WO2024004194A1 - Terminal, wireless communication method, and base station - Google Patents

Terminal, wireless communication method, and base station Download PDF

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Publication number
WO2024004194A1
WO2024004194A1 PCT/JP2022/026446 JP2022026446W WO2024004194A1 WO 2024004194 A1 WO2024004194 A1 WO 2024004194A1 JP 2022026446 W JP2022026446 W JP 2022026446W WO 2024004194 A1 WO2024004194 A1 WO 2024004194A1
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Prior art keywords
model
information
csi
input
base station
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PCT/JP2022/026446
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French (fr)
Japanese (ja)
Inventor
春陽 越後
浩樹 原田
リュー リュー
シン ワン
ヤジュオ グァン
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株式会社Ｎｔｔドコモ
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Priority to PCT/JP2022/026446 priority Critical patent/WO2024004194A1/en
Publication of WO2024004194A1 publication Critical patent/WO2024004194A1/en

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    • 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

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 LTE-Advanced (3GPP Rel. 10-14) has been specified for the purpose of further increasing capacity and sophistication of LTE (Third Generation Partnership Project (3GPP) Releases (Rel.) 8 and 9).
  • 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
  • CSI channel state information reference signal
  • one of the purposes of the present disclosure is to provide a terminal, a wireless communication method, and a base station that can realize appropriate CSI feedback using AI.
  • a terminal determines input to an Artificial Intelligence (AI)/Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback, and applies the input to the AI/ML model. and a transmitter that transmits information regarding CSI using the output of the AI/ML model.
  • AI Artificial Intelligence
  • ML Machine Learning
  • CSI Channel State Information Reference Signal
  • FIG. 1 is a diagram illustrating an example of an AI model management framework.
  • FIG. 2 is a diagram illustrating an example of specifying an AI model.
  • FIG. 3 is a diagram showing an example of an AI model.
  • FIG. 4 is a diagram illustrating an example of AI-based CSI feedback.
  • FIG. 5 is a diagram showing an overview of a CSI report.
  • FIG. 6 is a diagram illustrating an example of the relationship between ranks, layers, and AI/ML models.
  • FIG. 7 is a diagram showing an example of encoder selection.
  • FIG. 8 is a diagram illustrating an example of an AI/ML model for CSI feedback.
  • FIG. 9A is a diagram showing an overview of preprocessing.
  • FIG. 9B is a diagram showing details of rank adaptation.
  • FIG. 9A is a diagram showing an overview of preprocessing.
  • FIG. 9B is a diagram showing details of rank adaptation.
  • FIG. 9A is a diagram showing an overview of preprocessing.
  • FIG. 10 is a diagram showing an example of application of preprocessed H.
  • FIG. 11 is a diagram illustrating an example of transmission of AI model related information.
  • FIG. 12 is a diagram illustrating an example of AI model related information.
  • FIG. 13 is a diagram illustrating an example of AI/ML model selection.
  • FIG. 14 is a diagram showing an example of payload size for each RI.
  • FIG. 15 is a diagram showing determination of an AI/ML model in the seventh embodiment.
  • FIG. 16 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment.
  • FIG. 17 is a diagram illustrating an example of the configuration of a base station according to an embodiment.
  • FIG. 18 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment.
  • FIG. 19 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment.
  • FIG. 20 is a diagram illustrating an example of a vehicle according to
  • AI Artificial Intelligence
  • ML machine learning
  • improved Channel State Information Reference Signal e.g., reduced overhead, improved accuracy, prediction
  • improved beam management e.g., improved accuracy, time
  • positioning e.g., position estimation/prediction in the spatial domain
  • position measurement e.g., position estimation/prediction
  • 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).
  • 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., ensuring that the trained model meets performance thresholds).
  • model exchange e.g., transferring a model for distributed learning
  • model deployment/updating deploying/updating a model to entities performing model inference
  • 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.
  • the Actor stage provides information necessary for action triggers (e.g., deciding whether to trigger an action on other entities), feedback (e.g., training data/inference data/performance feedback). (feedback) etc.
  • action triggers e.g., deciding whether to trigger an action on other entities
  • feedback e.g., training data/inference data/performance feedback. (feedback) etc.
  • 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 training/inference may be different.
  • 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.
  • Identifier (ID)-based model approaches can be one way to manage AI models in such scenarios.
  • the NW/gNB does not know the details of the AI model, but may only know some information about the AI model (for example, which ML model is used for what purpose in the UE) for AI model management. Can be done.
  • FIG. 2 is a diagram showing an example of specifying an AI model.
  • the UE and NW eg, base station (BS)
  • NW eg, base station (BS)
  • the UE may report, for example, the performance of model #1 and the performance of model #2 to the NW, and the NW may instruct the UE about the AI model to use.
  • the UE/BS inputs channel state information, reference signal measurements, etc. to the ML model to obtain highly accurate channel state information/measurements/beam selection/position, future channel state information, etc. /Wireless 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.
  • an object may be, for example, an apparatus, a device, such as a terminal or a base station. Furthermore, in the present disclosure, an object may correspond to a program/model/entity that operates on the device.
  • the ML 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.
  • AI, AI/ML, AI/ML model, ML model, model, AI model, predictive analytics, predictive analysis model, etc. may be read interchangeably.
  • the ML 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, and the like.
  • regression analysis eg, linear regression analysis, multiple regression analysis, logistic regression analysis
  • support vector machine random forest, neural network, deep learning, and the like.
  • a model may be interpreted as at least one of an encoder, a decoder, a tool, etc.
  • the ML model Based on the input information, the ML model outputs at least one information such as an estimated value, a predicted value, a selected action, a classification, etc.
  • the ML model may include supervised learning, unsupervised learning, reinforcement learning, and the like.
  • Supervised learning may be used to learn general rules that map inputs to outputs.
  • Unsupervised learning may be used to learn features of the data.
  • Reinforcement learning may be used to learn actions to maximize a goal.
  • 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.
  • Signal may be interchanged with signal/channel.
  • a terminal also referred to as a user terminal, User Equipment (UE), etc. transmits channel state information (CSI) based on a reference signal (RS) (or resources for the RS). )) (also referred to as determination, calculation, estimation, measurement, etc.) and transmits (also referred to as report, feedback, etc.) the generated CSI to the network (for example, a base station).
