情報には、ＡＩモデルを特定するための情報は明示的に含まれなくてもよい。ＡＩモデル関連情報は、例えばメタ情報のみを含んだ情報であってもよい。 The AI model information in the present disclosure may be associated with the AI model, and may also be referred to as AI model relevant information, simply related information, or the like. The AI model related information does not need to explicitly include information for identifying the AI model. The AI model related information may be information containing only meta information, for example.
　本開示において、モデルＩＤは、ＡＩモデルのセットに対応するＩＤ（モデルセットＩＤ）と互いに読み替えられてもよい。また、本開示において、モデルＩＤは、メタ情報ＩＤと互いに読み替えられてもよい。メタ情報（又はメタ情報ＩＤ）は、上述したようにビームに関する情報（ビーム設定）と関連付けられてもよい。例えば、メタ情報（又はメタ情報ＩＤ）は、どのビームをＢＳが使用しているかを考慮してＵＥがＡＩモデルを選択するために用いられてもよいし、ＵＥがデプロイしたＡＩモデルを適用するためにＢＳがどのビームを使用すべきかを通知するために用いられてもよい。なお、本開示において、メタ情報ＩＤは、メタ情報のセットに対応するＩＤ（メタ情報セットＩＤ）と互いに読み替えられてもよい。 In the present disclosure, a model ID may be mutually read as an ID corresponding to a set of AI models (model set ID). Further, in the present disclosure, the model ID may be interchanged with the meta information ID. The meta information (or meta information ID) may be associated with beam-related information (beam settings) as described above. For example, the meta information (or meta information ID) may be used by the UE to select an AI model considering which beams the BS is using, or the UE may apply the deployed AI model. It may be used to inform the BS which beam to use for this purpose. Note that in the present disclosure, the meta information ID may be interchanged with an ID corresponding to a set of meta information (meta information set ID).
［ＵＥへの情報の通知］
　上述の実施形態における（ＮＷから）ＵＥへの任意の情報の通知（言い換えると、ＵＥにおけるＢＳからの任意の情報の受信）は、物理レイヤシグナリング（例えば、ＤＣＩ）、上位レイヤシグナリング（例えば、ＲＲＣシグナリング、ＭＡＣ　ＣＥ）、特定の信号／チャネル（例えば、ＰＤＣＣＨ、ＰＤＳＣＨ、参照信号）、又はこれらの組み合わせを用いて行われてもよい。 [Notification of information to UE]
In the embodiments described above, the notification of any information to the UE (from the NW) (in other words, the reception of any information from the BS in the UE) is performed using physical layer signaling (e.g., DCI), upper layer signaling (e.g., RRC). MAC CE), specific signals/channels (eg, PDCCH, PDSCH, reference signals), or a combination thereof.
　上記通知がＭＡＣ　ＣＥによって行われる場合、当該ＭＡＣ　ＣＥは、既存の規格では規定されていない新たな論理チャネルＩＤ（Logical　Channel　ID（ＬＣＩＤ））がＭＡＣサブヘッダに含まれることによって識別されてもよい。 When the above notification is performed by a MAC CE, the MAC CE may be identified by including a new logical channel ID (LCID), which is not specified in the existing standard, in the MAC subheader.
　上記通知がＤＣＩによって行われる場合、上記通知は、当該ＤＣＩの特定のフィールド、当該ＤＣＩに付与される巡回冗長検査（Cyclic　Redundancy　Check（ＣＲＣ））ビットのスクランブルに用いられる無線ネットワーク一時識別子（Radio　Network　Temporary　Identifier（ＲＮＴＩ））、当該ＤＣＩのフォーマットなどによって行われてもよい。 When the above notification is performed by a DCI, the above notification includes a specific field of the DCI, a radio network temporary identifier (Radio Network Temporary Identifier (RNTI)), the format of the DCI, etc.
　また、上述の実施形態におけるＵＥへの任意の情報の通知は、周期的、セミパーシステント又は非周期的に行われてもよい。 Additionally, notification of any information to the UE in the above embodiments may be performed periodically, semi-persistently, or aperiodically.
［ＵＥからの情報の通知］
　上述の実施形態におけるＵＥから（ＮＷへ）の任意の情報の通知（言い換えると、ＵＥにおけるＢＳへの任意の情報の送信）は、物理レイヤシグナリング（例えば、ＵＣＩ）、上位レイヤシグナリング（例えば、ＲＲＣシグナリング、ＭＡＣ　ＣＥ）、特定の信号／チャネル（例えば、ＰＵＣＣＨ、ＰＵＳＣＨ、参照信号）、又はこれらの組み合わせを用いて行われてもよい。 [Notification of information from UE]
In the embodiments described above, notification of any information from the UE (to the NW) (in other words, transmission of any information from the UE to the BS) is performed using physical layer signaling (e.g., UCI), upper layer signaling (e.g., RRC). MAC CE), specific signals/channels (eg, PUCCH, PUSCH, reference signals), or a combination thereof.
　上記通知がＭＡＣ　ＣＥによって行われる場合、当該ＭＡＣ　ＣＥは、既存の規格では規定されていない新たなＬＣＩＤがＭＡＣサブヘッダに含まれることによって識別されてもよい。 When the above notification is performed by a MAC CE, the MAC CE may be identified by including a new LCID that is not defined in the existing standard in the MAC subheader.
　上記通知がＵＣＩによって行われる場合、上記通知は、ＰＵＣＣＨ又はＰＵＳＣＨを用いて送信されてもよい。 When the above notification is performed by UCI, the above notification may be transmitted using PUCCH or PUSCH.
　また、上述の実施形態におけるＵＥからの任意の情報の通知は、周期的、セミパーシステント又は非周期的に行われてもよい。 Further, notification of arbitrary information from the UE in the above embodiments may be performed periodically, semi-persistently, or aperiodically.
［各実施形態の適用について］
　上述の実施形態の少なくとも１つは、特定の条件を満たす場合に適用されてもよい。当該特定の条件は、規格において規定されてもよいし、上位レイヤシグナリング／物理レイヤシグナリングを用いてＵＥ／ＢＳに通知されてもよい。 [About application of each embodiment]
At least one of the embodiments described above may be applied if certain conditions are met. The specific conditions may be specified in the standard, or may be notified to the UE/BS using upper layer signaling/physical layer signaling.
　上述の実施形態の少なくとも１つは、特定のＵＥ能力（UE　capability）を報告した又は当該特定のＵＥ能力をサポートするＵＥに対してのみ適用されてもよい。 At least one of the embodiments described above may be applied only to UEs that have reported or support a particular UE capability.
　当該特定のＵＥ能力は、以下の少なくとも１つを示してもよい：
　・上記実施形態の少なくとも１つについての特定の処理／動作／制御／情報をサポートすること、
　・モニタされるＡＩモデルの数（機能ごとの値であってもよい）、
　・ＵＥがデプロイできるＡＩモデルの最大の浮動小数点演算（floating　point　operations（ＦＬＯＰｓ（なお、ｓは小文字）））（これは、浮動小数点演算量を意味する）、
　・ＵＥがデプロイできるＡＩモデルの最大のパラメータ数、
　・ＵＥがサポートするレイヤ／アルゴリズム／機能、
　・計算能力、
　・データ収集能力。 The particular UE capability may indicate at least one of the following:
- supporting specific processing/operation/control/information for at least one of the above embodiments;
・Number of AI models to be monitored (may be a value for each function),
・Maximum floating point operations (FLOPs (note that s is lowercase)) of the AI model that the UE can deploy (this means the amount of floating point operations),
・Maximum number of parameters of AI model that UE can deploy,
・Layers/algorithms/functions supported by the UE,
・Calculating ability,
・Data collection ability.
　また、上記特定のＵＥ能力は、全周波数にわたって（周波数に関わらず共通に）適用される能力であってもよいし、周波数（例えば、セル、バンド、バンドコンビネーション、ＢＷＰ、コンポーネントキャリアなどの１つ又はこれらの組み合わせ）ごとの能力であってもよいし、周波数レンジ（例えば、Frequency　Range　1（ＦＲ１）、ＦＲ２、ＦＲ３、ＦＲ４、ＦＲ５、ＦＲ２－１、ＦＲ２－２）ごとの能力であってもよいし、サブキャリア間隔（SubCarrier　Spacing（ＳＣＳ））ごとの能力であってもよいし、Feature　Set（ＦＳ）又はFeature　Set　Per　Component-carrier（ＦＳＰＣ）ごとの能力であってもよい。 Further, the specific UE capability may be a capability that is applied across all frequencies (commonly regardless of frequency) or a capability that is applied across all frequencies (e.g., cell, band, band combination, BWP, component carrier, etc.). or a combination thereof), or it may be a capability for each frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2). Alternatively, it may be a capability for each subcarrier spacing (SCS), or a capability for each Feature Set (FS) or Feature Set Per Component-carrier (FSPC).
　また、上記特定のＵＥ能力は、全複信方式にわたって（複信方式に関わらず共通に）適用される能力であってもよいし、複信方式（例えば、時分割複信（Time　Division　Duplex（ＴＤＤ））、周波数分割複信（Frequency　Division　Duplex（ＦＤＤ）））ごとの能力であってもよい。 Furthermore, the above-mentioned specific UE capability may be a capability that is applied across all duplex schemes (commonly regardless of the duplex scheme), or may be a capability that is applied across all duplex schemes (for example, Time Division Duplex). The capability may be for each frequency division duplex (TDD)) or frequency division duplex (FDD)).
　また、上述の実施形態の少なくとも１つは、ＵＥが上位レイヤシグナリング／物理レイヤシグナリングによって、上述の実施形態に関連する特定の情報（又は上述の実施形態の動作を実施すること）を設定／アクティベート／トリガされた場合に適用されてもよい。例えば、当該特定の情報は、ＡＩモデルの利用を有効化することを示す情報、特定のリリース（例えば、Ｒｅｌ．１８）向けの任意のＲＲＣパラメータなどであってもよい。 In addition, at least one of the embodiments described above may be configured such that the UE configures/activates specific information related to the embodiment described above (or performs the operation of the embodiment described above) by upper layer signaling/physical layer signaling. / May be applied when triggered. For example, the specific information may be information indicating that the use of the AI model is enabled, arbitrary RRC parameters for a specific release (for example, Rel. 18), or the like.
　ＵＥは、上記特定のＵＥ能力の少なくとも１つをサポートしない又は上記特定の情報を設定されない場合、例えばＲｅｌ．１５／１６の動作を適用してもよい。 If the UE does not support at least one of the specific UE capabilities or is not configured with the specific information, for example, Rel. 15/16 operations may be applied.
（付記）
　本開示の一実施形態に関して、以下の発明を付記する。
［付記１］
　人工知能（Artificial　Intelligence（ＡＩ））ベースドビーム管理（Beam　Management（ＢＭ））に関して、性能モニタリングのための設定情報を受信する受信部と、
　前記性能モニタリングを制御する制御部と、を有する端末。
［付記２］
　前記制御部は、特定の対応関係にあるビーム予測のための参照信号リソースと性能モニタリングリファレンスのための参照信号とに基づいて、前記ＡＩベースドＢＭの性能をモニタする付記１に記載の端末。
［付記３］
　前記制御部は、ビーム予測のためのＣＳＩ－ＲＳ機会及び性能モニタリングリファレンスのためのＣＳＩ－ＲＳ機会の一方に基づいて他方を決定し、これらのＣＳＩ機会に基づいて、前記ＡＩベースドＢＭの性能をモニタする付記１又は付記２に記載の端末。 (Additional note)
Regarding one embodiment of the present disclosure, the following invention will be added.
[Additional note 1]
A receiving unit that receives configuration information for performance monitoring regarding artificial intelligence (AI) based beam management (BM);
A terminal comprising: a control unit that controls the performance monitoring.
[Additional note 2]
The terminal according to supplementary note 1, wherein the control unit monitors the performance of the AI-based BM based on a reference signal resource for beam prediction and a reference signal for performance monitoring reference that have a specific correspondence relationship.
[Additional note 3]
The control unit determines the other based on one of a CSI-RS opportunity for beam prediction and a CSI-RS opportunity for performance monitoring reference, and determines the performance of the AI-based BM based on these CSI opportunities. The terminal described in Appendix 1 or 2 to be monitored.
（付記）
　本開示の一実施形態に関して、以下の発明を付記する。
［付記１］
　人工知能（Artificial　Intelligence（ＡＩ））ベースドビーム管理（Beam　Management（ＢＭ））に関して、性能モニタリング向けの報告のための設定情報を受信する受信部と、
　前記設定情報に基づいて測定されるLayer　1　Reference　Signal　Received　Power（Ｌ１－ＲＳＲＰ）に関する報告の送信を制御する制御部と、を有する端末。
［付記２］
　前記制御部は、性能モニタリングリファレンスのために参照される参照信号機会より一定の単位時間遅くなく、又は一定の単位時間早くない予測時間に対応する予測Ｌ１－ＲＳＲＰを、前記報告に含める付記１に記載の端末。 (Additional note)
Regarding one embodiment of the present disclosure, the following invention will be added.
[Additional note 1]
a receiving unit that receives configuration information for reporting for performance monitoring regarding artificial intelligence (AI) based beam management (BM);
A terminal comprising: a control unit that controls transmission of a report regarding Layer 1 Reference Signal Received Power (L1-RSRP) measured based on the configuration information.
[Additional note 2]
In Supplementary Note 1, the control unit includes in the report a predicted L1-RSRP corresponding to a predicted time that is not later than a certain unit time or is not earlier than a certain unit time than a reference signal opportunity referred to for a performance monitoring reference. The device listed.
（無線通信システム）
　以下、本開示の一実施形態に係る無線通信システムの構成について説明する。この無線通信システムでは、本開示の上記各実施形態に係る無線通信方法のいずれか又はこれらの組み合わせを用いて通信が行われる。 (wireless communication system)
The configuration of a wireless communication system according to an embodiment of the present disclosure will be described below. In this wireless communication system, communication is performed using any one of the wireless communication methods according to the above-described embodiments of the present disclosure or a combination thereof.
　図９は、一実施形態に係る無線通信システムの概略構成の一例を示す図である。無線通信システム１（単にシステム１と呼ばれてもよい）は、Third　Generation　Partnership　Project（３ＧＰＰ）によって仕様化されるLong　Term　Evolution（ＬＴＥ）、5th　generation　mobile　communication　system　New　Radio（５Ｇ　ＮＲ）などを用いて通信を実現するシステムであってもよい。 FIG. 9 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment. The wireless communication system 1 (also simply referred to as system 1) uses Long Term Evolution (LTE), 5th generation mobile communication system New Radio (5G NR), etc. specified by the Third Generation Partnership Project (3GPP). It may also be a system that realizes communication using
　また、無線通信システム１は、複数のRadio　Access　Technology（ＲＡＴ）間のデュアルコネクティビティ（マルチＲＡＴデュアルコネクティビティ（Multi-RAT　Dual　Connectivity（ＭＲ－ＤＣ）））をサポートしてもよい。ＭＲ－ＤＣは、ＬＴＥ（Evolved　Universal　Terrestrial　Radio　Access（Ｅ－ＵＴＲＡ））とＮＲとのデュアルコネクティビティ（E-UTRA-NR　Dual　Connectivity（ＥＮ－ＤＣ））、ＮＲとＬＴＥとのデュアルコネクティビティ（NR-E-UTRA　Dual　Connectivity（ＮＥ－ＤＣ））などを含んでもよい。 Additionally, the wireless communication system 1 may support dual connectivity between multiple Radio Access Technologies (RATs) (Multi-RAT Dual Connectivity (MR-DC)). MR-DC has dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (EN-DC)), and dual connectivity between NR and LTE (NR-E -UTRA Dual Connectivity (NE-DC)).