  • the CSI may be transmitted to the base station using, for example, an uplink control channel (eg, Physical Uplink Control Channel (PUCCH)) or an uplink shared channel (eg, Physical Uplink Shared Channel (PUSCH)).
  • PUCCH Physical Uplink Control Channel
  • PUSCH Physical Uplink Shared Channel
  • the RS used to generate CSI is, for example, a channel state information reference signal (CSI-RS), a synchronization signal/physical broadcast channel (SS/PBCH) block, or a synchronization signal/physical broadcast channel (SS/PBCH) block.
  • CSI-RS channel state information reference signal
  • SS/PBCH synchronization signal/physical broadcast channel
  • SS/PBCH synchronization signal/physical broadcast channel
  • DMRS demodulation reference signal
  • the CSI-RS may include at least one of a Non-Zero Power (NZP) CSI-RS and a CSI-Interference Management (CSI-IM).
  • the SS/PBCH block is a block that includes SS and PBCH (and corresponding DMRS), and may be called an SS block (SSB) or the like. Further, the SS may include at least one of a primary synchronization signal (PSS) and a secondary synchronization signal (SSS).
  • PSS primary synchronization signal
  • SSS secondary synchronization signal
  • CSI includes a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), and a SS /PBCH block resource indicator (SSBRI), layer indicator (LI), rank indicator (RI), L1-RSRP (reference signal reception in layer 1) At least one of the even if it includes one good.
  • 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 signal reception in layer 1
  • the UE may receive information regarding CSI reporting (report configuration information) and control CSI reporting based on the report configuration information.
  • the report configuration information may be, for example, "CSI-ReportConfig" of an information element (IE) of radio resource control (RRC).
  • IE information element
  • RRC radio resource control
  • the report configuration information may include, for example, at least one of the following.
  • - Information about the type of CSI report (report type information, e.g. "reportConfigType” of RRC IE)
  • - Information regarding one or more quantities of CSI to be reported (one or more CSI parameters)
  • report quantity information e.g. "reportQuantity” of RRC IE
  • report quantity information e.g. "reportQuantity” of RRC IE
  • resource information for example, "CSI-ResourceConfigId" of the RRC IE
  • frequency domain information e.g. "reportFreqConfiguration" of RRC IE
  • the report type information may include periodic CSI (P-CSI) reporting, aperiodic CSI (A-CSI) reporting, or semi-persistent (semi-persistent, semi-persistent) reporting.
  • P-CSI periodic CSI
  • A-CSI aperiodic CSI
  • SP-CSI Semi-Persistent CSI
  • the report amount information may specify at least one combination of the above CSI parameters (for example, CRI, RI, PMI, CQI, LI, L1-RSRP, etc.).
  • the resource information may be an ID of an RS resource.
  • the RS resources may include, for example, non-zero power CSI-RS resources or SSBs and CSI-IM resources (for example, zero-power CSI-RS resources).
  • the frequency domain information may also indicate the frequency granularity of the CSI report.
  • the frequency granularity may include, for example, widebands and subbands.
  • Wideband is the entire CSI reporting band.
  • the wideband may be, for example, the entirety of a certain carrier (component carrier (CC), cell, serving cell), or the entire bandwidth part (BWP) within a certain carrier. There may be.
  • the wideband may also be referred to as a CSI reporting band, the entire CSI reporting band, or the like.
  • wideband PMI reporting is configured (determined)
  • one wideband PMI may be reported for the entire CSI reporting band.
  • subband PMI reporting is configured, a single wideband indication i1 is reported for the entire CSI reporting band, and a subband indication for each of one or more subbands within the entire CSI reporting band. (one subband indication) i2 (eg, subband indication of each subband) may be reported.
  • the PMI may indicate a precoder matrix (also simply referred to as a precoder) that the UE considers appropriate for use in downlink (DL) transmission to the UE.
  • a precoder matrix also simply referred to as a precoder
  • Each value of PMI may correspond to one precoder matrix.
  • a set of PMI values may correspond to a different set of precoder matrices, referred to as a precoder codebook (also simply referred to as a codebook).
  • a CSI report may include one or more types of CSI.
  • the CSI may include at least one of a first type (type 1 CSI) used for single beam selection and a second type (type 2 CSI) used for multi beam selection.
  • a single beam may be expressed as a single layer, and a multibeam may be expressed as a plurality of beams.
  • type 1 CSI does not assume multi-user multiple input multiple output (MIMO), and type 2 CSI may assume multi-user MIMO.
  • Type 1 and Type I may be read interchangeably.
  • Type 2 and Type II may be interchanged.
  • the UCI may include one CSI part for wideband PMI feedback.
  • CSI report #n includes PMI wideband information if reported.
  • the UCI may include two CSI parts for subband PMI feedback.
  • CSI part 1 includes wideband PMI information.
  • CSI part 2 includes one wideband PMI information and some subband PMI information.
  • CSI part 1 and CSI part 2 are encoded separately.
  • 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), - 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)) ), location information
  • ⁇ Data auxiliary information may be called meta information
  • 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 base station serving (or serving) e.g.
  • 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 mobility types are fixed location UE, movable/moving UE, no mobility UE, low mobility UE, and medium mobility UE.
  • 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).
  • FIG. 3 is a diagram showing an example of an AI model.
  • This example shows an AI model that includes ResNet as model component #1, a transformer model as model component #2, a dense layer, and a normalization layer.
  • ResNet as model component #1
  • transformer model as model component #2
  • dense layer a dense layer
  • normalization layer a normalization layer
  • one AI model may be included as a component of another AI model.
  • FIG. 3 may be an AI model in which processing progresses from left to right.
  • 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)),
  • 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 referred to as AI model related information (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.
  • the UE receives various information (for example, information regarding settings/instructions) from the NW (base station, gNB) using upper layer signaling/physical layer signaling (for example, RRC signaling/MAC CE/DCI). You may receive it.
  • NW base station
  • gNB base station
  • upper layer signaling/physical layer signaling for example, RRC signaling/MAC CE/DCI.
  • the MAC CE may have a new Logical Channel ID (LCID) in the subheader.
  • LCID Logical Channel ID
  • An existing MAC CE may be expanded. For example, new octets may be introduced.
  • the DCI may have an existing DCI field or a newly introduced DCI field.
  • the DCI may be Cyclic Redundancy Check (CRC) scrambled by an existing Radio Network Temporary Identifier (RNTI) or a newly introduced RNTI.
  • CRC Cyclic Redundancy Check
  • RNTI Radio Network Temporary Identifier
  • RNTI Radio Network Temporary Identifier
  • the UE may transmit (report) various information to the NW (base station, gNB) using at least one of upper layer signaling (e.g., RRC message), MAC CE, and UCI. .
  • upper layer signaling e.g., RRC message
  • MAC CE e.g., MAC CE
  • UCI User Information
  • the MAC CE may have a new Logical Channel ID (LCID) in the subheader.
  • LCID Logical Channel ID
  • An existing MAC CE may be expanded. For example, new octets may be introduced.
  • the UE may transmit information to the NW in a periodic, semi-permanent (triggered by the UE or gNB instruction), or aperiodic (triggered by the UE or gNB instruction) type.
  • AI-based CSI feedback As a typical sub-use case, space-frequency domain CSI compression using a two-sided AI model is being considered.
  • FIG. 4 is a diagram showing an example of AI-based CSI feedback.
  • the UE performs pre-processing, AI/ML-based CSI generation, and post-processing on measurement results related to CSI, and transmits encoded bits (CSI feedback information) to the NW (base station).
  • the NW (base station) performs pre-processing, AI/ML-based CSI reconstruction, and post-processing on the received bits to obtain a CSI (channel/precoding matrix).
  • FIG. 5 is a diagram showing an overview of the CSI report.
  • the UE performs channel measurements on the received CSI-RS and calculates the CRI.
  • the UE may calculate the CSI parameters assuming LI, CQI, PMI, RI dependencies between the CSI parameters (if reported). For example, LI is calculated conditional on the reported CQI, PMI, RI and CRI.
  • CQI is calculated conditional on reported PMI, RI, and CRI.
  • PMI is calculated conditional on reported RI and CRI.
  • RI is calculated conditional on the reported CRI.
  • FIG. 6 is a diagram showing an example of the relationship between ranks and layers and AI/ML models.
  • the AI/ML model for eigenvector CSI compression will have multiple modes. For example, in mode 1, a model for each layer is applied. As mode 2, a model for each rank is applied. The rank corresponds to the number of layers.
  • FIG. 7 is a diagram showing an example of encoder selection.
  • the UE is configured with two encoders (encoders #1 and #2) from the base station (gNB), and selects one of the multiple encoders.
  • the UE may select the encoder to use based on certain rules and report information about the selected encoder, such as the encoder index, to the BS (e.g. using CSI part 1/MAC CE/RRC in the CSI report). hand).
  • AI/ML-based using AI/ML models
  • CSI feedback AI-assisted CSI feedback
  • H channel matrix
  • 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.”
  • 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.
  • encoder encoding, encoding, modification/change/control by encoder, etc.
  • decoder decoding, decoding, modification/change/control by decoder, etc.
  • UCI UCI
  • CSI report CSI feedback
  • feedback information feedback bit, etc.
  • bits, bit strings, bit sequences, sequences, values, information, values obtained from bits, information obtained from bits, etc. may be interchanged.
  • layers for encoders may be interchanged with layers (input layer, intermediate layer, etc.) used in the AI model.
  • the layer of the present disclosure may correspond to at least one of an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolution layer, a dropout layer, a fully connected layer, and the like.
  • a layer regarding a precoding matrix may be interchanged with a Multi Input Multi Output (MIMO) layer, stream, etc.
  • MIMO Multi Input Multi Output
  • 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.
  • rank and RI may be read interchangeably.
  • Network (NW), base station, and gNB may be read interchangeably.
  • input and input information may be read interchangeably.
  • the UE determines inputs to the AI/ML model for AI-based CSI feedback. Then, the determined input is applied to the AI/ML model, and information regarding the CSI (for example, encoded bits in FIG. 4) using the output (output information) of the AI/ML model is transmitted to the base station.
  • the following options are available as input ( Figure 8).
  • p means preprocessing, and q means quantization.
  • Each input (H, W, pH...) corresponds to one or more AI/ML models (AI/ML #1 to N).
  • - Precoding matrix/eigenvector (W). Preprocessed/quantized H (p-H, Q-H, Qp-H).
  • Preprocessed/quantized W QW, pW, Qp-W).
  • the UE may determine the input according to specific rules defined in the specifications or set by the NW (base station) and may report the determined input.
  • the above-mentioned example (reception of information) may be applied to this setting.
  • the example of (information reporting) described above may be applied to the report.
  • the UE may, for example, determine H (or pre-processed/post-processed/quantized H) as input if SINR ⁇ threshold.
  • the UE may determine QW (quantized W) as input, for example, when uplink resources are limited.
  • the UE may be configured with input from the NW (base station).
  • the configuration may be sent to the UE according to the example of (receiving information) above.
  • FIG. 9A is a diagram showing an overview of preprocessing.
  • FIG. 9B is a diagram showing details of rank adaptation.
  • Step 1 The UE decomposes H to obtain the highest rank R and singular matrix ⁇ (R).
  • Step 2 The UE performs rank adaptation and obtains the singular matrix ⁇ ′(R a ) corresponding to the adapted rank R a ( ⁇ R).
  • Step 3 The UE reconstructs a channel matrix (H a ) equivalent to H using ⁇ ′(R a ).
  • the UE can obtain an appropriate channel matrix according to the rank by performing preprocessing using the rank.
  • the UE Information may or may not be transmitted.
  • the pre-processing may be the process shown in the first embodiment.
  • FIG. 10 is a diagram showing an example of application of preprocessed H.
  • the UE inputs H a obtained by preprocessing H to the AI/ML model and obtains encoded bits.
  • the rank R a corresponding to this H a corresponds to the RI of this embodiment.
  • the UE may transmit the RI information in a manner similar to existing specifications.
  • the UE may compress and transmit the RI information together with H a (or preprocessed/quantized H a ).
  • the NW base station
  • the NW controls the input/output of the AI/ML model, the selected/reported AI/ML model information (see the third embodiment described below), or the reported AI-based CSI feedback.
  • the RI information may be inferred from the payload size (see the fourth embodiment described below).