　ＥＮ－ＤＣでは、ＬＴＥ（Ｅ－ＵＴＲＡ）の基地局（ｅＮＢ）がマスタノード（Master　Node（ＭＮ））であり、ＮＲの基地局（ｇＮＢ）がセカンダリノード（Secondary　Node（ＳＮ））である。ＮＥ－ＤＣでは、ＮＲの基地局（ｇＮＢ）がＭＮであり、ＬＴＥ（Ｅ－ＵＴＲＡ）の基地局（ｅＮＢ）がＳＮである。 In EN-DC, the LTE (E-UTRA) base station (eNB) is the master node (Master Node (MN)), and the NR base station (gNB) is the secondary node (Secondary Node (SN)). In NE-DC, the NR base station (gNB) is the MN, and the LTE (E-UTRA) base station (eNB) is the SN.
　無線通信システム１は、同一のＲＡＴ内の複数の基地局間のデュアルコネクティビティ（例えば、ＭＮ及びＳＮの双方がＮＲの基地局（ｇＮＢ）であるデュアルコネクティビティ（NR-NR　Dual　Connectivity（ＮＮ－ＤＣ）））をサポートしてもよい。 The wireless communication system 1 has dual connectivity between multiple base stations within the same RAT (for example, dual connectivity (NR-NR Dual Connectivity (NN-DC) where both the MN and SN are NR base stations (gNB)). )) may be supported.
　無線通信システム１は、比較的カバレッジの広いマクロセルＣ１を形成する基地局１１と、マクロセルＣ１内に配置され、マクロセルＣ１よりも狭いスモールセルＣ２を形成する基地局１２（１２ａ－１２ｃ）と、を備えてもよい。ユーザ端末２０は、少なくとも１つのセル内に位置してもよい。各セル及びユーザ端末２０の配置、数などは、図に示す態様に限定されない。以下、基地局１１及び１２を区別しない場合は、基地局１０と総称する。 The wireless communication system 1 includes a base station 11 that forms a macro cell C1 with relatively wide coverage, and base stations 12 (12a-12c) that are located within the macro cell C1 and form a small cell C2 that is narrower than the macro cell C1. You may prepare. User terminal 20 may be located within at least one cell. The arrangement, number, etc. of each cell and user terminal 20 are not limited to the embodiment shown in the figure. Hereinafter, when base stations 11 and 12 are not distinguished, they will be collectively referred to as base station 10.
　ユーザ端末２０は、複数の基地局１０のうち、少なくとも１つに接続してもよい。ユーザ端末２０は、複数のコンポーネントキャリア（Component　Carrier（ＣＣ））を用いたキャリアアグリゲーション（Carrier　Aggregation（ＣＡ））及びデュアルコネクティビティ（ＤＣ）の少なくとも一方を利用してもよい。 The user terminal 20 may be connected to at least one of the plurality of base stations 10. The user terminal 20 may utilize at least one of carrier aggregation (CA) using a plurality of component carriers (CC) and dual connectivity (DC).
　各ＣＣは、第１の周波数帯（Frequency　Range　1（ＦＲ１））及び第２の周波数帯（Frequency　Range　2（ＦＲ２））の少なくとも１つに含まれてもよい。マクロセルＣ１はＦＲ１に含まれてもよいし、スモールセルＣ２はＦＲ２に含まれてもよい。例えば、ＦＲ１は、６ＧＨｚ以下の周波数帯（サブ６ＧＨｚ（sub-6GHz））であってもよいし、ＦＲ２は、２４ＧＨｚよりも高い周波数帯（above-24GHz）であってもよい。なお、ＦＲ１及びＦＲ２の周波数帯、定義などはこれらに限られず、例えばＦＲ１がＦＲ２よりも高い周波数帯に該当してもよい。 Each CC may be included in at least one of a first frequency band (Frequency Range 1 (FR1)) and a second frequency band (Frequency Range 2 (FR2)). Macro cell C1 may be included in FR1, and small cell C2 may be included in FR2. For example, FR1 may be a frequency band below 6 GHz (sub-6 GHz), and FR2 may be a frequency band above 24 GHz (above-24 GHz). Note that the frequency bands and definitions of FR1 and FR2 are not limited to these, and FR1 may correspond to a higher frequency band than FR2, for example.
　また、ユーザ端末２０は、各ＣＣにおいて、時分割複信（Time　Division　Duplex（ＴＤＤ））及び周波数分割複信（Frequency　Division　Duplex（ＦＤＤ））の少なくとも１つを用いて通信を行ってもよい。 Further, the user terminal 20 may communicate using at least one of time division duplex (TDD) and frequency division duplex (FDD) in each CC.
　複数の基地局１０は、有線（例えば、Common　Public　Radio　Interface（ＣＰＲＩ）に準拠した光ファイバ、Ｘ２インターフェースなど）又は無線（例えば、ＮＲ通信）によって接続されてもよい。例えば、基地局１１及び１２間においてＮＲ通信がバックホールとして利用される場合、上位局に該当する基地局１１はIntegrated　Access　Backhaul（ＩＡＢ）ドナー、中継局（リレー）に該当する基地局１２はＩＡＢノードと呼ばれてもよい。 The plurality of base stations 10 may be connected by wire (for example, optical fiber, X2 interface, etc. compliant with Common Public Radio Interface (CPRI)) or wirelessly (for example, NR communication). For example, when NR communication is used as a backhaul between base stations 11 and 12, base station 11, which is an upper station, is an Integrated Access Backhaul (IAB) donor, and base station 12, which is a relay station, is an IAB donor. May also be called a node.
　基地局１０は、他の基地局１０を介して、又は直接コアネットワーク３０に接続されてもよい。コアネットワーク３０は、例えば、Evolved　Packet　Core（ＥＰＣ）、5G　Core　Network（５ＧＣＮ）、Next　Generation　Core（ＮＧＣ）などの少なくとも１つを含んでもよい。 The base station 10 may be connected to the core network 30 via another base station 10 or directly. The core network 30 may include, for example, at least one of Evolved Packet Core (EPC), 5G Core Network (5GCN), Next Generation Core (NGC), and the like.
　コアネットワーク３０は、例えば、User　Plane　Function（ＵＰＦ）、Access　and　Mobility　management　Function（ＡＭＦ）、Session　Management　Function（ＳＭＦ）、Unified　Data　Management（ＵＤＭ）、ApplicationFunction（ＡＦ）、Data　Network（ＤＮ）、Location　Management　Function（ＬＭＦ）、保守運用管理（Operation、Administration　and　Maintenance（Management）（ＯＡＭ））などのネットワーク機能（Network　Functions（ＮＦ））を含んでもよい。なお、１つのネットワークノードによって複数の機能が提供されてもよい。また、ＤＮを介して外部ネットワーク（例えば、インターネット）との通信が行われてもよい。 Core Network 30 is, for example, User Plane Function (UPF), Access and Mobility Management Function (AMF), Session Management (SMF), Unified Data Management. T (UDM), ApplicationFunction (AF), Data Network (DN), Location Management Network Functions (NF) such as Function (LMF) and Operation, Administration and Maintenance (Management) (OAM) may also be included. Note that multiple functions may be provided by one network node. Furthermore, communication with an external network (eg, the Internet) may be performed via the DN.
　ユーザ端末２０は、ＬＴＥ、ＬＴＥ－Ａ、５Ｇなどの通信方式の少なくとも１つに対応した端末であってもよい。 The user terminal 20 may be a terminal compatible with at least one of communication systems such as LTE, LTE-A, and 5G.
　無線通信システム１においては、直交周波数分割多重（Orthogonal　Frequency　Division　Multiplexing（ＯＦＤＭ））ベースの無線アクセス方式が利用されてもよい。例えば、下りリンク（Downlink（ＤＬ））及び上りリンク（Uplink（ＵＬ））の少なくとも一方において、Cyclic　Prefix　OFDM（ＣＰ－ＯＦＤＭ）、Discrete　Fourier　Transform　Spread　OFDM（ＤＦＴ－ｓ－ＯＦＤＭ）、Orthogonal　Frequency　Division　Multiple　Access（ＯＦＤＭＡ）、Single　Carrier　Frequency　Division　Multiple　Access（ＳＣ－ＦＤＭＡ）などが利用されてもよい。 In the wireless communication system 1, an orthogonal frequency division multiplexing (OFDM)-based wireless access method may be used. For example, in at least one of the downlink (DL) and uplink (UL), Cyclic Prefix OFDM (CP-OFDM), Discrete Fourier Transform Spread OFDM (DFT-s-OFDM), Orthogonal Frequency Division Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-FDMA), etc. may be used.
　無線アクセス方式は、波形（waveform）と呼ばれてもよい。なお、無線通信システム１においては、ＵＬ及びＤＬの無線アクセス方式には、他の無線アクセス方式（例えば、他のシングルキャリア伝送方式、他のマルチキャリア伝送方式）が用いられてもよい。 A wireless access method may also be called a waveform. Note that in the wireless communication system 1, other wireless access methods (for example, other single carrier transmission methods, other multicarrier transmission methods) may be used as the UL and DL radio access methods.
　無線通信システム１では、下りリンクチャネルとして、各ユーザ端末２０で共有される下り共有チャネル（Physical　Downlink　Shared　Channel（ＰＤＳＣＨ））、ブロードキャストチャネル（Physical　Broadcast　Channel（ＰＢＣＨ））、下り制御チャネル（Physical　Downlink　Control　Channel（ＰＤＣＣＨ））などが用いられてもよい。 In the wireless communication system 1, the downlink channels include a physical downlink shared channel (PDSCH) shared by each user terminal 20, a broadcast channel (physical broadcast channel (PBCH)), and a downlink control channel (physical downlink control). Channel (PDCCH)) or the like may be used.
　また、無線通信システム１では、上りリンクチャネルとして、各ユーザ端末２０で共有される上り共有チャネル（Physical　Uplink　Shared　Channel（ＰＵＳＣＨ））、上り制御チャネル（Physical　Uplink　Control　Channel（ＰＵＣＣＨ））、ランダムアクセスチャネル（Physical　Random　Access　Channel（ＰＲＡＣＨ））などが用いられてもよい。 In the wireless communication system 1, uplink channels include a physical uplink shared channel (PUSCH) shared by each user terminal 20, a physical uplink control channel (PUCCH), and a random access channel. (Physical Random Access Channel (PRACH)) or the like may be used.
　ＰＤＳＣＨによって、ユーザデータ、上位レイヤ制御情報、System　Information　Block（ＳＩＢ）などが伝送される。ＰＵＳＣＨによって、ユーザデータ、上位レイヤ制御情報などが伝送されてもよい。また、ＰＢＣＨによって、Master　Information　Block（ＭＩＢ）が伝送されてもよい。 User data, upper layer control information, System Information Block (SIB), etc. are transmitted by the PDSCH. User data, upper layer control information, etc. may be transmitted by PUSCH. Further, a Master Information Block (MIB) may be transmitted by PBCH.
　ＰＤＣＣＨによって、下位レイヤ制御情報が伝送されてもよい。下位レイヤ制御情報は、例えば、ＰＤＳＣＨ及びＰＵＳＣＨの少なくとも一方のスケジューリング情報を含む下り制御情報（Downlink　Control　Information（ＤＣＩ））を含んでもよい。 Lower layer control information may be transmitted by PDCCH. The lower layer control information may include, for example, downlink control information (DCI) that includes scheduling information for at least one of PDSCH and PUSCH.
　なお、ＰＤＳＣＨをスケジューリングするＤＣＩは、ＤＬアサインメント、ＤＬ　ＤＣＩなどと呼ばれてもよいし、ＰＵＳＣＨをスケジューリングするＤＣＩは、ＵＬグラント、ＵＬ　ＤＣＩなどと呼ばれてもよい。なお、ＰＤＳＣＨはＤＬデータで読み替えられてもよいし、ＰＵＳＣＨはＵＬデータで読み替えられてもよい。 Note that the DCI that schedules PDSCH may be called DL assignment, DL DCI, etc., and the DCI that schedules PUSCH may be called UL grant, UL DCI, etc. Note that PDSCH may be replaced with DL data, and PUSCH may be replaced with UL data.
　ＰＤＣＣＨの検出には、制御リソースセット（COntrol　REsource　SET（ＣＯＲＥＳＥＴ））及びサーチスペース（search　space）が利用されてもよい。ＣＯＲＥＳＥＴは、ＤＣＩをサーチするリソースに対応する。サーチスペースは、ＰＤＣＣＨ候補（PDCCH　candidates）のサーチ領域及びサーチ方法に対応する。１つのＣＯＲＥＳＥＴは、１つ又は複数のサーチスペースに関連付けられてもよい。ＵＥは、サーチスペース設定に基づいて、あるサーチスペースに関連するＣＯＲＥＳＥＴをモニタしてもよい。 A control resource set (CONtrol REsource SET (CORESET)) and a search space may be used to detect the PDCCH. CORESET corresponds to a resource for searching DCI. The search space corresponds to a search area and a search method for PDCCH candidates (PDCCH candidates). One CORESET may be associated with one or more search spaces. The UE may monitor the CORESET associated with a certain search space based on the search space configuration.
　１つのサーチスペースは、１つ又は複数のアグリゲーションレベル（aggregation　Level）に該当するＰＤＣＣＨ候補に対応してもよい。１つ又は複数のサーチスペースは、サーチスペースセットと呼ばれてもよい。なお、本開示の「サーチスペース」、「サーチスペースセット」、「サーチスペース設定」、「サーチスペースセット設定」、「ＣＯＲＥＳＥＴ」、「ＣＯＲＥＳＥＴ設定」などは、互いに読み替えられてもよい。 One search space may correspond to PDCCH candidates corresponding to one or more aggregation levels. One or more search spaces may be referred to as a search space set. Note that "search space", "search space set", "search space setting", "search space set setting", "CORESET", "CORESET setting", etc. in the present disclosure may be read interchangeably.
　ＰＵＣＣＨによって、チャネル状態情報（Channel　State　Information（ＣＳＩ））、送達確認情報（例えば、Hybrid　Automatic　Repeat　reQuest　ACKnowledgement（ＨＡＲＱ－ＡＣＫ）、ＡＣＫ／ＮＡＣＫなどと呼ばれてもよい）及びスケジューリングリクエスト（Scheduling　Request（ＳＲ））の少なくとも１つを含む上り制御情報（Uplink　Control　Information（ＵＣＩ））が伝送されてもよい。ＰＲＡＣＨによって、セルとの接続確立のためのランダムアクセスプリアンブルが伝送されてもよい。 The PUCCH allows channel state information (CSI), delivery confirmation information (for example, may be called Hybrid Automatic Repeat Request ACKnowledgement (HARQ-ACK), ACK/NACK, etc.), and scheduling request ( Uplink Control Information (UCI) including at least one of SR)) may be transmitted. A random access preamble for establishing a connection with a cell may be transmitted by PRACH.
　なお、本開示において下りリンク、上りリンクなどは「リンク」を付けずに表現されてもよい。また、各種チャネルの先頭に「物理（Physical）」を付けずに表現されてもよい。 Note that in this disclosure, downlinks, uplinks, etc. may be expressed without adding "link". Furthermore, various channels may be expressed without adding "Physical" at the beginning.
　無線通信システム１では、同期信号（Synchronization　Signal（ＳＳ））、下りリンク参照信号（Downlink　Reference　Signal（ＤＬ－ＲＳ））などが伝送されてもよい。無線通信システム１では、ＤＬ－ＲＳとして、セル固有参照信号（Cell-specific　Reference　Signal（ＣＲＳ））、チャネル状態情報参照信号（Channel　State　Information　Reference　Signal（ＣＳＩ－ＲＳ））、復調用参照信号（DeModulation　Reference　Signal（ＤＭＲＳ））、位置決定参照信号（Positioning　Reference　Signal（ＰＲＳ））、位相トラッキング参照信号（Phase　Tracking　Reference　Signal（ＰＴＲＳ））などが伝送されてもよい。 In the wireless communication system 1, a synchronization signal (SS), a downlink reference signal (DL-RS), and the like may be transmitted. In the wireless communication system 1, the DL-RS includes a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS), and a demodulation reference signal (DeModulation). Reference Signal (DMRS)), Positioning Reference Signal (PRS), Phase Tracking Reference Signal (PTRS), etc. may be transmitted.