  • the presence or absence of RI information becomes clear.
  • Communication capacity can be reduced by compressing RI information or omitting transmission.
  • FIG. 11 is a diagram illustrating an example of transmission of AI model related information.
  • the UE enters the illustrated cell and performs initial access/handover to this cell (BS).
  • the BS sends a message asking the UE for its ability to support AI model inference or not.
  • the UE sends capability information about it.
  • the NW base station (BS)
  • BS base station
  • AI model related information also referred to simply as related information
  • FIG. 12 is a diagram showing an example of AI model related information.
  • the related information may include at least one of information such as model ID, model function, model input/output, and application range.
  • the model ID may include an integer, a character string, etc.
  • the model functionality may include, for example, a description of the AI model's functionality of "estimate the best CSI-RS.”
  • the input of the model may be the RSRP of SSB #1-#n.
  • the output of the model may be the best CSI-RS index (CSI-RS resource ID).
  • the scope of application is, for example, cells that support AI-assisted technology (cells that can be subject to beam reports based on AI-assisted beam estimation, cells that may use prediction/estimation based on AI) are cells #1 to #3. It may also be shown that The coverage may be indicated by physical cell ID, serving cell index, etc.
  • the AI model related information may include the model ID and other related information (for example, model input/output, etc.), or may include only the model ID.
  • the UE may associate the model ID with related information based on specific rules. For example, the UE may decide which AI/ML model to apply based on specific rules and the received model ID. That is, the model ID may be mapped to other related information. Alternatively, the UE may receive information from the base station indicating what the relevant information corresponding to the model ID is.
  • ⁇ Third embodiment> If the UE selects the channel matrix (H) or preprocessed/quantized H as input for the AI/ML model, it may select the appropriate AI/ML model to use based on the RI.
  • FIG. 13 is a diagram showing an example of AI/ML model selection. For example, if RSRP ⁇ threshold (limited to Rank 1 transmission), the UE may determine 2D-CNN or a transformer as the AI/ML model. For example, if RSRP ⁇ threshold (Rank 2 or higher), the UE may determine 3D-CNN or 2D attention and transformer as the AI/ML model. The UE may determine the AI/ML model based on the SINR, for example.
  • the UE may be configured with an AI/ML model from the base station.
  • the above-mentioned example (reception of information) may be applied to this setting.
  • the UE may be configured with an AI/ML model corresponding to the RI and may report the RI.
  • the example of (information reporting) described above may be applied to the report.
  • the UE may be configured with a set of available AI/ML models (multiple AI/MLs), select one AI/ML from the set according to the RI value, and report the selected AI/ML model. good.
  • the above-mentioned example of (information report) may be applied to the report.
  • the UE can select an appropriate AI/ML model according to the RI.
  • the UE selects the channel matrix (H) or the preprocessed/quantized H as the input of the AI/ML model, it determines the appropriate payload size (H) for the output of the AI/ML model based on the rank (RI). output bits).
  • the UE may, for example, determine a larger payload size for a higher rank.
  • the UE may decide which output bits to report according to information regarding the rank.
  • the UE may determine the output bit according to certain rules defined in the specifications, rules set by the NW.
  • FIG. 14 is a diagram showing an example of payload size for each RI.
  • the UE may report information regarding which output bits are reported.
  • the information is, for example, the corresponding RI, payload size, or bit index.
  • the UE can determine an appropriate payload size (output bits) according to the RI.
  • the UE applies layer-by-layer mode or You may decide to use per-rank mode.
  • the example of FIG. 6 may be applied as the layer-by-layer mode and rank-by-rank mode.
  • the UE may decide on a per-layer mode or a per-rank mode based on UE capabilities, for example.
  • the layer-by-layer mode means a mode that uses a layer-by-layer AI/ML model (for example, a different/independent AI/ML model for each layer).
  • per-rank mode refers to a mode that uses a per-rank AI/ML model (eg, a different/independent AI/ML model for each rank).
  • the UE may decide the mode according to specific rules defined in the specifications, rules set by the NW (base station), and report the decision.
  • the example of (information reporting) described above may be applied to the report. For example, if the computing power of the UE or the storage capacity of the UE is lower than a threshold, the per-layer mode may be applied, otherwise the per-rank mode may be applied.
  • the UE may be set to a per-layer mode or a per-rank mode from the NW (base station).
  • the above-mentioned example may be applied to this setting.
  • the UE may report computing and storage capacity to the NW.
  • the UE may or may not transmit the RI information when W or preprocessed/quantized W is selected and the per-rank mode is determined. use).
  • the UE may select the appropriate AI/ML model to use according to the RI if W or preprocessed/quantized W is selected and per-rank mode is determined (as in the third embodiment). Reuse).
  • the UE may select an appropriate payload size (output bits) for the output of the AI/ML model according to the RI if W or preprocessed/quantized W is selected and per-rank mode is determined. (Reuse of the fourth embodiment).
  • the UE may decide (select) an AI/ML model and report it according to certain rules defined in the specifications and rules set by the NW.
  • the above-mentioned example (receiving information) may be applied to this setting.
  • the above-mentioned example of (information report) may be applied to the report.
  • the UE may be configured with an AI/ML model by the NW.
  • the above-mentioned example (receiving information) may be applied to this setting.
  • a UE may be configured with one AI/ML model.
  • the UE is configured with a set of available AI/ML models (multiple AI/ML models) and may select and report one AI/ML model from the set.
  • the example of (information reporting) described above may be applied to the report.
  • the UE may determine (select) an AI/ML model using any of the following methods (1) to (3).
  • (1) The UE selects the same AI/ML model in all layers.
  • (2) The UE selects an independent AI/ML model for each layer.
  • FIG. 15 is a diagram showing determination of an AI/ML model in the seventh embodiment.
  • the UE determines (selects) a precoding matrix/eigenvector (W) as an input for the AI/ML model, and determines (selects) the AI/ML model for each layer.
  • the same AI/ML model (AI/ML #1) may be determined in multiple layers such as layers 1 and 2.
  • an appropriate AI/ML model can be determined (selected) for each layer.
  • At least one of the embodiments described above may apply only to UEs that have transmitted (reported) or support a particular UE capability.
  • the specific UE capability may indicate supporting specific processing/operation/control/information for at least one of the above embodiments/options, applying at least one of the above embodiments/options. .
  • the above-mentioned specific UE capabilities may be capabilities that are applied across all frequencies (commonly regardless of frequency), or capabilities that are applied per frequency (for example, per cell, band, BWP, or band combination).
  • it may be a capability for each frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or it may be a capability for each frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or it may be a capability for each frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or it may be a capability for each frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2). ), or it may be an ability for each Feature Set (FS) or Feature Set Per Com
  • 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 is configured with specific information related to the embodiments described above by upper layer signaling or physical layer signaling.
  • the specific information may be information indicating that CSI feedback using an AI/ML 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 control unit that determines an input to an Artificial Intelligence (AI)/Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback and applies the input to the AI/ML model; a transmitting unit that transmits information regarding CSI using the output of the AI/ML model;
  • a terminal with [Additional note 2] The control unit selects, as the input, a channel matrix that has been subjected to at least one of preprocessing and quantization, The terminal according to supplementary note 1, wherein the transmitter transmits Rank Indicator (RI) information corresponding to the channel matrix.
  • RI Rank Indicator
  • control unit selects as the input a channel matrix that has undergone at least one of preprocessing and quantization
  • the control unit determines the AI/ML model based on a Rank Indicator (RI).
  • RI Rank Indicator
  • control unit selects as the input a precoding matrix or eigenvector that has been subjected to at least one of preprocessing and quantization
  • the control unit selects a mode in which the AI/ML model for each layer is used or the AI for each rank.
  • the terminal according to any one of Supplementary Notes 1 to 3, which determines to use a mode that uses a ML model.
  • 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. 16 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 use 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
  • 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 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, an 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 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 via the 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. 17 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 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 transmitter/receiver 120 may form at least one of a transmit beam and a receive beam using digital beam forming (e.g., precoding), analog beam forming (e.g., phase rotation), or the like.
  • digital beam forming e.g., precoding
  • analog beam forming 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 transmitter/receiver 120 may transmit reference information (for example, CSI-RS).
  • the transmitting/receiving unit 120 determines an input to an Artificial Intelligence (AI)/Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback corresponding to the reference signal at the terminal, and inputs the input into the AI/ML model.
  • AI Artificial Intelligence
  • ML Machine Learning
  • CSI Channel State Information Reference Signal
  • FIG. 18 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 control unit 210 may determine inputs to an Artificial Intelligence (AI)/Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback, and apply the inputs to the AI/ML model.
  • the transmitter/receiver 220 may transmit information regarding CSI using the output of the AI/ML model.
  • the control unit 210 may select, as the input, a channel matrix that has undergone at least one of preprocessing and quantization.
  • the transmitter/receiver 220 may transmit Rank Indicator (RI) information corresponding to the channel matrix.
  • RI Rank Indicator
  • control unit 210 may determine the AI/ML model based on the Rank Indicator (RI).
  • RI Rank Indicator
  • control unit 210 selects as the input a precoding matrix or eigenvector that has been subjected to at least one of preprocessing and quantization, the control unit 210 selects a mode in which the AI/ML model for each layer is used or the AI for each rank. /ML model may be decided to be used.
  • 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. 19 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 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 (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.
  • 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
  • slot etc.
  • TTI that is shorter than a 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 in 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. 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. 20 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 unit 41, steering unit 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.
  • Each aspect/embodiment described in this disclosure may be used alone, in combination, or may be switched and used in accordance with execution. Further, 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.”
  • 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.”

Abstract

A terminal according to one aspect of the present disclosure is characterized by comprising: a control unit that determines input to an artificial intelligence (AI) / machine learning (ML) model for channel state information reference signal (CSI) feedback, and applies the input to the AI/ML model; and a transmission unit that transmits information relating to CSI using output of the AI/ML model. According to the one aspect of the present disclosure, appropriate CSI feedback using AI can be achieved.

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）　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). Additionally, LTE-Advanced (3GPP Rel. 10-14) has been specified for the purpose of further increasing capacity and sophistication of LTE (Third Generation Partnership Project (3GPP) Releases (Rel.) 8 and 9).
　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。例えば、将来の無線通信技術について、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、予測などのためにＡＩ技術を活用することが検討されている。ＡＩ技術に基づくＣＳＩフィードバックは、ＡＩ支援ＣＳＩフィードバック（AI-aided　CSI　feedback）又はＡＩベースＣＳＩフィードバックと呼ばれてもよい。 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. For example, with regard to future wireless communication technology, the use of AI technology is being considered to improve channel state information reference signal (CSI) feedback, such as reducing overhead, improving accuracy, and predicting. There is. CSI feedback based on AI technology may be referred to as AI-aided CSI feedback or AI-based CSI feedback.
　しかしながら、ＡＩ支援ＣＳＩフィードバックの具体的な内容については、まだ検討が進んでいない。これらを適切に規定しなければ、ＡＩを用いた適切なＣＳＩフィードバックを実行できないおそれがある。これにより、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。 However, the specific content of AI-assisted CSI feedback has not yet been considered. If these are not properly defined, there is a possibility that appropriate CSI feedback using AI cannot be performed. As a result, appropriate overhead reduction/highly accurate channel estimation/highly efficient resource utilization cannot be achieved, and there is a possibility that improvement in communication throughput/communication quality may be suppressed.
　そこで、本開示は、ＡＩを用いた適切なＣＳＩフィードバックを実現できる端末、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 appropriate CSI feedback using AI.
　本開示の一態様に係る端末は、Channel　State　Information　Reference　Signal（ＣＳＩ）フィードバックのためのArtificial　Intelligence（ＡＩ）／Machine　Learning（ＭＬ）モデルに対する入力を決定し、当該入力を当該ＡＩ／ＭＬモデルに適用する制御部と、前記ＡＩ／ＭＬモデルの出力を用いた、ＣＳＩに関する情報を送信する送信部と、を有することを特徴とする。 A terminal according to one aspect of the present disclosure determines input to an Artificial Intelligence (AI)/Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback, and applies the input to the AI/ML model. and a transmitter that transmits information regarding CSI using the output of the AI/ML model.