　同期信号は、例えば、プライマリ同期信号（Primary　Synchronization　Signal（ＰＳＳ））及びセカンダリ同期信号（Secondary　Synchronization　Signal（ＳＳＳ））の少なくとも１つであってもよい。ＳＳ（ＰＳＳ、ＳＳＳ）及びＰＢＣＨ（及びＰＢＣＨ用のＤＭＲＳ）を含む信号ブロックは、ＳＳ／ＰＢＣＨブロック、SS　Block（ＳＳＢ）などと呼ばれてもよい。なお、ＳＳ、ＳＳＢなども、参照信号と呼ばれてもよい。 The synchronization signal may be, for example, at least one of a primary synchronization signal (PSS) and a secondary synchronization signal (SSS). A signal block including SS (PSS, SSS) and PBCH (and DMRS for PBCH) may be called an SS/PBCH block, SS Block (SSB), etc. Note that SS, SSB, etc. may also be called reference signals.
　また、無線通信システム１では、上りリンク参照信号（Uplink　Reference　Signal（ＵＬ－ＲＳ））として、測定用参照信号（Sounding　Reference　Signal（ＳＲＳ））、復調用参照信号（ＤＭＲＳ）などが伝送されてもよい。なお、ＤＭＲＳはユーザ端末固有参照信号（UE-specific　Reference　Signal）と呼ばれてもよい。 In addition, in the wireless communication system 1, measurement reference signals (Sounding Reference Signal (SRS)), demodulation reference signals (DMRS), etc. are transmitted as uplink reference signals (UL-RS). good. Note that DMRS may be called a user terminal-specific reference signal (UE-specific reference signal).
（基地局）
　図１０は、一実施形態に係る基地局の構成の一例を示す図である。基地局１０は、制御部１１０、送受信部１２０、送受信アンテナ１３０及び伝送路インターフェース（transmission　line　interface）１４０を備えている。なお、制御部１１０、送受信部１２０及び送受信アンテナ１３０及び伝送路インターフェース１４０は、それぞれ１つ以上が備えられてもよい。 (base station)
FIG. 10 is a diagram illustrating an example of the configuration of a base station according to an embodiment. The base station 10 includes a control section 110, a transmitting/receiving section 120, a transmitting/receiving antenna 130, and a transmission line interface 140. Note that one or more of each of the control unit 110, the transmitting/receiving unit 120, the transmitting/receiving antenna 130, and the transmission path interface 140 may be provided.
　なお、本例では、本実施の形態における特徴部分の機能ブロックを主に示しており、基地局１０は、無線通信に必要な他の機能ブロックも有すると想定されてもよい。以下で説明する各部の処理の一部は、省略されてもよい。 Note that this example mainly shows functional blocks that are characteristic of the present embodiment, and it may be assumed that the base station 10 also has other functional blocks necessary for wireless communication. A part of the processing of each unit described below may be omitted.
　制御部１１０は、基地局１０全体の制御を実施する。制御部１１０は、本開示に係る技術分野での共通認識に基づいて説明されるコントローラ、制御回路などから構成することができる。 The control unit 110 controls the entire base station 10. The control unit 110 can be configured from a controller, a control circuit, etc., which will be explained based on common recognition in the technical field related to the present disclosure.
　制御部１１０は、信号の生成、スケジューリング（例えば、リソース割り当て、マッピング）などを制御してもよい。制御部１１０は、送受信部１２０、送受信アンテナ１３０及び伝送路インターフェース１４０を用いた送受信、測定などを制御してもよい。制御部１１０は、信号として送信するデータ、制御情報、系列（sequence）などを生成し、送受信部１２０に転送してもよい。制御部１１０は、通信チャネルの呼処理（設定、解放など）、基地局１０の状態管理、無線リソースの管理などを行ってもよい。 The control unit 110 may control signal generation, scheduling (e.g., resource allocation, mapping), and the like. The control unit 110 may control transmission and reception, measurement, etc. using the transmitting/receiving unit 120, the transmitting/receiving antenna 130, and the transmission path interface 140. The control unit 110 may generate data, control information, a sequence, etc. to be transmitted as a signal, and may transfer the generated data to the transmitting/receiving unit 120. The control unit 110 may perform communication channel call processing (setting, release, etc.), status management of the base station 10, radio resource management, and the like.
　送受信部１２０は、ベースバンド（baseband）部１２１、Radio　Frequency（ＲＦ）部１２２、測定部１２３を含んでもよい。ベースバンド部１２１は、送信処理部１２１１及び受信処理部１２１２を含んでもよい。送受信部１２０は、本開示に係る技術分野での共通認識に基づいて説明されるトランスミッター／レシーバー、ＲＦ回路、ベースバンド回路、フィルタ、位相シフタ（phase　shifter）、測定回路、送受信回路などから構成することができる。 The transmitting/receiving section 120 may include a baseband section 121, a radio frequency (RF) section 122, and a measuring section 123. The baseband section 121 may include a transmission processing section 1211 and a reception processing section 1212. The transmitter/receiver unit 120 includes a transmitter/receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transmitter/receiver circuit, etc., which are explained based on common understanding in the technical field related to the present disclosure. be able to.
　送受信部１２０は、一体の送受信部として構成されてもよいし、送信部及び受信部から構成されてもよい。当該送信部は、送信処理部１２１１、ＲＦ部１２２から構成されてもよい。当該受信部は、受信処理部１２１２、ＲＦ部１２２、測定部１２３から構成されてもよい。 The transmitting/receiving section 120 may be configured as an integrated transmitting/receiving section, or may be configured from a transmitting section and a receiving section. The transmitting section may include a transmitting processing section 1211 and an RF section 122. The reception section may include a reception processing section 1212, an RF section 122, and a measurement section 123.
　送受信アンテナ１３０は、本開示に係る技術分野での共通認識に基づいて説明されるアンテナ、例えばアレイアンテナなどから構成することができる。 The transmitting/receiving antenna 130 can be configured from an antenna described based on common recognition in the technical field related to the present disclosure, such as an array antenna.
　送受信部１２０は、上述の下りリンクチャネル、同期信号、下りリンク参照信号などを送信してもよい。送受信部１２０は、上述の上りリンクチャネル、上りリンク参照信号などを受信してもよい。 The transmitter/receiver 120 may transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transmitter/receiver 120 may receive the above-mentioned uplink channel, uplink reference signal, and the like.
　送受信部１２０は、デジタルビームフォーミング（例えば、プリコーディング）、アナログビームフォーミング（例えば、位相回転）などを用いて、送信ビーム及び受信ビームの少なくとも一方を形成してもよい。 The transmitting/receiving unit 120 may form at least one of a transmitting beam and a receiving beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.
　送受信部１２０（送信処理部１２１１）は、例えば制御部１１０から取得したデータ、制御情報などに対して、Packet　Data　Convergence　Protocol（ＰＤＣＰ）レイヤの処理、Radio　Link　Control（ＲＬＣ）レイヤの処理（例えば、ＲＬＣ再送制御）、Medium　Access　Control（ＭＡＣ）レイヤの処理（例えば、ＨＡＲＱ再送制御）などを行い、送信するビット列を生成してもよい。 The transmitting/receiving unit 120 (transmission processing unit 1211) performs Packet Data Convergence Protocol (PDCP) layer processing, Radio Link Control (RLC) layer processing (for example, RLC retransmission control), Medium Access Control (MAC) layer processing (for example, HARQ retransmission control), etc. may be performed to generate a bit string to be transmitted.
　送受信部１２０（送信処理部１２１１）は、送信するビット列に対して、チャネル符号化（誤り訂正符号化を含んでもよい）、変調、マッピング、フィルタ処理、離散フーリエ変換（Discrete　Fourier　Transform（ＤＦＴ））処理（必要に応じて）、逆高速フーリエ変換（Inverse　Fast　Fourier　Transform（ＩＦＦＴ））処理、プリコーディング、デジタル－アナログ変換などの送信処理を行い、ベースバンド信号を出力してもよい。 The transmitting/receiving unit 120 (transmission processing unit 1211) performs channel encoding (which may include error correction encoding), modulation, mapping, filter processing, and discrete Fourier transform (DFT) on the bit string to be transmitted. A baseband signal may be output by performing transmission processing such as processing (if necessary), Inverse Fast Fourier Transform (IFFT) processing, precoding, and digital-to-analog conversion.
　送受信部１２０（ＲＦ部１２２）は、ベースバンド信号に対して、無線周波数帯への変調、フィルタ処理、増幅などを行い、無線周波数帯の信号を、送受信アンテナ１３０を介して送信してもよい。 The transmitting/receiving unit 120 (RF unit 122) may perform modulation, filter processing, amplification, etc. on the baseband signal in a radio frequency band, and may transmit the signal in the radio frequency band via the transmitting/receiving antenna 130. .
　一方、送受信部１２０（ＲＦ部１２２）は、送受信アンテナ１３０によって受信された無線周波数帯の信号に対して、増幅、フィルタ処理、ベースバンド信号への復調などを行ってもよい。 On the other hand, the transmitting/receiving section 120 (RF section 122) may perform amplification, filter processing, demodulation into a baseband signal, etc. on the radio frequency band signal received by the transmitting/receiving antenna 130.
　送受信部１２０（受信処理部１２１２）は、取得されたベースバンド信号に対して、アナログ－デジタル変換、高速フーリエ変換（Fast　Fourier　Transform（ＦＦＴ））処理、逆離散フーリエ変換（Inverse　Discrete　Fourier　Transform（ＩＤＦＴ））処理（必要に応じて）、フィルタ処理、デマッピング、復調、復号（誤り訂正復号を含んでもよい）、ＭＡＣレイヤ処理、ＲＬＣレイヤの処理及びＰＤＣＰレイヤの処理などの受信処理を適用し、ユーザデータなどを取得してもよい。 The transmitting/receiving unit 120 (reception processing unit 1212) performs analog-to-digital conversion, fast Fourier transform (FFT) processing, and inverse discrete Fourier transform (IDFT) on the acquired baseband signal. )) processing (if necessary), applying reception processing such as filter processing, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing and PDCP layer processing, User data etc. may also be acquired.
　送受信部１２０（測定部１２３）は、受信した信号に関する測定を実施してもよい。例えば、測定部１２３は、受信した信号に基づいて、Radio　Resource　Management（ＲＲＭ）測定、Channel　State　Information（ＣＳＩ）測定などを行ってもよい。測定部１２３は、受信電力（例えば、Reference　Signal　Received　Power（ＲＳＲＰ））、受信品質（例えば、Reference　Signal　Received　Quality（ＲＳＲＱ）、Signal　to　Interference　plus　Noise　Ratio（ＳＩＮＲ）、Signal　to　Noise　Ratio（ＳＮＲ））、信号強度（例えば、Received　Signal　Strength　Indicator（ＲＳＳＩ））、伝搬路情報（例えば、ＣＳＩ）などについて測定してもよい。測定結果は、制御部１１０に出力されてもよい。 The transmitting/receiving unit 120 (measuring unit 123) may perform measurements regarding the received signal. For example, the measurement unit 123 may perform Radio Resource Management (RRM) measurement, Channel State Information (CSI) measurement, etc. based on the received signal. The measurement unit 123 is the receiving power (for example, Reference Signal Received Power (RSRP)), Receive Quality (eg, Reference Signal Received Quality (RSRQ), Signal To InterfERENCE PLUS NOI. SE RATIO (SINR), Signal to Noise Ratio (SNR) , signal strength (for example, Received Signal Strength Indicator (RSSI)), propagation path information (for example, CSI), etc. may be measured. The measurement results may be output to the control unit 110.
　伝送路インターフェース１４０は、コアネットワーク３０に含まれる装置（例えば、ＮＦを提供するネットワークノード）、他の基地局１０などとの間で信号を送受信（バックホールシグナリング）し、ユーザ端末２０のためのユーザデータ（ユーザプレーンデータ）、制御プレーンデータなどを取得、伝送などしてもよい。 The transmission path interface 140 transmits and receives signals (backhaul signaling) between devices included in the core network 30 (for example, network nodes providing NF), other base stations 10, etc., and provides information for the user terminal 20. User data (user plane data), control plane data, etc. may be acquired and transmitted.
　なお、本開示における基地局１０の送信部及び受信部は、送受信部１２０、送受信アンテナ１３０及び伝送路インターフェース１４０の少なくとも１つによって構成されてもよい。 Note that the transmitting unit and receiving unit of the base station 10 in the present disclosure may be configured by at least one of the transmitting/receiving unit 120, the transmitting/receiving antenna 130, and the transmission path interface 140.
　なお、送受信部１２０は、人工知能（Artificial　Intelligence（ＡＩ））ベースドビーム管理（Beam　Management（ＢＭ））に関して、性能モニタリングのための設定情報を送信してもよい。送受信部１２０は、前記性能モニタリングに基づいて送信される報告又はモデル要求を受信してもよい。 Note that the transmitting/receiving unit 120 may transmit setting information for performance monitoring regarding artificial intelligence (AI) based beam management (BM). The transmitting/receiving unit 120 may receive a report or a model request transmitted based on the performance monitoring.
　また、送受信部１２０は、人工知能（Artificial　Intelligence（ＡＩ））ベースドビーム管理（Beam　Management（ＢＭ））に関して、性能モニタリング向けの報告のための設定情報を送信してもよい。送受信部１２０は、前記設定情報に基づいて測定されるLayer　1　Reference　Signal　Received　Power（Ｌ１－ＲＳＲＰ）に関する報告を受信してもよい。 Additionally, the transmitting/receiving unit 120 may transmit setting information for reporting for performance monitoring regarding artificial intelligence (AI) based beam management (BM). The transmitting/receiving unit 120 may receive a report regarding Layer 1 Reference Signal Received Power (L1-RSRP) measured based on the configuration information.
（ユーザ端末）
　図１１は、一実施形態に係るユーザ端末の構成の一例を示す図である。ユーザ端末２０は、制御部２１０、送受信部２２０及び送受信アンテナ２３０を備えている。なお、制御部２１０、送受信部２２０及び送受信アンテナ２３０は、それぞれ１つ以上が備えられてもよい。 (user terminal)
FIG. 11 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment. The user terminal 20 includes a control section 210, a transmitting/receiving section 220, and a transmitting/receiving antenna 230. Note that one or more of each of the control unit 210, the transmitting/receiving unit 220, and the transmitting/receiving antenna 230 may be provided.
　なお、本例では、本実施の形態における特徴部分の機能ブロックを主に示しており、ユーザ端末２０は、無線通信に必要な他の機能ブロックも有すると想定されてもよい。以下で説明する各部の処理の一部は、省略されてもよい。 Note that this example mainly shows functional blocks that are characteristic of the present embodiment, and it may be assumed that the user terminal 20 also has other functional blocks necessary for wireless communication. A part of the processing of each unit described below may be omitted.
　制御部２１０は、ユーザ端末２０全体の制御を実施する。制御部２１０は、本開示に係る技術分野での共通認識に基づいて説明されるコントローラ、制御回路などから構成することができる。 The control unit 210 controls the entire user terminal 20. The control unit 210 can be configured from a controller, a control circuit, etc., which will be explained based on common recognition in the technical field related to the present disclosure.
　制御部２１０は、信号の生成、マッピングなどを制御してもよい。制御部２１０は、送受信部２２０及び送受信アンテナ２３０を用いた送受信、測定などを制御してもよい。制御部２１０は、信号として送信するデータ、制御情報、系列などを生成し、送受信部２２０に転送してもよい。 The control unit 210 may control signal generation, mapping, etc. The control unit 210 may control transmission and reception using the transmitting/receiving unit 220 and the transmitting/receiving antenna 230, measurement, and the like. The control unit 210 may generate data, control information, sequences, etc. to be transmitted as a signal, and may transfer the generated data to the transmitting/receiving unit 220.
　送受信部２２０は、ベースバンド部２２１、ＲＦ部２２２、測定部２２３を含んでもよい。ベースバンド部２２１は、送信処理部２２１１、受信処理部２２１２を含んでもよい。送受信部２２０は、本開示に係る技術分野での共通認識に基づいて説明されるトランスミッター／レシーバー、ＲＦ回路、ベースバンド回路、フィルタ、位相シフタ、測定回路、送受信回路などから構成することができる。 The transmitting/receiving section 220 may include a baseband section 221, an RF section 222, and a measuring section 223. The baseband section 221 may include a transmission processing section 2211 and a reception processing section 2212. The transmitting/receiving unit 220 can be configured from a transmitter/receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measuring circuit, a transmitting/receiving circuit, etc., which are explained based on common recognition in the technical field related to the present disclosure.
　送受信部２２０は、一体の送受信部として構成されてもよいし、送信部及び受信部から構成されてもよい。当該送信部は、送信処理部２２１１、ＲＦ部２２２から構成されてもよい。当該受信部は、受信処理部２２１２、ＲＦ部２２２、測定部２２３から構成されてもよい。 The transmitting/receiving section 220 may be configured as an integrated transmitting/receiving section, or may be configured from a transmitting section and a receiving section. The transmitting section may include a transmitting processing section 2211 and an RF section 222. The reception section may include a reception processing section 2212, an RF section 222, and a measurement section 223.
　送受信アンテナ２３０は、本開示に係る技術分野での共通認識に基づいて説明されるアンテナ、例えばアレイアンテナなどから構成することができる。 The transmitting/receiving antenna 230 can be configured from an antenna, such as an array antenna, as described based on common recognition in the technical field related to the present disclosure.
　送受信部２２０は、上述の下りリンクチャネル、同期信号、下りリンク参照信号などを受信してもよい。送受信部２２０は、上述の上りリンクチャネル、上りリンク参照信号などを送信してもよい。 The transmitter/receiver 220 may receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transmitter/receiver 220 may transmit the above-mentioned uplink channel, uplink reference signal, and the like.
　送受信部２２０は、デジタルビームフォーミング（例えば、プリコーディング）、アナログビームフォーミング（例えば、位相回転）などを用いて、送信ビーム及び受信ビームの少なくとも一方を形成してもよい。 The transmitting/receiving unit 220 may form at least one of a transmitting beam and a receiving beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.
　送受信部２２０（送信処理部２２１１）は、例えば制御部２１０から取得したデータ、制御情報などに対して、ＰＤＣＰレイヤの処理、ＲＬＣレイヤの処理（例えば、ＲＬＣ再送制御）、ＭＡＣレイヤの処理（例えば、ＨＡＲＱ再送制御）などを行い、送信するビット列を生成してもよい。 The transmission/reception unit 220 (transmission processing unit 2211) performs PDCP layer processing, RLC layer processing (e.g. RLC retransmission control), MAC layer processing (e.g. , HARQ retransmission control), etc., to generate a bit string to be transmitted.
　送受信部２２０（送信処理部２２１１）は、送信するビット列に対して、チャネル符号化（誤り訂正符号化を含んでもよい）、変調、マッピング、フィルタ処理、ＤＦＴ処理（必要に応じて）、ＩＦＦＴ処理、プリコーディング、デジタル－アナログ変換などの送信処理を行い、ベースバンド信号を出力してもよい。 The transmitting/receiving unit 220 (transmission processing unit 2211) performs channel encoding (which may include error correction encoding), modulation, mapping, filter processing, DFT processing (as necessary), and IFFT processing on the bit string to be transmitted. , precoding, digital-to-analog conversion, etc., and output a baseband signal.
　なお、ＤＦＴ処理を適用するか否かは、トランスフォームプリコーディングの設定に基づいてもよい。送受信部２２０（送信処理部２２１１）は、あるチャネル（例えば、ＰＵＳＣＨ）について、トランスフォームプリコーディングが有効（enabled）である場合、当該チャネルをＤＦＴ－ｓ－ＯＦＤＭ波形を用いて送信するために上記送信処理としてＤＦＴ処理を行ってもよいし、そうでない場合、上記送信処理としてＤＦＴ処理を行わなくてもよい。 Note that whether or not to apply DFT processing may be based on the settings of transform precoding. When transform precoding is enabled for a certain channel (for example, PUSCH), the transmitting/receiving unit 220 (transmission processing unit 2211) performs the above processing in order to transmit the channel using the DFT-s-OFDM waveform. DFT processing may be performed as the transmission processing, or if not, DFT processing may not be performed as the transmission processing.
　送受信部２２０（ＲＦ部２２２）は、ベースバンド信号に対して、無線周波数帯への変調、フィルタ処理、増幅などを行い、無線周波数帯の信号を、送受信アンテナ２３０を介して送信してもよい。 The transmitting/receiving unit 220 (RF unit 222) may perform modulation, filter processing, amplification, etc. on the baseband signal in a radio frequency band, and may transmit the signal in the radio frequency band via the transmitting/receiving antenna 230. .
　一方、送受信部２２０（ＲＦ部２２２）は、送受信アンテナ２３０によって受信された無線周波数帯の信号に対して、増幅、フィルタ処理、ベースバンド信号への復調などを行ってもよい。 On the other hand, the transmitting/receiving section 220 (RF section 222) may perform amplification, filter processing, demodulation into a baseband signal, etc. on the radio frequency band signal received by the transmitting/receiving antenna 230.
　送受信部２２０（受信処理部２２１２）は、取得されたベースバンド信号に対して、アナログ－デジタル変換、ＦＦＴ処理、ＩＤＦＴ処理（必要に応じて）、フィルタ処理、デマッピング、復調、復号（誤り訂正復号を含んでもよい）、ＭＡＣレイヤ処理、ＲＬＣレイヤの処理及びＰＤＣＰレイヤの処理などの受信処理を適用し、ユーザデータなどを取得してもよい。 The transmission/reception unit 220 (reception processing unit 2212) performs analog-to-digital conversion, FFT processing, IDFT processing (if necessary), filter processing, demapping, demodulation, and decoding (error correction) on the acquired baseband signal. (which may include decoding), MAC layer processing, RLC layer processing, and PDCP layer processing may be applied to obtain user data and the like.
　送受信部２２０（測定部２２３）は、受信した信号に関する測定を実施してもよい。例えば、測定部２２３は、受信した信号に基づいて、ＲＲＭ測定、ＣＳＩ測定などを行ってもよい。測定部２２３は、受信電力（例えば、ＲＳＲＰ）、受信品質（例えば、ＲＳＲＱ、ＳＩＮＲ、ＳＮＲ）、信号強度（例えば、ＲＳＳＩ）、伝搬路情報（例えば、ＣＳＩ）などについて測定してもよい。測定結果は、制御部２１０に出力されてもよい。 The transmitting/receiving unit 220 (measuring unit 223) may perform measurements regarding the received signal. For example, the measurement unit 223 may perform RRM measurement, CSI measurement, etc. based on the received signal. The measurement unit 223 may measure received power (for example, RSRP), reception quality (for example, RSRQ, SINR, SNR), signal strength (for example, RSSI), propagation path information (for example, CSI), and the like. The measurement results may be output to the control unit 210.
　なお、本開示におけるユーザ端末２０の送信部及び受信部は、送受信部２２０及び送受信アンテナ２３０の少なくとも１つによって構成されてもよい。 Note that the transmitting unit and receiving unit of the user terminal 20 in the present disclosure may be configured by at least one of the transmitting/receiving unit 220 and the transmitting/receiving antenna 230.
　なお、送受信部２２０は、人工知能（Artificial　Intelligence（ＡＩ））ベースドビーム管理（Beam　Management（ＢＭ））に関して、性能モニタリングのための設定情報を受信してもよい。制御部２１０は、前記性能モニタリングを制御してもよい。 Note that the transmitting/receiving unit 220 may receive setting information for performance monitoring regarding artificial intelligence (AI) based beam management (BM). The control unit 210 may control the performance monitoring.
　制御部２１０は、特定の対応関係にあるビーム予測のための参照信号リソースと性能モニタリングリファレンスのための参照信号とに基づいて、前記ＡＩベースドＢＭの性能をモニタしてもよい。 The control unit 210 may monitor the performance of the AI-based BM based on a reference signal resource for beam prediction and a reference signal for performance monitoring reference that have a specific correspondence relationship.
　制御部２１０は、ビーム予測のためのＣＳＩ－ＲＳ機会及び性能モニタリングリファレンスのためのＣＳＩ－ＲＳ機会の一方に基づいて他方を決定し、これらのＣＳＩ機会に基づいて、前記ＡＩベースドＢＭの性能をモニタしてもよい。 The control unit 210 determines the CSI-RS opportunity for beam prediction and the CSI-RS opportunity for performance monitoring reference based on the other, and determines the performance of the AI-based BM based on these CSI opportunities. May be monitored.
　また、送受信部２２０は、人工知能（Artificial　Intelligence（ＡＩ））ベースドビーム管理（Beam　Management（ＢＭ））に関して、性能モニタリング向けの報告のための設定情報を受信してもよい。制御部２１０は、前記設定情報に基づいて測定されるLayer　1　Reference　Signal　Received　Power（Ｌ１－ＲＳＲＰ）に関する報告の送信を制御してもよい。 The transmitting/receiving unit 220 may also receive setting information for reporting for performance monitoring regarding artificial intelligence (AI) based beam management (BM). The control unit 210 may control the transmission of a report regarding Layer 1 Reference Signal Received Power (L1-RSRP) measured based on the setting information.
　制御部２１０は、性能モニタリングリファレンスのために参照される参照信号機会より一定の単位時間遅くなく、又は一定の単位時間早くない予測時間に対応する予測Ｌ１－ＲＳＲＰを、前記報告に含めてもよい。 The control unit 210 may include in the report a predicted L1-RSRP corresponding to a predicted time that is not later than a predetermined unit time or not earlier than a predetermined unit time than a reference signal occasion referenced for the performance monitoring reference. .
（ハードウェア構成）
　なお、上記実施形態の説明に用いたブロック図は、機能単位のブロックを示している。これらの機能ブロック（構成部）は、ハードウェア及びソフトウェアの少なくとも一方の任意の組み合わせによって実現される。また、各機能ブロックの実現方法は特に限定されない。すなわち、各機能ブロックは、物理的又は論理的に結合した１つの装置を用いて実現されてもよいし、物理的又は論理的に分離した２つ以上の装置を直接的又は間接的に（例えば、有線、無線などを用いて）接続し、これら複数の装置を用いて実現されてもよい。機能ブロックは、上記１つの装置又は上記複数の装置にソフトウェアを組み合わせて実現されてもよい。 (Hardware configuration)
It should be noted that the block diagram used to explain the above embodiment shows blocks in functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using one physically or logically coupled device, or may be realized using two or more physically or logically separated devices directly or indirectly (e.g. , wired, wireless, etc.) and may be realized using a plurality of these devices. The functional block may be realized by combining software with the one device or the plurality of devices.
　ここで、機能には、判断、決定、判定、計算、算出、処理、導出、調査、探索、確認、受信、送信、出力、アクセス、解決、選択、選定、確立、比較、想定、期待、みなし、報知（broadcasting）、通知（notifying）、通信（communicating）、転送（forwarding）、構成（configuring）、再構成（reconfiguring）、割り当て（allocating、mapping）、割り振り（assigning）などがあるが、これらに限られない。例えば、送信を機能させる機能ブロック（構成部）は、送信部（transmitting　unit）、送信機（transmitter）などと呼称されてもよい。いずれも、上述したとおり、実現方法は特に限定されない。 Here, functions include judgment, decision, judgement, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, solution, selection, selection, establishment, comparison, assumption, expectation, and consideration. , broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, assigning, etc. Not limited. For example, a functional block (configuration unit) that performs transmission may be called a transmitting unit, a transmitter, or the like. In either case, as described above, the implementation method is not particularly limited.
　例えば、本開示の一実施形態における基地局、ユーザ端末などは、本開示の無線通信方法の処理を行うコンピュータとして機能してもよい。図１２は、一実施形態に係る基地局及びユーザ端末のハードウェア構成の一例を示す図である。上述の基地局１０及びユーザ端末２０は、物理的には、プロセッサ１００１、メモリ１００２、ストレージ１００３、通信装置１００４、入力装置１００５、出力装置１００６、バス１００７などを含むコンピュータ装置として構成されてもよい。 For example, a base station, a user terminal, etc. in an embodiment of the present disclosure may function as a computer that performs processing of the wireless communication method of the present disclosure. FIG. 12 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment. The base station 10 and user terminal 20 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc. .
　なお、本開示において、装置、回路、デバイス、部（section）、ユニットなどの文言は、互いに読み替えることができる。基地局１０及びユーザ端末２０のハードウェア構成は、図に示した各装置を１つ又は複数含むように構成されてもよいし、一部の装置を含まずに構成されてもよい。 Note that in this disclosure, words such as apparatus, circuit, device, section, unit, etc. can be read interchangeably. The hardware configuration of the base station 10 and the user terminal 20 may be configured to include one or more of each device shown in the figure, or may be configured not to include some of the devices.
　例えば、プロセッサ１００１は１つだけ図示されているが、複数のプロセッサがあってもよい。また、処理は、１のプロセッサによって実行されてもよいし、処理が同時に、逐次に、又はその他の手法を用いて、２以上のプロセッサによって実行されてもよい。なお、プロセッサ１００１は、１以上のチップによって実装されてもよい。 For example, although only one processor 1001 is illustrated, there may be multiple processors. Also, the processing may be performed by one processor, or the processing may be performed by two or more processors simultaneously, sequentially, or using other techniques. Note that the processor 1001 may be implemented using one or more chips.
　基地局１０及びユーザ端末２０における各機能は、例えば、プロセッサ１００１、メモリ１００２などのハードウェア上に所定のソフトウェア（プログラム）を読み込ませることによって、プロセッサ１００１が演算を行い、通信装置１００４を介する通信を制御したり、メモリ１００２及びストレージ１００３におけるデータの読み出し及び書き込みの少なくとも一方を制御したりすることによって実現される。 Each function in the base station 10 and the user terminal 20 is performed by, for example, loading predetermined software (program) onto hardware such as a processor 1001 and a memory 1002, so that the processor 1001 performs calculations and communicates via the communication device 1004. This is achieved by controlling at least one of reading and writing data in the memory 1002 and storage 1003.
　プロセッサ１００１は、例えば、オペレーティングシステムを動作させてコンピュータ全体を制御する。プロセッサ１００１は、周辺装置とのインターフェース、制御装置、演算装置、レジスタなどを含む中央処理装置（Central　Processing　Unit（ＣＰＵ））によって構成されてもよい。例えば、上述の制御部１１０（２１０）、送受信部１２０（２２０）などの少なくとも一部は、プロセッサ１００１によって実現されてもよい。 The processor 1001, for example, operates an operating system to control the entire computer. The processor 1001 may be configured by a central processing unit (CPU) that includes interfaces with peripheral devices, a control device, an arithmetic unit, registers, and the like. For example, at least a portion of the above-mentioned control unit 110 (210), transmitting/receiving unit 120 (220), etc. may be realized by the processor 1001.