　本開示の一態様によれば、ＡＩを用いた適切なＣＳＩフィードバックを実現できる。 According to one aspect of the present disclosure, appropriate CSI feedback using AI 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２は、ＡＩモデルの指定の一例を示す図である。FIG. 2 is a diagram illustrating an example of specifying an AI model. 図３は、ＡＩモデルの一例を示す図である。FIG. 3 is a diagram showing an example of an AI model. 図４は、ＡＩベースのＣＳＩフィードバックの例を示す図である。FIG. 4 is a diagram illustrating an example of AI-based CSI feedback. 図５は、ＣＳＩ報告の概要を示す図である。FIG. 5 is a diagram showing an overview of a CSI report. 図６は、ランク及びレイヤとＡＩ／ＭＬモデルの関係の例を示す図である。FIG. 6 is a diagram illustrating an example of the relationship between ranks, layers, and AI/ML models. 図７は、エンコーダ選択の一例を示す図である。FIG. 7 is a diagram showing an example of encoder selection. 図８は、ＣＳＩフィードバックのためのＡＩ／ＭＬモデルの例を示す図である。FIG. 8 is a diagram illustrating an example of an AI/ML model for CSI feedback. 図９Ａは、前処理の概要を示す図である。図９Ｂは、ランク適応の詳細を示す図である。FIG. 9A is a diagram showing an overview of preprocessing. FIG. 9B is a diagram showing details of rank adaptation. 図１０は、前処理されたＨの適用例を示す図である。FIG. 10 is a diagram showing an example of application of preprocessed H. 図１１は、ＡＩモデル関連情報の送信の一例を示す図である。FIG. 11 is a diagram illustrating an example of transmission of AI model related information. 図１２は、ＡＩモデル関連情報の一例を示す図である。FIG. 12 is a diagram illustrating an example of AI model related information. 図１３は、ＡＩ／ＭＬモデルの選択の例を示す図である。FIG. 13 is a diagram illustrating an example of AI/ML model selection. 図１４は、ＲＩ毎のペイロードサイズの例を示す図である。FIG. 14 is a diagram showing an example of payload size for each RI. 図１５は、第７の実施形態におけるＡＩ／ＭＬモデルの決定を示す図である。FIG. 15 is a diagram showing determination of an AI/ML model in the seventh embodiment. 図１６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. 16 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment. 図１７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. 17 is a diagram illustrating an example of the configuration of a base station according to an embodiment. 図１８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. 18 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment. 図１９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. 19 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment. 図２０は、一実施形態に係る車両の一例を示す図である。FIG. 20 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、将来の無線通信技術について、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ＡＩ技術を活用することが検討されている。 For example, for future wireless communication technologies, improved Channel State Information Reference Signal (CSI) feedback (e.g., reduced overhead, improved accuracy, prediction), improved beam management (e.g., improved accuracy, time The use of AI technology is being considered to improve positioning (e.g., position estimation/prediction in the spatial domain), position measurement (e.g., position estimation/prediction), and so on.
　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（Data　Collection）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).
　モデル訓練ステージ（Model　Training）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、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., ensuring that the trained model meets performance thresholds). , model exchange (e.g., transferring a model for distributed learning), model deployment/updating (deploying/updating a model to entities performing model inference), and the like.
　モデル推論（Model　Inference）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。 In 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.
　アクター（Actor）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 provides information necessary for action triggers (e.g., deciding whether to trigger an action on other entities), feedback (e.g., training data/inference data/performance feedback). (feedback) etc.
　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.
　また、ユースケースに応じて、訓練／推論を行うエンティティは異なってもよい。 Also, depending on the use case, the entity that performs training/inference may be different.
　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.
　自己符号化器（オートエンコーダ（autoencoder））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.
　ところで、データ／ＡＩモデルは、プロプライエタリな資産として扱われることが望ましい。例えば、高精度なＡＩモデルの作成には膨大な費用／時間がかかるため、ある企業が作成したＡＩモデルの内容が他社にわかってしまうと、大きな不利益となる。このため、異なるベンダーから提供されるＵＥ／ｇＮＢに対して、ＡＩモデルに関する情報の一部を利用不可能にする（又は推察できなくする）ことが検討されている。 By the way, it is desirable that data/AI models be treated as proprietary assets. For example, creating highly accurate AI models takes a huge amount of money and time, so if another company finds out the content of an AI model created by a company, it will be a big disadvantage. For this reason, it is being considered to make part of the information regarding the AI model unavailable (or inferred) for UEs/gNBs provided by different vendors.
　識別子（Identifier（ＩＤ））ベースのモデルアプローチは、そのようなシナリオにおけるＡＩモデルの管理方法の１つになり得る。例えば、ＮＷ／ｇＮＢはＡＩモデルの詳細を知らないが、ＡＩモデル管理のために、ＡＩモデルの一部情報（例えば、ＵＥにおいてどのＭＬモデルが何のために利用されているか）のみを知ることができる。 Identifier (ID)-based model approaches can be one way to manage AI models in such scenarios. For example, the NW/gNB does not know the details of the AI model, but may only know some information about the AI model (for example, which ML model is used for what purpose in the UE) for AI model management. Can be done.
　図２は、ＡＩモデルの指定の一例を示す図である。本例において、ＵＥ及びＮＷ（例えば、基地局（Base　Station（ＢＳ）））は、モデル＃１及び＃２を認識できる（モデルの詳細については完全には理解しなくてもよい）。ＵＥは、例えばモデル＃１の性能及びモデル＃２の性能をＮＷに報告し、ＮＷは、利用するＡＩモデルについてＵＥに指示してもよい。 FIG. 2 is a diagram showing an example of specifying an AI model. In this example, the UE and NW (eg, base station (BS)) can recognize models #1 and #2 (although they may not fully understand the details of the models). The UE may report, for example, the performance of model #1 and the performance of model #2 to the NW, and the NW may instruct the UE about the AI model to use.
　本開示においては、ＵＥ／ＢＳは、ＭＬ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。 In this disclosure, the UE/BS inputs channel state information, reference signal measurements, etc. to the ML model to obtain highly accurate channel state information/measurements/beam selection/position, future channel state information, etc. /Wireless 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、オブジェクトは、例えば、端末、基地局などの装置、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, such as a terminal or a base station. 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、ＭＬ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 ML 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、ＡＩ／ＭＬ、ＡＩ／ＭＬモデル、ＭＬモデル、モデル、ＡＩモデル、予測分析（predictive　analytics）、予測分析モデルなどは、互いに読み替えられてもよい。また、ＭＬ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。本開示において、モデルは、エンコーダ、デコーダ、ツールなどの少なくとも１つで読み替えられてもよい。 Furthermore, in the present disclosure, AI, AI/ML, AI/ML model, ML model, model, AI model, predictive analytics, predictive analysis model, etc. may be read interchangeably. Further, the ML 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, and the like. In this disclosure, a model may be interpreted as at least one of an encoder, a decoder, a tool, etc.