　また、プロセッサ１００１は、プログラム（プログラムコード）、ソフトウェアモジュール、データなどを、ストレージ１００３及び通信装置１００４の少なくとも一方からメモリ１００２に読み出し、これらに従って各種の処理を実行する。プログラムとしては、上述の実施形態において説明した動作の少なくとも一部をコンピュータに実行させるプログラムが用いられる。例えば、制御部１１０（２１０）は、メモリ１００２に格納され、プロセッサ１００１において動作する制御プログラムによって実現されてもよく、他の機能ブロックについても同様に実現されてもよい。 Furthermore, the processor 1001 reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 to the memory 1002, and executes various processes in accordance with these. As the program, a program that causes a computer to execute at least part of the operations described in the above embodiments is used. For example, the control unit 110 (210) may be realized by a control program stored in the memory 1002 and operated in the processor 1001, and other functional blocks may also be realized in the same way.
　メモリ１００２は、コンピュータ読み取り可能な記録媒体であり、例えば、Read　Only　Memory（ＲＯＭ）、Erasable　Programmable　ROM（ＥＰＲＯＭ）、Electrically　EPROM（ＥＥＰＲＯＭ）、Random　Access　Memory（ＲＡＭ）、その他の適切な記憶媒体の少なくとも１つによって構成されてもよい。メモリ１００２は、レジスタ、キャッシュ、メインメモリ（主記憶装置）などと呼ばれてもよい。メモリ１００２は、本開示の一実施形態に係る無線通信方法を実施するために実行可能なプログラム（プログラムコード）、ソフトウェアモジュールなどを保存することができる。 The memory 1002 is a computer-readable recording medium, and includes at least one of Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically EPROM (EEPROM), Random Access Memory (RAM), and other suitable storage media. It may be composed of one. Memory 1002 may be called a register, cache, main memory, or the like. The memory 1002 can store executable programs (program codes), software modules, and the like to implement a wireless communication method according to an embodiment of the present disclosure.
　ストレージ１００３は、コンピュータ読み取り可能な記録媒体であり、例えば、フレキシブルディスク、フロッピー（登録商標）ディスク、光磁気ディスク（例えば、コンパクトディスク（Compact　Disc　ROM（ＣＤ－ＲＯＭ）など）、デジタル多用途ディスク、Ｂｌｕ－ｒａｙ（登録商標）ディスク）、リムーバブルディスク、ハードディスクドライブ、スマートカード、フラッシュメモリデバイス（例えば、カード、スティック、キードライブ）、磁気ストライプ、データベース、サーバ、その他の適切な記憶媒体の少なくとも１つによって構成されてもよい。ストレージ１００３は、補助記憶装置と呼ばれてもよい。 The storage 1003 is a computer-readable recording medium, such as a flexible disk, a floppy (registered trademark) disk, a magneto-optical disk (for example, a compact disk (CD-ROM), etc.), a digital versatile disk, removable disk, hard disk drive, smart card, flash memory device (e.g., card, stick, key drive), magnetic stripe, database, server, or other suitable storage medium. It may be configured by Storage 1003 may also be called an auxiliary storage device.
　通信装置１００４は、有線ネットワーク及び無線ネットワークの少なくとも一方を介してコンピュータ間の通信を行うためのハードウェア（送受信デバイス）であり、例えばネットワークデバイス、ネットワークコントローラ、ネットワークカード、通信モジュールなどともいう。通信装置１００４は、例えば周波数分割複信（Frequency　Division　Duplex（ＦＤＤ））及び時分割複信（Time　Division　Duplex（ＴＤＤ））の少なくとも一方を実現するために、高周波スイッチ、デュプレクサ、フィルタ、周波数シンセサイザなどを含んで構成されてもよい。例えば、上述の送受信部１２０（２２０）、送受信アンテナ１３０（２３０）などは、通信装置１００４によって実現されてもよい。送受信部１２０（２２０）は、送信部１２０ａ（２２０ａ）と受信部１２０ｂ（２２０ｂ）とで、物理的に又は論理的に分離された実装がなされてもよい。 The communication device 1004 is hardware (transmission/reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc., for example. The communication device 1004 includes, for example, a high frequency switch, a duplexer, a filter, a frequency synthesizer, etc. in order to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). It may be configured to include. For example, the above-described transmitting/receiving unit 120 (220), transmitting/receiving antenna 130 (230), etc. may be realized by the communication device 1004. The transmitter/receiver 120 (220) may be physically or logically separated into a transmitter 120a (220a) and a receiver 120b (220b).
　入力装置１００５は、外部からの入力を受け付ける入力デバイス（例えば、キーボード、マウス、マイクロフォン、スイッチ、ボタン、センサなど）である。出力装置１００６は、外部への出力を実施する出力デバイス（例えば、ディスプレイ、スピーカー、Light　Emitting　Diode（ＬＥＤ）ランプなど）である。なお、入力装置１００５及び出力装置１００６は、一体となった構成（例えば、タッチパネル）であってもよい。 The input device 1005 is an input device (eg, keyboard, mouse, microphone, switch, button, sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (for example, a display, a speaker, a light emitting diode (LED) lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may have an integrated configuration (for example, a touch panel).
　また、プロセッサ１００１、メモリ１００２などの各装置は、情報を通信するためのバス１００７によって接続される。バス１００７は、単一のバスを用いて構成されてもよいし、装置間ごとに異なるバスを用いて構成されてもよい。 Further, each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses for each device.
　また、基地局１０及びユーザ端末２０は、マイクロプロセッサ、デジタル信号プロセッサ（Digital　Signal　Processor（ＤＳＰ））、Application　Specific　Integrated　Circuit（ＡＳＩＣ）、Programmable　Logic　Device（ＰＬＤ）、Field　Programmable　Gate　Array（ＦＰＧＡ）などのハードウェアを含んで構成されてもよく、当該ハードウェアを用いて各機能ブロックの一部又は全てが実現されてもよい。例えば、プロセッサ１００１は、これらのハードウェアの少なくとも１つを用いて実装されてもよい。 The base station 10 and user terminal 20 also include a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. It may be configured to include hardware, and a part or all of each functional block may be realized using the hardware. For example, processor 1001 may be implemented using at least one of these hardwares.
（変形例）
　なお、本開示において説明した用語及び本開示の理解に必要な用語については、同一の又は類似する意味を有する用語と置き換えてもよい。例えば、チャネル、シンボル及び信号（シグナル又はシグナリング）は、互いに読み替えられてもよい。また、信号はメッセージであってもよい。参照信号（reference　signal）は、ＲＳと略称することもでき、適用される標準によってパイロット（Pilot）、パイロット信号などと呼ばれてもよい。また、コンポーネントキャリア（Component　Carrier（ＣＣ））は、セル、周波数キャリア、キャリア周波数などと呼ばれてもよい。 (Modified example)
Note that terms explained in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, channel, symbol and signal may be interchanged. Also, the signal may be a message. The reference signal may also be abbreviated as RS, and may be called a pilot, pilot signal, etc. depending on the applicable standard. Further, a component carrier (CC) may be called a cell, a frequency carrier, a carrier frequency, or the like.
　無線フレームは、時間領域において１つ又は複数の期間（フレーム）によって構成されてもよい。無線フレームを構成する当該１つ又は複数の各期間（フレーム）は、サブフレームと呼ばれてもよい。さらに、サブフレームは、時間領域において１つ又は複数のスロットによって構成されてもよい。サブフレームは、ニューメロロジー（numerology）に依存しない固定の時間長（例えば、１ｍｓ）であってもよい。 A radio frame may be composed of one or more periods (frames) in the time domain. Each of the one or more periods (frames) constituting a radio frame may be called a subframe. Furthermore, a subframe may be composed of one or more slots in the time domain. A subframe may have a fixed time length (eg, 1 ms) that does not depend on numerology.
　ここで、ニューメロロジーは、ある信号又はチャネルの送信及び受信の少なくとも一方に適用される通信パラメータであってもよい。ニューメロロジーは、例えば、サブキャリア間隔（SubCarrier　Spacing（ＳＣＳ））、帯域幅、シンボル長、サイクリックプレフィックス長、送信時間間隔（Transmission　Time　Interval（ＴＴＩ））、ＴＴＩあたりのシンボル数、無線フレーム構成、送受信機が周波数領域において行う特定のフィルタリング処理、送受信機が時間領域において行う特定のウィンドウイング処理などの少なくとも１つを示してもよい。 Here, the numerology may be a communication parameter applied to at least one of transmission and reception of a certain signal or channel. Numerology includes, for example, subcarrier spacing (SCS), bandwidth, symbol length, cyclic prefix length, transmission time interval (TTI), number of symbols per TTI, and radio frame structure. , a specific filtering process performed by the transceiver in the frequency domain, a specific windowing process performed by the transceiver in the time domain, etc.
　スロットは、時間領域において１つ又は複数のシンボル（Orthogonal　Frequency　Division　Multiplexing（ＯＦＤＭ）シンボル、Single　Carrier　Frequency　Division　Multiple　Access（ＳＣ－ＦＤＭＡ）シンボルなど）によって構成されてもよい。また、スロットは、ニューメロロジーに基づく時間単位であってもよい。 A slot may be composed of one or more symbols (Orthogonal Frequency Division Multiplexing (OFDM) symbols, Single Carrier Frequency Division Multiple Access (SC-FDMA) symbols, etc.) in the time domain. Furthermore, a slot may be a time unit based on numerology.
　スロットは、複数のミニスロットを含んでもよい。各ミニスロットは、時間領域において１つ又は複数のシンボルによって構成されてもよい。また、ミニスロットは、サブスロットと呼ばれてもよい。ミニスロットは、スロットよりも少ない数のシンボルによって構成されてもよい。ミニスロットより大きい時間単位で送信されるＰＤＳＣＨ（又はＰＵＳＣＨ）は、ＰＤＳＣＨ（ＰＵＳＣＨ）マッピングタイプＡと呼ばれてもよい。ミニスロットを用いて送信されるＰＤＳＣＨ（又はＰＵＳＣＨ）は、ＰＤＳＣＨ（ＰＵＳＣＨ）マッピングタイプＢと呼ばれてもよい。 A slot may include multiple mini-slots. Each minislot may be made up of one or more symbols in the time domain. Furthermore, a mini-slot may also be called a sub-slot. A minislot may be made up of fewer symbols than a slot. PDSCH (or PUSCH) transmitted in time units larger than minislots may be referred to as PDSCH (PUSCH) mapping type A. PDSCH (or PUSCH) transmitted using minislots may be referred to as PDSCH (PUSCH) mapping type B.
　無線フレーム、サブフレーム、スロット、ミニスロット及びシンボルは、いずれも信号を伝送する際の時間単位を表す。無線フレーム、サブフレーム、スロット、ミニスロット及びシンボルは、それぞれに対応する別の呼称が用いられてもよい。なお、本開示におけるフレーム、サブフレーム、スロット、ミニスロット、シンボルなどの時間単位は、互いに読み替えられてもよい。 Radio frames, subframes, slots, minislots, and symbols all represent time units when transmitting signals. Other names may be used for the radio frame, subframe, slot, minislot, and symbol. Note that time units such as frames, subframes, slots, minislots, and symbols in the present disclosure may be read interchangeably.
　例えば、１サブフレームはＴＴＩと呼ばれてもよいし、複数の連続したサブフレームがＴＴＩと呼ばれてよいし、１スロット又は１ミニスロットがＴＴＩと呼ばれてもよい。つまり、サブフレーム及びＴＴＩの少なくとも一方は、既存のＬＴＥにおけるサブフレーム（１ｍｓ）であってもよいし、１ｍｓより短い期間（例えば、１－１３シンボル）であってもよいし、１ｍｓより長い期間であってもよい。なお、ＴＴＩを表す単位は、サブフレームではなくスロット、ミニスロットなどと呼ばれてもよい。 For example, one subframe may be called a TTI, a plurality of consecutive subframes may be called a TTI, and one slot or one minislot may be called a TTI. In other words, at least one of the subframe and TTI may be a subframe (1ms) in existing LTE, a period shorter than 1ms (for example, 1-13 symbols), or a period longer than 1ms. It may be. Note that the unit representing the TTI may be called a slot, minislot, etc. instead of a subframe.
　ここで、ＴＴＩは、例えば、無線通信におけるスケジューリングの最小時間単位のことをいう。例えば、ＬＴＥシステムでは、基地局が各ユーザ端末に対して、無線リソース（各ユーザ端末において使用することが可能な周波数帯域幅、送信電力など）を、ＴＴＩ単位で割り当てるスケジューリングを行う。なお、ＴＴＩの定義はこれに限られない。 Here, TTI refers to, for example, the minimum time unit for scheduling in wireless communication. For example, in the LTE system, a base station performs scheduling to allocate radio resources (frequency bandwidth, transmission power, etc. that can be used by each user terminal) to each user terminal on a TTI basis. Note that the definition of TTI is not limited to this.
　ＴＴＩは、チャネル符号化されたデータパケット（トランスポートブロック）、コードブロック、コードワードなどの送信時間単位であってもよいし、スケジューリング、リンクアダプテーションなどの処理単位となってもよい。なお、ＴＴＩが与えられたとき、実際にトランスポートブロック、コードブロック、コードワードなどがマッピングされる時間区間（例えば、シンボル数）は、当該ＴＴＩよりも短くてもよい。 The TTI may be a transmission time unit of a channel-coded data packet (transport block), a code block, a codeword, etc., or may be a processing unit of scheduling, link adaptation, etc. Note that when a TTI is given, the time interval (for example, the number of symbols) to which transport blocks, code blocks, code words, etc. are actually mapped may be shorter than the TTI.
　なお、１スロット又は１ミニスロットがＴＴＩと呼ばれる場合、１以上のＴＴＩ（すなわち、１以上のスロット又は１以上のミニスロット）が、スケジューリングの最小時間単位となってもよい。また、当該スケジューリングの最小時間単位を構成するスロット数（ミニスロット数）は制御されてもよい。 Note that when one slot or one minislot is called a TTI, one or more TTIs (that is, one or more slots or one or more minislots) may be the minimum time unit for scheduling. Further, the number of slots (minislot number) that constitutes the minimum time unit of the scheduling may be controlled.
　１ｍｓの時間長を有するＴＴＩは、通常ＴＴＩ（３ＧＰＰ　Ｒｅｌ．８－１２におけるＴＴＩ）、ノーマルＴＴＩ、ロングＴＴＩ、通常サブフレーム、ノーマルサブフレーム、ロングサブフレーム、スロットなどと呼ばれてもよい。通常ＴＴＩより短いＴＴＩは、短縮ＴＴＩ、ショートＴＴＩ、部分ＴＴＩ（partial又はfractional　TTI）、短縮サブフレーム、ショートサブフレーム、ミニスロット、サブスロット、スロットなどと呼ばれてもよい。 A TTI having a time length of 1 ms may be called a normal TTI (TTI in 3GPP Rel. 8-12), normal TTI, long TTI, normal subframe, normal subframe, long subframe, slot, etc. A TTI that is shorter than the normal TTI may be referred to as an abbreviated TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, minislot, subslot, slot, etc.
　なお、ロングＴＴＩ（例えば、通常ＴＴＩ、サブフレームなど）は、１ｍｓを超える時間長を有するＴＴＩで読み替えてもよいし、ショートＴＴＩ（例えば、短縮ＴＴＩなど）は、ロングＴＴＩのＴＴＩ長未満かつ１ｍｓ以上のＴＴＩ長を有するＴＴＩで読み替えてもよい。 Note that long TTI (for example, normal TTI, subframe, etc.) may be read as TTI with a time length exceeding 1 ms, and short TTI (for example, short TTI, etc.) It may also be read as a TTI having the above TTI length.