　ＭＬ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つの情報を出力する。 Based on the input information, the ML model outputs at least one information such as an estimated value, a predicted value, a selected action, a classification, etc.
　ＭＬモデルには、教師あり学習（supervised　learning）、教師なし学習（unsupervised　learning）、強化学習（Reinforcement　learning）などが含まれてもよい。教師あり学習は、入力を出力にマップする一般的なルールを学習するために用いられてもよい。教師なし学習は、データの特徴を学習するために用いられてもよい。強化学習は、目的（ゴール）を最大化するための動作を学習するために用いられてもよい。 The ML model may include supervised learning, unsupervised learning, reinforcement learning, and the like. Supervised learning may be used to learn general rules that map inputs to outputs. Unsupervised learning may be used to learn features of the data. Reinforcement learning may be used to learn actions to maximize a goal.
　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。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. Signal may be interchanged with signal/channel.
（ＣＳＩ報告（CSI　report又はreporting））
　Ｒｅｌ．１５　ＮＲでは、端末（ユーザ端末、User　Equipment（ＵＥ）等ともいう）は、参照信号（Reference　Signal（ＲＳ））（又は、当該ＲＳ用のリソース）に基づいてチャネル状態情報（Channel　State　Information（ＣＳＩ））を生成（決定、計算、推定、測定等ともいう）し、生成したＣＳＩをネットワーク（例えば、基地局）に送信（報告、フィードバック等ともいう）する。当該ＣＳＩは、例えば、上り制御チャネル（例えば、Physical　Uplink　Control　Channel（ＰＵＣＣＨ））又は上り共有チャネル（例えば、Physical　Uplink　Shared　Channel（ＰＵＳＣＨ））を用いて基地局に送信されてもよい。 (CSI report or reporting)
Rel. 15 In NR, a terminal (also referred to as a user terminal, User Equipment (UE), etc.) transmits channel state information (CSI) based on a reference signal (RS) (or resources for the RS). )) (also referred to as determination, calculation, estimation, measurement, etc.) and transmits (also referred to as report, feedback, etc.) the generated CSI to the network (for example, a base station). The CSI may be transmitted to the base station using, for example, an uplink control channel (eg, Physical Uplink Control Channel (PUCCH)) or an uplink shared channel (eg, Physical Uplink Shared Channel (PUSCH)).
　ＣＳＩの生成に用いられるＲＳ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／ブロードキャストチャネル（Synchronization　Signal/Physical　Broadcast　Channel（ＳＳ／ＰＢＣＨ））ブロック、同期信号（Synchronization　Signal（ＳＳ））、復調用参照信号（DeModulation　Reference　Signal（ＤＭＲＳ））等の少なくとも一つであってもよい。 The RS used to generate CSI is, for example, a channel state information reference signal (CSI-RS), a synchronization signal/physical broadcast channel (SS/PBCH) block, or a synchronization signal/physical broadcast channel (SS/PBCH) block. The signal may be at least one of a synchronization signal (SS), a demodulation reference signal (DMRS), or the like.
　ＣＳＩ－ＲＳは、ノンゼロパワー（Non　Zero　Power（ＮＺＰ））ＣＳＩ－ＲＳ及びＣＳＩ－Interference　Management（ＣＳＩ－ＩＭ）の少なくとも１つを含んでもよい。ＳＳ／ＰＢＣＨブロックは、ＳＳ及びＰＢＣＨ（及び対応するＤＭＲＳ）を含むブロックであり、ＳＳブロック（ＳＳＢ）などと呼ばれてもよい。また、ＳＳは、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））の少なくとも一つを含んでもよい。 The CSI-RS may include at least one of a Non-Zero Power (NZP) CSI-RS and a CSI-Interference Management (CSI-IM). The SS/PBCH block is a block that includes SS and PBCH (and corresponding DMRS), and may be called an SS block (SSB) or the like. Further, the SS may include at least one of a primary synchronization signal (PSS) and a secondary synchronization signal (SSS).
　なお、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）などの少なくとも１つを含んでもよい。 Note that CSI includes a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), and a SS /PBCH block resource indicator (SSBRI), layer indicator (LI), rank indicator (RI), L1-RSRP (reference signal reception in layer 1) At least one of the even if it includes one good.
　ＵＥは、ＣＳＩ報告に関する情報（報告設定（report　configuration）情報）を受信し、当該報告設定情報に基づいてＣＳＩ報告を制御してもよい。当該報告設定情報は、例えば、無線リソース制御（Radio　Resource　Control（ＲＲＣ））の情報要素（Information　Element（ＩＥ））の「CSI-ReportConfig」であってもよい。なお、本開示において、ＲＲＣ　ＩＥは、ＲＲＣパラメータ、上位レイヤパラメータなどと互いに読み替えられてもよい。 The UE may receive information regarding CSI reporting (report configuration information) and control CSI reporting based on the report configuration information. The report configuration information may be, for example, "CSI-ReportConfig" of an information element (IE) of radio resource control (RRC). Note that in the present disclosure, RRC IE may be interchanged with RRC parameters, upper layer parameters, and the like.
　当該報告設定情報（例えば、ＲＲＣ　ＩＥの「CSI-ReportConfig」）は、例えば、以下の少なくとも一つを含んでもよい。
・ＣＳＩ報告のタイプに関する情報（報告タイプ情報、例えば、ＲＲＣ　ＩＥの「reportConfigType」）
・報告すべきＣＳＩの一以上の量（quantity）（一以上のＣＳＩパラメータ）に関する情報（報告量情報、例えば、ＲＲＣ　ＩＥの「reportQuantity」）
・当該量（当該ＣＳＩパラメータ）の生成に用いられるＲＳ用リソースに関する情報（リソース情報、例えば、ＲＲＣ　ＩＥの「CSI-ResourceConfigId」）
・ＣＳＩ報告の対象となる周波数ドメイン（frequency　domain）に関する情報（周波数ドメイン情報、例えば、ＲＲＣ　ＩＥの「reportFreqConfiguration」） The report configuration information (for example, "CSI-ReportConfig" of the RRC IE) may include, for example, at least one of the following.