　リソースブロック（Resource　Block（ＲＢ））は、時間領域及び周波数領域のリソース割当単位であり、周波数領域において、１つ又は複数個の連続した副搬送波（サブキャリア（subcarrier））を含んでもよい。ＲＢに含まれるサブキャリアの数は、ニューメロロジーに関わらず同じであってもよく、例えば１２であってもよい。ＲＢに含まれるサブキャリアの数は、ニューメロロジーに基づいて決定されてもよい。 A resource block (RB) is a resource allocation unit in the time domain and frequency domain, and may include one or more continuous subcarriers (subcarriers) in the frequency domain. The number of subcarriers included in an RB may be the same regardless of the numerology, and may be 12, for example. The number of subcarriers included in an RB may be determined based on numerology.
　また、ＲＢは、時間領域において、１つ又は複数個のシンボルを含んでもよく、１スロット、１ミニスロット、１サブフレーム又は１ＴＴＩの長さであってもよい。１ＴＴＩ、１サブフレームなどは、それぞれ１つ又は複数のリソースブロックによって構成されてもよい。 Additionally, an RB may include one or more symbols in the time domain, and may have a length of one slot, one minislot, one subframe, or one TTI. One TTI, one subframe, etc. may each be composed of one or more resource blocks.
　なお、１つ又は複数のＲＢは、物理リソースブロック（Physical　RB（ＰＲＢ））、サブキャリアグループ（Sub-Carrier　Group（ＳＣＧ））、リソースエレメントグループ（Resource　Element　Group（ＲＥＧ））、ＰＲＢペア、ＲＢペアなどと呼ばれてもよい。 Note that one or more RBs include a physical resource block (Physical RB (PRB)), a sub-carrier group (SCG), a resource element group (REG), a PRB pair, and an RB. They may also be called pairs.
　また、リソースブロックは、１つ又は複数のリソースエレメント（Resource　Element（ＲＥ））によって構成されてもよい。例えば、１ＲＥは、１サブキャリア及び１シンボルの無線リソース領域であってもよい。 Additionally, a resource block may be configured by one or more resource elements (REs). For example, 1 RE may be a radio resource region of 1 subcarrier and 1 symbol.
　帯域幅部分（Bandwidth　Part（ＢＷＰ））（部分帯域幅などと呼ばれてもよい）は、あるキャリアにおいて、あるニューメロロジー用の連続する共通ＲＢ（common　resource　blocks）のサブセットのことを表してもよい。ここで、共通ＲＢは、当該キャリアの共通参照ポイントを基準としたＲＢのインデックスによって特定されてもよい。ＰＲＢは、あるＢＷＰで定義され、当該ＢＷＰ内で番号付けされてもよい。 Bandwidth Part (BWP) (also called partial bandwidth, etc.) refers to a subset of consecutive common resource blocks (RB) for a certain numerology in a certain carrier. Good too. Here, the common RB may be specified by an RB index based on a common reference point of the carrier. PRBs may be defined in a BWP and numbered within that BWP.
　ＢＷＰには、ＵＬ　ＢＷＰ（ＵＬ用のＢＷＰ）と、ＤＬ　ＢＷＰ（ＤＬ用のＢＷＰ）とが含まれてもよい。ＵＥに対して、１キャリア内に１つ又は複数のＢＷＰが設定されてもよい。 BWP may include UL BWP (BWP for UL) and DL BWP (BWP for DL). One or more BWPs may be configured within one carrier for a UE.
　設定されたＢＷＰの少なくとも１つがアクティブであってもよく、ＵＥは、アクティブなＢＷＰの外で所定の信号／チャネルを送受信することを想定しなくてもよい。なお、本開示における「セル」、「キャリア」などは、「ＢＷＰ」で読み替えられてもよい。 At least one of the configured BWPs may be active and the UE may not expect to transmit or receive a given signal/channel outside of the active BWP. Note that "cell", "carrier", etc. in the present disclosure may be replaced with "BWP".
　なお、上述した無線フレーム、サブフレーム、スロット、ミニスロット及びシンボルなどの構造は例示に過ぎない。例えば、無線フレームに含まれるサブフレームの数、サブフレーム又は無線フレームあたりのスロットの数、スロット内に含まれるミニスロットの数、スロット又はミニスロットに含まれるシンボル及びＲＢの数、ＲＢに含まれるサブキャリアの数、並びにＴＴＩ内のシンボル数、シンボル長、サイクリックプレフィックス（Cyclic　Prefix（ＣＰ））長などの構成は、様々に変更することができる。 Note that the structures of the radio frame, subframe, slot, minislot, symbol, etc. described above are merely examples. For example, the number of subframes included in a radio frame, the number of slots per subframe or radio frame, the number of minislots included in a slot, the number of symbols and RBs included in a slot or minislot, the number of symbols included in an RB, The number of subcarriers, the number of symbols within a TTI, the symbol length, the cyclic prefix (CP) length, and other configurations can be changed in various ways.
　また、本開示において説明した情報、パラメータなどは、絶対値を用いて表されてもよいし、所定の値からの相対値を用いて表されてもよいし、対応する別の情報を用いて表されてもよい。例えば、無線リソースは、所定のインデックスによって指示されてもよい。 In addition, the information, parameters, etc. described in this disclosure may be expressed using absolute values, relative values from a predetermined value, or using other corresponding information. may be expressed. For example, radio resources may be indicated by a predetermined index.
　本開示においてパラメータなどに使用する名称は、いかなる点においても限定的な名称ではない。さらに、これらのパラメータを使用する数式などは、本開示において明示的に開示したものと異なってもよい。様々なチャネル（ＰＵＣＣＨ、ＰＤＣＣＨなど）及び情報要素は、あらゆる好適な名称によって識別できるので、これらの様々なチャネル及び情報要素に割り当てている様々な名称は、いかなる点においても限定的な名称ではない。 The names used for parameters and the like in this disclosure are not limiting in any respect. Furthermore, the mathematical formulas etc. using these parameters may differ from those explicitly disclosed in this disclosure. Since the various channels (PUCCH, PDCCH, etc.) and information elements can be identified by any suitable designation, the various names assigned to these various channels and information elements are not in any way exclusive designations. .
　本開示において説明した情報、信号などは、様々な異なる技術のいずれかを使用して表されてもよい。例えば、上記の説明全体に渡って言及され得るデータ、命令、コマンド、情報、信号、ビット、シンボル、チップなどは、電圧、電流、電磁波、磁界若しくは磁性粒子、光場若しくは光子、又はこれらの任意の組み合わせによって表されてもよい。 The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc., which may be referred to throughout the above description, may refer to voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any of these. It may also be represented by a combination of
　また、情報、信号などは、上位レイヤから下位レイヤ及び下位レイヤから上位レイヤの少なくとも一方へ出力され得る。情報、信号などは、複数のネットワークノードを介して入出力されてもよい。 Additionally, information, signals, etc. may be output from the upper layer to the lower layer and from the lower layer to at least one of the upper layer. Information, signals, etc. may be input and output via multiple network nodes.
　入出力された情報、信号などは、特定の場所（例えば、メモリ）に保存されてもよいし、管理テーブルを用いて管理してもよい。入出力される情報、信号などは、上書き、更新又は追記をされ得る。出力された情報、信号などは、削除されてもよい。入力された情報、信号などは、他の装置へ送信されてもよい。 Input/output information, signals, etc. may be stored in a specific location (for example, memory) or may be managed using a management table. Information, signals, etc. that are input and output can be overwritten, updated, or added. The output information, signals, etc. may be deleted. The input information, signals, etc. may be transmitted to other devices.
　情報の通知は、本開示において説明した態様／実施形態に限られず、他の方法を用いて行われてもよい。例えば、本開示における情報の通知は、物理レイヤシグナリング（例えば、下り制御情報（Downlink　Control　Information（ＤＣＩ））、上り制御情報（Uplink　Control　Information（ＵＣＩ）））、上位レイヤシグナリング（例えば、Radio　Resource　Control（ＲＲＣ）シグナリング、ブロードキャスト情報（マスタ情報ブロック（Master　Information　Block（ＭＩＢ））、システム情報ブロック（System　Information　Block（ＳＩＢ））など）、Medium　Access　Control（ＭＡＣ）シグナリング）、その他の信号又はこれらの組み合わせによって実施されてもよい。 Notification of information is not limited to the aspects/embodiments described in this disclosure, and may be performed using other methods. For example, the notification of information in this disclosure may be physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), upper layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB), etc.), Medium Access Control (MAC) signaling), other signals, or a combination thereof It may be carried out by
　なお、物理レイヤシグナリングは、Layer　1／Layer　2（Ｌ１／Ｌ２）制御情報（Ｌ１／Ｌ２制御信号）、Ｌ１制御情報（Ｌ１制御信号）などと呼ばれてもよい。また、ＲＲＣシグナリングは、ＲＲＣメッセージと呼ばれてもよく、例えば、ＲＲＣ接続セットアップ（RRC　Connection　Setup）メッセージ、ＲＲＣ接続再構成（RRC　Connection　Reconfiguration）メッセージなどであってもよい。また、ＭＡＣシグナリングは、例えば、ＭＡＣ制御要素（ＭＡＣ　Control　Element（ＣＥ））を用いて通知されてもよい。 Note that the physical layer signaling may also be called Layer 1/Layer 2 (L1/L2) control information (L1/L2 control signal), L1 control information (L1 control signal), etc. Further, RRC signaling may be called an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like. Further, MAC signaling may be notified using, for example, a MAC Control Element (CE).
　また、所定の情報の通知（例えば、「Ｘであること」の通知）は、明示的な通知に限られず、暗示的に（例えば、当該所定の情報の通知を行わないことによって又は別の情報の通知によって）行われてもよい。 Further, notification of prescribed information (for example, notification of "X") is not limited to explicit notification, but may be made implicitly (for example, by not notifying the prescribed information or by providing other information) (by notification).
　判定は、１ビットで表される値（０か１か）によって行われてもよいし、真（true）又は偽（false）で表される真偽値（boolean）によって行われてもよいし、数値の比較（例えば、所定の値との比較）によって行われてもよい。 The determination may be made by a value expressed by 1 bit (0 or 1), or by a boolean value expressed by true or false. , may be performed by numerical comparison (for example, comparison with a predetermined value).
　ソフトウェアは、ソフトウェア、ファームウェア、ミドルウェア、マイクロコード、ハードウェア記述言語と呼ばれるか、他の名称で呼ばれるかを問わず、命令、命令セット、コード、コードセグメント、プログラムコード、プログラム、サブプログラム、ソフトウェアモジュール、アプリケーション、ソフトウェアアプリケーション、ソフトウェアパッケージ、ルーチン、サブルーチン、オブジェクト、実行可能ファイル、実行スレッド、手順、機能などを意味するよう広く解釈されるべきである。 Software includes instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, whether referred to as software, firmware, middleware, microcode, hardware description language, or by any other name. , should be broadly construed to mean an application, software application, software package, routine, subroutine, object, executable, thread of execution, procedure, function, etc.
　また、ソフトウェア、命令、情報などは、伝送媒体を介して送受信されてもよい。例えば、ソフトウェアが、有線技術（同軸ケーブル、光ファイバケーブル、ツイストペア、デジタル加入者回線（Digital　Subscriber　Line（ＤＳＬ））など）及び無線技術（赤外線、マイクロ波など）の少なくとも一方を使用してウェブサイト、サーバ、又は他のリモートソースから送信される場合、これらの有線技術及び無線技術の少なくとも一方は、伝送媒体の定義内に含まれる。 Additionally, software, instructions, information, etc. may be sent and received via a transmission medium. For example, if the software uses wired technology (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.) and/or wireless technology (such as infrared, microwave, etc.) to , a server, or other remote source, these wired and/or wireless technologies are included within the definition of a transmission medium.
　本開示において使用する「システム」及び「ネットワーク」という用語は、互換的に使用され得る。「ネットワーク」は、ネットワークに含まれる装置（例えば、基地局）のことを意味してもよい。 The terms "system" and "network" used in this disclosure may be used interchangeably. "Network" may refer to devices (eg, base stations) included in the network.
　本開示において、「プリコーディング」、「プリコーダ」、「ウェイト（プリコーディングウェイト）」、「擬似コロケーション（Quasi-Co-Location（ＱＣＬ））」、「Transmission　Configuration　Indication　state（ＴＣＩ状態）」、「空間関係（spatial　relation）」、「空間ドメインフィルタ（spatial　domain　filter）」、「送信電力」、「位相回転」、「アンテナポート」、「アンテナポートグル－プ」、「レイヤ」、「レイヤ数」、「ランク」、「リソース」、「リソースセット」、「リソースグループ」、「ビーム」、「ビーム幅」、「ビーム角度」、「アンテナ」、「アンテナ素子」、「パネル」などの用語は、互換的に使用され得る。 In this disclosure, "precoding", "precoder", "weight (precoding weight)", "quasi-co-location (QCL)", "Transmission Configuration Indication state (TCI state)", "space "spatial relation", "spatial domain filter", "transmission power", "phase rotation", "antenna port", "antenna port group", "layer", "number of layers", Terms such as "rank", "resource", "resource set", "resource group", "beam", "beam width", "beam angle", "antenna", "antenna element", and "panel" are interchangeable. can be used.
　本開示においては、「基地局（Base　Station（ＢＳ））」、「無線基地局」、「固定局（fixed　station）」、「ＮｏｄｅＢ」、「ｅＮＢ（ｅＮｏｄｅＢ）」、「ｇＮＢ（ｇＮｏｄｅＢ）」、「アクセスポイント（access　point）」、「送信ポイント（Transmission　Point（ＴＰ））」、「受信ポイント（Reception　Point（ＲＰ））」、「送受信ポイント（Transmission/Reception　Point（ＴＲＰ））」、「パネル」、「セル」、「セクタ」、「セルグループ」、「キャリア」、「コンポーネントキャリア」などの用語は、互換的に使用され得る。基地局は、マクロセル、スモールセル、フェムトセル、ピコセルなどの用語で呼ばれる場合もある。 In the present disclosure, "Base Station (BS)", "Wireless base station", "Fixed station", "NodeB", "eNB (eNodeB)", "gNB (gNodeB)", "Access point", "Transmission Point (TP)", "Reception Point (RP)", "Transmission/Reception Point (TRP)", "Panel" , "cell," "sector," "cell group," "carrier," "component carrier," and the like may be used interchangeably. A base station is sometimes referred to by terms such as macrocell, small cell, femtocell, and picocell.
　基地局は、１つ又は複数（例えば、３つ）のセルを収容することができる。基地局が複数のセルを収容する場合、基地局のカバレッジエリア全体は複数のより小さいエリアに区分でき、各々のより小さいエリアは、基地局サブシステム（例えば、屋内用の小型基地局（Remote　Radio　Head（ＲＲＨ）））によって通信サービスを提供することもできる。「セル」又は「セクタ」という用語は、このカバレッジにおいて通信サービスを行う基地局及び基地局サブシステムの少なくとも一方のカバレッジエリアの一部又は全体を指す。 A base station can accommodate one or more (eg, three) cells. If a base station accommodates multiple cells, the overall coverage area of the base station can be partitioned into multiple smaller areas, and each smaller area is connected to a base station subsystem (e.g., an indoor small base station (Remote Radio Communication services can also be provided by the Head (RRH)). The term "cell" or "sector" refers to part or all of the coverage area of a base station and/or base station subsystem that provides communication services in this coverage.
　本開示において、基地局が端末に情報を送信することは、当該基地局が当該端末に対して、当該情報に基づく制御／動作を指示することと、互いに読み替えられてもよい。 In the present disclosure, a base station transmitting information to a terminal may be interchanged with the base station instructing the terminal to control/operate based on the information.
　本開示においては、「移動局（Mobile　Station（ＭＳ））」、「ユーザ端末（user　terminal）」、「ユーザ装置（User　Equipment（ＵＥ））」、「端末」などの用語は、互換的に使用され得る。 In this disclosure, terms such as "Mobile Station (MS)," "user terminal," "User Equipment (UE)," and "terminal" are used interchangeably. can be done.
　移動局は、加入者局、モバイルユニット、加入者ユニット、ワイヤレスユニット、リモートユニット、モバイルデバイス、ワイヤレスデバイス、ワイヤレス通信デバイス、リモートデバイス、モバイル加入者局、アクセス端末、モバイル端末、ワイヤレス端末、リモート端末、ハンドセット、ユーザエージェント、モバイルクライアント、クライアント又はいくつかの他の適切な用語で呼ばれる場合もある。 A mobile station is a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal. , handset, user agent, mobile client, client, or some other suitable terminology.
　基地局及び移動局の少なくとも一方は、送信装置、受信装置、無線通信装置などと呼ばれてもよい。なお、基地局及び移動局の少なくとも一方は、移動体（moving　object）に搭載されたデバイス、移動体自体などであってもよい。 At least one of a base station and a mobile station may be called a transmitting device, a receiving device, a wireless communication device, etc. Note that at least one of the base station and the mobile station may be a device mounted on a moving object, the moving object itself, or the like.
　当該移動体は、移動可能な物体をいい、移動速度は任意であり、移動体が停止している場合も当然含む。当該移動体は、例えば、車両、輸送車両、自動車、自動二輪車、自転車、コネクテッドカー、ショベルカー、ブルドーザー、ホイールローダー、ダンプトラック、フォークリフト、列車、バス、リヤカー、人力車、船舶（ship　and　other　watercraft）、飛行機、ロケット、人工衛星、ドローン、マルチコプター、クアッドコプター、気球及びこれらに搭載される物を含み、またこれらに限られない。また、当該移動体は、運行指令に基づいて自律走行する移動体であってもよい。 The moving body refers to a movable object, and the moving speed is arbitrary, and naturally includes cases where the moving body is stopped. The mobile objects include, for example, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, carts, rickshaws, and ships (ships and other watercraft). , including, but not limited to, airplanes, rockets, artificial satellites, drones, multicopters, quadcopters, balloons, and items mounted thereon. Furthermore, the mobile object may be a mobile object that autonomously travels based on a travel command.
　当該移動体は、乗り物（例えば、車、飛行機など）であってもよいし、無人で動く移動体（例えば、ドローン、自動運転車など）であってもよいし、ロボット（有人型又は無人型）であってもよい。なお、基地局及び移動局の少なくとも一方は、必ずしも通信動作時に移動しない装置も含む。例えば、基地局及び移動局の少なくとも一方は、センサなどのInternet　of　Things（ＩｏＴ）機器であってもよい。 The moving object may be a vehicle (for example, a car, an airplane, etc.), an unmanned moving object (for example, a drone, a self-driving car, etc.), or a robot (manned or unmanned). ). Note that at least one of the base station and the mobile station includes devices that do not necessarily move during communication operations. For example, at least one of the base station and the mobile station may be an Internet of Things (IoT) device such as a sensor.
　図１３は、一実施形態に係る車両の一例を示す図である。車両４０は、駆動部４１、操舵部４２、アクセルペダル４３、ブレーキペダル４４、シフトレバー４５、左右の前輪４６、左右の後輪４７、車軸４８、電子制御部４９、各種センサ（電流センサ５０、回転数センサ５１、空気圧センサ５２、車速センサ５３、加速度センサ５４、アクセルペダルセンサ５５、ブレーキペダルセンサ５６、シフトレバーセンサ５７、及び物体検知センサ５８を含む）、情報サービス部５９と通信モジュール６０を備える。 FIG. 13 is a diagram illustrating an example of a vehicle according to an embodiment. The vehicle 40 includes a drive unit 41, a steering unit 42, an accelerator pedal 43, a brake pedal 44, a shift lever 45, left and right front wheels 46, left and right rear wheels 47, an axle 48, an electronic control unit 49, various sensors (current sensor 50, (including a rotation speed sensor 51, an air pressure sensor 52, a vehicle speed sensor 53, an acceleration sensor 54, an accelerator pedal sensor 55, a brake pedal sensor 56, a shift lever sensor 57, and an object detection sensor 58), an information service section 59, and a communication module 60. Be prepared.
　駆動部４１は、例えば、エンジン、モータ、エンジンとモータのハイブリッドの少なくとも１つで構成される。操舵部４２は、少なくともステアリングホイール（ハンドルとも呼ぶ）を含み、ユーザによって操作されるステアリングホイールの操作に基づいて前輪４６及び後輪４７の少なくとも一方を操舵するように構成される。 The drive unit 41 is composed of, for example, at least one of an engine, a motor, and a hybrid of an engine and a motor. The steering unit 42 includes at least a steering wheel (also referred to as a steering wheel), and is configured to steer at least one of the front wheels 46 and the rear wheels 47 based on the operation of the steering wheel operated by the user.
　電子制御部４９は、マイクロプロセッサ６１、メモリ（ＲＯＭ、ＲＡＭ）６２、通信ポート（例えば、入出力（Input/Output（ＩＯ））ポート）６３で構成される。電子制御部４９には、車両に備えられた各種センサ５０－５８からの信号が入力される。電子制御部４９は、Electronic　Control　Unit（ＥＣＵ）と呼ばれてもよい。 The electronic control unit 49 includes a microprocessor 61, a memory (ROM, RAM) 62, and a communication port (for example, an input/output (IO) port) 63. Signals from various sensors 50-58 provided in the vehicle are input to the electronic control unit 49. The electronic control section 49 may be called an electronic control unit (ECU).
　各種センサ５０－５８からの信号としては、モータの電流をセンシングする電流センサ５０からの電流信号、回転数センサ５１によって取得された前輪４６／後輪４７の回転数信号、空気圧センサ５２によって取得された前輪４６／後輪４７の空気圧信号、車速センサ５３によって取得された車速信号、加速度センサ５４によって取得された加速度信号、アクセルペダルセンサ５５によって取得されたアクセルペダル４３の踏み込み量信号、ブレーキペダルセンサ５６によって取得されたブレーキペダル４４の踏み込み量信号、シフトレバーセンサ５７によって取得されたシフトレバー４５の操作信号、物体検知センサ５８によって取得された障害物、車両、歩行者などを検出するための検出信号などがある。 The signals from the various sensors 50 to 58 include a current signal from the current sensor 50 that senses the current of the motor, a rotation speed signal of the front wheel 46/rear wheel 47 obtained by the rotation speed sensor 51, and a signal obtained by the air pressure sensor 52. air pressure signals of the front wheels 46/rear wheels 47, a vehicle speed signal acquired by the vehicle speed sensor 53, an acceleration signal acquired by the acceleration sensor 54, a depression amount signal of the accelerator pedal 43 acquired by the accelerator pedal sensor 55, and a brake pedal sensor. 56, a shift lever 45 operation signal obtained by the shift lever sensor 57, and an object detection sensor 58 for detecting obstacles, vehicles, pedestrians, etc. There are signals etc.
　情報サービス部５９は、カーナビゲーションシステム、オーディオシステム、スピーカー、ディスプレイ、テレビ、ラジオ、といった、運転情報、交通情報、エンターテイメント情報などの各種情報を提供（出力）するための各種機器と、これらの機器を制御する１つ以上のＥＣＵとから構成される。情報サービス部５９は、外部装置から通信モジュール６０などを介して取得した情報を利用して、車両４０の乗員に各種情報／サービス（例えば、マルチメディア情報／マルチメディアサービス）を提供する。 The information service department 59 includes various devices such as car navigation systems, audio systems, speakers, displays, televisions, and radios that provide (output) various information such as driving information, traffic information, and entertainment information, and these devices. It consists of one or more ECUs that control the The information service unit 59 provides various information/services (for example, multimedia information/multimedia services) to the occupants of the vehicle 40 using information acquired from an external device via the communication module 60 or the like.
　情報サービス部５９は、外部からの入力を受け付ける入力デバイス（例えば、キーボード、マウス、マイクロフォン、スイッチ、ボタン、センサ、タッチパネルなど）を含んでもよいし、外部への出力を実施する出力デバイス（例えば、ディスプレイ、スピーカー、ＬＥＤランプ、タッチパネルなど）を含んでもよい。 The information service unit 59 may include an input device (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, a touch panel, etc.) that accepts input from the outside, and an output device that performs output to the outside (for example, display, speaker, LED lamp, touch panel, etc.).
　運転支援システム部６４は、ミリ波レーダ、Light　Detection　and　Ranging（ＬｉＤＡＲ）、カメラ、測位ロケータ（例えば、Global　Navigation　Satellite　System（ＧＮＳＳ）など）、地図情報（例えば、高精細（High　Definition（ＨＤ））マップ、自動運転車（Autonomous　Vehicle（ＡＶ））マップなど）、ジャイロシステム（例えば、慣性計測装置（Inertial　Measurement　Unit（ＩＭＵ））、慣性航法装置（Inertial　Navigation　System（ＩＮＳ））など）、人工知能（Artificial　Intelligence（ＡＩ））チップ、ＡＩプロセッサといった、事故を未然に防止したりドライバの運転負荷を軽減したりするための機能を提供するための各種機器と、これらの機器を制御する１つ以上のＥＣＵとから構成される。また、運転支援システム部６４は、通信モジュール６０を介して各種情報を送受信し、運転支援機能又は自動運転機能を実現する。 The driving support system unit 64 includes millimeter wave radar, Light Detection and Ranging (LiDAR), a camera, a positioning locator (for example, Global Navigation Satellite System (GNSS), etc.), and map information (for example, High Definition (HD)). maps, autonomous vehicle (AV) maps, etc.), gyro systems (e.g., inertial measurement units (IMUs), inertial navigation systems (INS), etc.), artificial intelligence ( Artificial Intelligence (AI) chips, AI processors, and other devices that provide functions to prevent accidents and reduce the driver's driving burden, as well as one or more devices that control these devices. It consists of an ECU. Further, the driving support system section 64 transmits and receives various information via the communication module 60, and realizes a driving support function or an automatic driving function.
　通信モジュール６０は、通信ポート６３を介して、マイクロプロセッサ６１及び車両４０の構成要素と通信することができる。例えば、通信モジュール６０は通信ポート６３を介して、車両４０に備えられた駆動部４１、操舵部４２、アクセルペダル４３、ブレーキペダル４４、シフトレバー４５、左右の前輪４６、左右の後輪４７、車軸４８、電子制御部４９内のマイクロプロセッサ６１及びメモリ（ＲＯＭ、ＲＡＭ）６２、各種センサ５０－５８との間でデータ（情報）を送受信する。 The communication module 60 can communicate with the microprocessor 61 and components of the vehicle 40 via the communication port 63. For example, the communication module 60 communicates via the communication port 63 with a drive unit 41, a steering unit 42, an accelerator pedal 43, a brake pedal 44, a shift lever 45, left and right front wheels 46, left and right rear wheels 47, which are included in the vehicle 40. Data (information) is transmitted and received between the axle 48, the microprocessor 61 and memory (ROM, RAM) 62 in the electronic control unit 49, and various sensors 50-58.
　通信モジュール６０は、電子制御部４９のマイクロプロセッサ６１によって制御可能であり、外部装置と通信を行うことが可能な通信デバイスである。例えば、外部装置との間で無線通信を介して各種情報の送受信を行う。通信モジュール６０は、電子制御部４９の内部と外部のどちらにあってもよい。外部装置は、例えば、上述の基地局１０、ユーザ端末２０などであってもよい。また、通信モジュール６０は、例えば、上述の基地局１０及びユーザ端末２０の少なくとも１つであってもよい（基地局１０及びユーザ端末２０の少なくとも１つとして機能してもよい）。 The communication module 60 is a communication device that can be controlled by the microprocessor 61 of the electronic control unit 49 and can communicate with external devices. For example, various information is transmitted and received with an external device via wireless communication. The communication module 60 may be located either inside or outside the electronic control unit 49. The external device may be, for example, the base station 10, user terminal 20, etc. described above. Further, the communication module 60 may be, for example, at least one of the base station 10 and the user terminal 20 described above (it may function as at least one of the base station 10 and the user terminal 20).
　通信モジュール６０は、電子制御部４９に入力された上述の各種センサ５０－５８からの信号、当該信号に基づいて得られる情報、及び情報サービス部５９を介して得られる外部（ユーザ）からの入力に基づく情報、の少なくとも１つを、無線通信を介して外部装置へ送信してもよい。電子制御部４９、各種センサ５０－５８、情報サービス部５９などは、入力を受け付ける入力部と呼ばれてもよい。例えば、通信モジュール６０によって送信されるＰＵＳＣＨは、上記入力に基づく情報を含んでもよい。 The communication module 60 receives signals from the various sensors 50 to 58 described above that are input to the electronic control unit 49, information obtained based on the signals, and input from the outside (user) obtained via the information service unit 59. At least one of the information based on the information may be transmitted to an external device via wireless communication. The electronic control unit 49, various sensors 50-58, information service unit 59, etc. may be called an input unit that receives input. For example, the PUSCH transmitted by the communication module 60 may include information based on the above input.
　通信モジュール６０は、外部装置から送信されてきた種々の情報（交通情報、信号情報、車間情報など）を受信し、車両に備えられた情報サービス部５９へ表示する。情報サービス部５９は、情報を出力する（例えば、通信モジュール６０によって受信されるＰＤＳＣＨ（又は当該ＰＤＳＣＨから復号されるデータ／情報）に基づいてディスプレイ、スピーカーなどの機器に情報を出力する）出力部と呼ばれてもよい。 The communication module 60 receives various information (traffic information, signal information, inter-vehicle information, etc.) transmitted from an external device, and displays it on the information service section 59 provided in the vehicle. The information service unit 59 is an output unit that outputs information (for example, outputs information to devices such as a display and a speaker based on the PDSCH (or data/information decoded from the PDSCH) received by the communication module 60). may be called.
　また、通信モジュール６０は、外部装置から受信した種々の情報をマイクロプロセッサ６１によって利用可能なメモリ６２へ記憶する。メモリ６２に記憶された情報に基づいて、マイクロプロセッサ６１が車両４０に備えられた駆動部４１、操舵部４２、アクセルペダル４３、ブレーキペダル４４、シフトレバー４５、左右の前輪４６、左右の後輪４７、車軸４８、各種センサ５０－５８などの制御を行ってもよい。 The communication module 60 also stores various information received from external devices into a memory 62 that can be used by the microprocessor 61. Based on the information stored in the memory 62, the microprocessor 61 controls the drive section 41, steering section 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, and left and right rear wheels provided in the vehicle 40. 47, axle 48, various sensors 50-58, etc. may be controlled.
　また、本開示における基地局は、ユーザ端末で読み替えてもよい。例えば、基地局及びユーザ端末間の通信を、複数のユーザ端末間の通信（例えば、Device-to-Device（Ｄ２Ｄ）、Vehicle-to-Everything（Ｖ２Ｘ）などと呼ばれてもよい）に置き換えた構成について、本開示の各態様／実施形態を適用してもよい。この場合、上述の基地局１０が有する機能をユーザ端末２０が有する構成としてもよい。また、「上りリンク（uplink）」、「下りリンク（downlink）」などの文言は、端末間通信に対応する文言（例えば、「サイドリンク（sidelink）」）で読み替えられてもよい。例えば、上りリンクチャネル、下りリンクチャネルなどは、サイドリンクチャネルで読み替えられてもよい。 Additionally, the base station in the present disclosure may be replaced by a user terminal. For example, communication between a base station and a user terminal is replaced with communication between multiple user terminals (for example, it may be called Device-to-Device (D2D), Vehicle-to-Everything (V2X), etc.). Regarding the configuration, each aspect/embodiment of the present disclosure may be applied. In this case, the user terminal 20 may have the functions that the base station 10 described above has. Further, words such as "uplink" and "downlink" may be replaced with words corresponding to inter-terminal communication (for example, "sidelink"). For example, uplink channels, downlink channels, etc. may be replaced with sidelink channels.
　同様に、本開示におけるユーザ端末は、基地局で読み替えてもよい。この場合、上述のユーザ端末２０が有する機能を基地局１０が有する構成としてもよい。 Similarly, the user terminal in the present disclosure may be replaced with a base station. In this case, the base station 10 may have the functions that the user terminal 20 described above has.
　本開示において、基地局によって行われるとした動作は、場合によってはその上位ノード（upper　node）によって行われることもある。基地局を有する１つ又は複数のネットワークノード（network　nodes）を含むネットワークにおいて、端末との通信のために行われる様々な動作は、基地局、基地局以外の１つ以上のネットワークノード（例えば、Mobility　Management　Entity（ＭＭＥ）、Serving-Gateway（Ｓ－ＧＷ）などが考えられるが、これらに限られない）又はこれらの組み合わせによって行われ得ることは明らかである。 In this disclosure, the operations performed by the base station may be performed by its upper node in some cases. In a network that includes one or more network nodes having a base station, various operations performed for communication with a terminal may be performed by the base station, one or more network nodes other than the base station (e.g. It is clear that this can be performed by a Mobility Management Entity (MME), a Serving-Gateway (S-GW), etc. (though not limited thereto), or a combination thereof.
　本開示において説明した各態様／実施形態は単独で用いてもよいし、組み合わせて用いてもよいし、実行に伴って切り替えて用いてもよい。また、本開示において説明した各態様／実施形態の処理手順、シーケンス、フローチャートなどは、矛盾の無い限り、順序を入れ替えてもよい。例えば、本開示において説明した方法については、例示的な順序を用いて様々なステップの要素を提示しており、提示した特定の順序に限定されない。 Each aspect/embodiment described in this disclosure may be used alone, in combination, or may be switched and used in accordance with execution. Furthermore, the order of the processing procedures, sequences, flowcharts, etc. of each aspect/embodiment described in this disclosure may be changed as long as there is no contradiction. For example, the methods described in this disclosure use an example order to present elements of the various steps and are not limited to the particular order presented.
　本開示において説明した各態様／実施形態は、Long　Term　Evolution（ＬＴＥ）、LTE-Advanced（ＬＴＥ－Ａ）、LTE-Beyond（ＬＴＥ－Ｂ）、ＳＵＰＥＲ　３Ｇ、ＩＭＴ－Ａｄｖａｎｃｅｄ、4th　generation　mobile　communication　system（４Ｇ）、5th　generation　mobile　communication　system（５Ｇ）、6th　generation　mobile　communication　system（６Ｇ）、xth　generation　mobile　communication　system（ｘＧ（ｘは、例えば整数、小数））、Future　Radio　Access（ＦＲＡ）、Ｎｅｗ－Radio　Access　Technology（ＲＡＴ）、New　Radio（ＮＲ）、New　radio　access（ＮＸ）、Future　generation　radio　access（ＦＸ）、Global　System　for　Mobile　communications（ＧＳＭ（登録商標））、ＣＤＭＡ２０００、Ultra　Mobile　Broadband（ＵＭＢ）、ＩＥＥＥ　８０２．１１（Ｗｉ－Ｆｉ（登録商標））、ＩＥＥＥ　８０２．１６（ＷｉＭＡＸ（登録商標））、ＩＥＥＥ　８０２．２０、Ultra-WideBand（ＵＷＢ）、Ｂｌｕｅｔｏｏｔｈ（登録商標）、その他の適切な無線通信方法を利用するシステム、これらに基づいて拡張、修正、作成又は規定された次世代システムなどに適用されてもよい。また、複数のシステムが組み合わされて（例えば、ＬＴＥ又はＬＴＥ－Ａと、５Ｇとの組み合わせなど）適用されてもよい。 Each aspect/embodiment described in this disclosure includes Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system ( 4G), 5th generation mobile communication system (5G), 6th generation mobile communication system (6G), xth generation mobile communication system (xG (x is an integer or decimal number, for example)), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802 .11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), and other appropriate wireless communication methods. The present invention may be applied to systems to be used, next-generation systems expanded, modified, created, or defined based on these systems. Furthermore, a combination of multiple systems (for example, a combination of LTE or LTE-A and 5G) may be applied.
　本開示において使用する「に基づいて」という記載は、別段に明記されていない限り、「のみに基づいて」を意味しない。言い換えれば、「に基づいて」という記載は、「のみに基づいて」と「に少なくとも基づいて」の両方を意味する。 As used in this disclosure, the phrase "based on" does not mean "based solely on" unless explicitly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."
　本開示において使用する「第１の」、「第２の」などの呼称を使用した要素へのいかなる参照も、それらの要素の量又は順序を全般的に限定しない。これらの呼称は、２つ以上の要素間を区別する便利な方法として本開示において使用され得る。したがって、第１及び第２の要素の参照は、２つの要素のみが採用され得ること又は何らかの形で第１の要素が第２の要素に先行しなければならないことを意味しない。 As used in this disclosure, any reference to elements using the designations "first," "second," etc. does not generally limit the amount or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, reference to a first and second element does not imply that only two elements may be employed or that the first element must precede the second element in any way.
　本開示において使用する「判断（決定）（determining）」という用語は、多種多様な動作を包含する場合がある。例えば、「判断（決定）」は、判定（judging）、計算（calculating）、算出（computing）、処理（processing）、導出（deriving）、調査（investigating）、探索（looking　up、search、inquiry）（例えば、テーブル、データベース又は別のデータ構造での探索）、確認（ascertaining）などを「判断（決定）」することであるとみなされてもよい。 The term "determining" as used in this disclosure may encompass a wide variety of actions. For example, "judgment" can mean judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry ( For example, searching in a table, database, or other data structure), ascertaining, etc. may be considered to be "determining."
　また、「判断（決定）」は、受信（receiving）（例えば、情報を受信すること）、送信（transmitting）（例えば、情報を送信すること）、入力（input）、出力（output）、アクセス（accessing）（例えば、メモリ中のデータにアクセスすること）などを「判断（決定）」することであるとみなされてもよい。 In addition, "judgment (decision)" includes receiving (e.g., receiving information), transmitting (e.g., sending information), input (input), output (output), access ( may be considered to be "determining", such as accessing data in memory (eg, accessing data in memory).
　また、「判断（決定）」は、解決（resolving）、選択（selecting）、選定（choosing）、確立（establishing）、比較（comparing）などを「判断（決定）」することであるとみなされてもよい。つまり、「判断（決定）」は、何らかの動作を「判断（決定）」することであるとみなされてもよい。 In addition, "judgment" is considered to mean "judging" resolving, selecting, choosing, establishing, comparing, etc. Good too. In other words, "judgment (decision)" may be considered to be "judgment (decision)" of some action.
　また、「判断（決定）」は、「想定する（assuming）」、「期待する（expecting）」、「みなす（considering）」などで読み替えられてもよい。 Furthermore, "judgment (decision)" may be read as "assuming", "expecting", "considering", etc.
　本開示に記載の「最大送信電力」は送信電力の最大値を意味してもよいし、公称最大送信電力（the　nominal　UE　maximum　transmit　power）を意味してもよいし、定格最大送信電力（the　rated　UE　maximum　transmit　power）を意味してもよい。 The "maximum transmit power" described in this disclosure may mean the maximum value of transmit power, the nominal maximum transmit power (the nominal UE maximum transmit power), or the rated maximum transmit power (the It may also mean rated UE maximum transmit power).
　本開示において使用する「接続された（connected）」、「結合された（coupled）」という用語、又はこれらのあらゆる変形は、２又はそれ以上の要素間の直接的又は間接的なあらゆる接続又は結合を意味し、互いに「接続」又は「結合」された２つの要素間に１又はそれ以上の中間要素が存在することを含むことができる。要素間の結合又は接続は、物理的であっても、論理的であっても、あるいはこれらの組み合わせであってもよい。例えば、「接続」は「アクセス」で読み替えられてもよい。 As used in this disclosure, the terms "connected", "coupled", or any variations thereof refer to any connection or coupling, direct or indirect, between two or more elements. can include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connection" may be replaced with "access."
　本開示において、２つの要素が接続される場合、１つ以上の電線、ケーブル、プリント電気接続などを用いて、並びにいくつかの非限定的かつ非包括的な例として、無線周波数領域、マイクロ波領域、光（可視及び不可視の両方）領域の波長を有する電磁エネルギーなどを用いて、互いに「接続」又は「結合」されると考えることができる。 In this disclosure, when two elements are connected, they may be connected using one or more electrical wires, cables, printed electrical connections, etc., as well as in the radio frequency domain, microwave can be considered to be "connected" or "coupled" to each other using electromagnetic energy having wavelengths in the light (both visible and invisible) range.
　本開示において、「ＡとＢが異なる」という用語は、「ＡとＢが互いに異なる」ことを意味してもよい。なお、当該用語は、「ＡとＢがそれぞれＣと異なる」ことを意味してもよい。「離れる」、「結合される」などの用語も、「異なる」と同様に解釈されてもよい。 In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." Note that the term may also mean that "A and B are each different from C". Terms such as "separate" and "coupled" may also be interpreted similarly to "different."
　本開示において、「含む（include）」、「含んでいる（including）」及びこれらの変形が使用されている場合、これらの用語は、用語「備える（comprising）」と同様に、包括的であることが意図される。さらに、本開示において使用されている用語「又は（or）」は、排他的論理和ではないことが意図される。 Where "include", "including" and variations thereof are used in this disclosure, these terms are inclusive, as is the term "comprising". It is intended that Furthermore, the term "or" as used in this disclosure is not intended to be exclusive or.
　本開示において、例えば、英語でのa,　an及びtheのように、翻訳によって冠詞が追加された場合、本開示は、これらの冠詞の後に続く名詞が複数形であることを含んでもよい。 In this disclosure, when articles are added by translation, such as a, an, and the in English, the present disclosure may include that the nouns following these articles are plural.
　本開示において、「以下」、「未満」、「以上」、「より多い」、「と等しい」などは、互いに読み替えられてもよい。また、本開示において、「良い」、「悪い」、「大きい」、「小さい」、「高い」、「低い」、「早い」、「遅い」などを意味する文言は、互いに読み替えられてもよい（原級、比較級、最上級を限らず）。また、本開示において、「良い」、「悪い」、「大きい」、「小さい」、「高い」、「低い」、「早い」、「遅い」などを意味する文言は、「ｉ番目に」を付けた表現として互いに読み替えられてもよい（原級、比較級、最上級を限らず）（例えば、「最高」は「ｉ番目に最高」と互いに読み替えられてもよい）。 In the present disclosure, "less than or equal to", "less than", "more than", "more than", "equal to", etc. may be read interchangeably. In addition, in the present disclosure, words meaning "good", "bad", "large", "small", "high", "low", "early", "slow", etc. may be read interchangeably. (Not limited to original, comparative, and superlative). In addition, in this disclosure, words meaning "good", "bad", "large", "small", "high", "low", "early", "slow", etc. are replaced with "i-th". They may be interchanged as expressions (not limited to the original, comparative, and superlative) (for example, "the highest" may be interchanged with "the i-th highest").
　本開示において、「の（of）」、「のための（for）」、「に関する（regarding）」、「に関係する（related　to）」、「に関連付けられる（associated　with）」などは、互いに読み替えられてもよい。 In this disclosure, "of", "for", "regarding", "related to", "associated with", etc. are used to refer to each other. It may be read differently.
　以上、本開示に係る発明について詳細に説明したが、当業者にとっては、本開示に係る発明が本開示中に説明した実施形態に限定されないということは明らかである。本開示に係る発明は、請求の範囲の記載に基づいて定まる発明の趣旨及び範囲を逸脱することなく修正及び変更態様として実施することができる。したがって、本開示の記載は、例示説明を目的とし、本開示に係る発明に対して何ら制限的な意味をもたらさない。 Although the invention according to the present disclosure has been described in detail above, it is clear for those skilled in the art that the invention according to the present disclosure is not limited to the embodiments described in the present disclosure. The invention according to the present disclosure can be implemented as modifications and variations without departing from the spirit and scope of the invention as determined based on the claims. Therefore, the description of the present disclosure is for the purpose of illustrative explanation and does not have any limiting meaning on the invention according to the present disclosure.

Claims (4)

  1. 　人工知能（Artificial　Intelligence（ＡＩ））ベースドビーム管理（Beam　Management（ＢＭ））に関して、性能モニタリング向けの報告のための設定情報を受信する受信部と、
    　前記設定情報に基づいて測定されるLayer　1　Reference　Signal　Received　Power（Ｌ１－ＲＳＲＰ）に関する報告の送信を制御する制御部と、を有する端末。     a receiving unit that receives configuration information for reporting for performance monitoring regarding artificial intelligence (AI) based beam management (BM);
    A terminal comprising: a control unit that controls transmission of a report regarding Layer 1 Reference Signal Received Power (L1-RSRP) measured based on the configuration information.
  2. 　前記制御部は、性能モニタリングリファレンスのために参照される参照信号機会より一定の単位時間遅くなく、又は一定の単位時間早くない予測時間に対応する予測Ｌ１－ＲＳＲＰを、前記報告に含める請求項１に記載の端末。 1 . The control unit includes, in the report, a predicted L1-RSRP corresponding to a predicted time that is not later than a predetermined unit time or earlier than a predetermined unit time than a reference signal opportunity referred to for a performance monitoring reference. Terminals listed in .
  3. 　人工知能（Artificial　Intelligence（ＡＩ））ベースドビーム管理（Beam　Management（ＢＭ））に関して、性能モニタリング向けの報告のための設定情報を受信するステップと、
    　前記設定情報に基づいて測定されるLayer　1　Reference　Signal　Received　Power（Ｌ１－ＲＳＲＰ）に関する報告の送信を制御するステップと、を有する端末の無線通信方法。     receiving configuration information for reporting for performance monitoring with respect to Artificial Intelligence (AI) based beam management (BM);
    A wireless communication method for a terminal, comprising: controlling transmission of a report regarding Layer 1 Reference Signal Received Power (L1-RSRP) measured based on the configuration information.
  4. 　人工知能（Artificial　Intelligence（ＡＩ））ベースドビーム管理（Beam　Management（ＢＭ））に関して、性能モニタリング向けの報告のための設定情報を送信する送信部と、
    　前記設定情報に基づいて測定されるLayer　1　Reference　Signal　Received　Power（Ｌ１－ＲＳＲＰ）に関する報告を受信する受信部と、を有する基地局。     a transmitting unit that transmits setting information for reporting for performance monitoring regarding artificial intelligence (AI) based beam management (BM);
    A base station comprising: a receiving unit that receives a report regarding Layer 1 Reference Signal Received Power (L1-RSRP) measured based on the configuration information.
PCT/JP2022/026514 2022-07-01 2022-07-01 Terminal, radio communication method, and base station WO2024004220A1 (en)

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021080222A1 (en) * 2019-10-25 2021-04-29 Samsung Electronics Co., Ltd. Method and apparatus for detecting physical downlink control channel based on predicted information

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021080222A1 (en) * 2019-10-25 2021-04-29 Samsung Electronics Co., Ltd. Method and apparatus for detecting physical downlink control channel based on predicted information

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HUAWEI, HISILICON: "Discussion on AI/ML for beam management", 3GPP DRAFT; R1-2203143, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. RAN WG1, no. e-Meeting; 20220509 - 20220520, 29 April 2022 (2022-04-29), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France, XP052143961 *

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