- Information about the type of CSI report (report type information, e.g. "reportConfigType" of RRC IE)
- Information regarding one or more quantities of CSI to be reported (one or more CSI parameters) (report quantity information, e.g. "reportQuantity" of RRC IE)
- Information regarding the RS resource used to generate the relevant amount (the relevant CSI parameter) (resource information, for example, "CSI-ResourceConfigId" of the RRC IE)
- Information regarding the frequency domain targeted for CSI reporting (frequency domain information, e.g. "reportFreqConfiguration" of RRC IE)
　例えば、報告タイプ情報は、周期的なＣＳＩ（Periodic　CSI（Ｐ－ＣＳＩ））報告、非周期的なＣＳＩ（Aperiodic　CSI（Ａ－ＣＳＩ））報告、又は、半永続的（半持続的、セミパーシステント（Semi-Persistent））なＣＳＩ報告（Semi-Persistent　CSI（ＳＰ－ＣＳＩ））報告を示し（indicate）てもよい。 For example, the report type information may include periodic CSI (P-CSI) reporting, aperiodic CSI (A-CSI) reporting, or semi-persistent (semi-persistent, semi-persistent) reporting. A Semi-Persistent CSI (SP-CSI) report may be indicated.
　また、報告量情報は、上記ＣＳＩパラメータ（例えば、ＣＲＩ、ＲＩ、ＰＭＩ、ＣＱＩ、ＬＩ、Ｌ１－ＲＳＲＰ等）の少なくとも一つの組み合わせを指定してもよい。 Additionally, the report amount information may specify at least one combination of the above CSI parameters (for example, CRI, RI, PMI, CQI, LI, L1-RSRP, etc.).
　また、リソース情報は、ＲＳ用リソースのＩＤであってもよい。当該ＲＳ用リソースは、例えば、ノンゼロパワーのＣＳＩ－ＲＳリソース又はＳＳＢと、ＣＳＩ－ＩＭリソース（例えば、ゼロパワーのＣＳＩ－ＲＳリソース）とを含んでもよい。 Additionally, the resource information may be an ID of an RS resource. The RS resources may include, for example, non-zero power CSI-RS resources or SSBs and CSI-IM resources (for example, zero-power CSI-RS resources).
　また、周波数ドメイン情報は、ＣＳＩ報告の周波数粒度（frequency　granularity）を示してもよい。当該周波数粒度は、例えば、ワイドバンド及びサブバンドを含んでもよい。ワイドバンドは、ＣＳＩ報告バンド全体（entire　CSI　reporting　band）である。ワイドバンドは、例えば、ある（certain）キャリア（コンポーネントキャリア（Component　Carrier（ＣＣ））、セル、サービングセル）全体であってもよいし、あるキャリア内の帯域幅部分（Bandwidth　part（ＢＷＰ））全体であってもよい。ワイドバンドは、ＣＳＩ報告バンド、ＣＳＩ報告バンド全体（entire　CSI　reporting　band）等と言い換えられてもよい。 The frequency domain information may also indicate the frequency granularity of the CSI report. The frequency granularity may include, for example, widebands and subbands. Wideband is the entire CSI reporting band. The wideband may be, for example, the entirety of a certain carrier (component carrier (CC), cell, serving cell), or the entire bandwidth part (BWP) within a certain carrier. There may be. The wideband may also be referred to as a CSI reporting band, the entire CSI reporting band, or the like.
　また、サブバンドは、ワイドバンド内の一部であり、一以上のリソースブロック（Resource　Block（ＲＢ）又は物理リソースブロック（Physical　Resource　Block（ＰＲＢ）））で構成されてもよい。サブバンドのサイズは、ＢＷＰのサイズ（ＰＲＢ数）に応じて決定されてもよい。 Further, a subband is a part of a wideband, and may be composed of one or more resource blocks (Resource Block (RB) or Physical Resource Block (PRB)). The size of the subband may be determined according to the size of the BWP (number of PRBs).
　周波数ドメイン情報は、ワイドバンド又はサブバンドのどちらのＰＭＩを報告するかを示してもよい（周波数ドメイン情報は、例えば、ワイドバンドＰＭＩ報告又はサブバンドＰＭＩ報告の何れかの決定に用いられるＲＲＣ　ＩＥの「pmi-FormatIndicator」を含んでもよい）。ＵＥは、上記報告量情報及び周波数ドメイン情報の少なくとも一つに基づいて、ＣＳＩ報告の周波数粒度（すなわち、ワイドバンドＰＭＩ報告又はサブバンドＰＭＩ報告の何れか）を決定してもよい。 The frequency domain information may indicate whether wideband or subband PMI is to be reported (the frequency domain information may include, for example, the RRC IE used to determine whether to report wideband or subband PMI). (may include "pmi-FormatIndicator"). The UE may determine the frequency granularity of the CSI report (ie, either wideband PMI report or subband PMI report) based on at least one of the report amount information and frequency domain information.
　ワイドバンドＰＭＩ報告が設定（決定）される場合、一つのワイドバンドＰＭＩがＣＳＩ報告バンド全体用に報告されてもよい。一方、サブバンドＰＭＩ報告が設定される場合、単一のワイドバンド表示（single　wideband　indication）ｉ１がＣＳＩ報告バンド全体用に報告され、当該ＣＳＩ報告全体内の一以上のサブバンドそれぞれのサブバンド表示（one　subband　indication）ｉ２（例えば、各サブバンドのサブバンド表示）が報告されてもよい。 If wideband PMI reporting is configured (determined), one wideband PMI may be reported for the entire CSI reporting band. On the other hand, if subband PMI reporting is configured, a single wideband indication i1 is reported for the entire CSI reporting band, and a subband indication for each of one or more subbands within the entire CSI reporting band. (one subband indication) i2 (eg, subband indication of each subband) may be reported.
　ＵＥは、受信したＲＳを用いてチャネル推定（channel　estimation）を行い、チャネル行列（Channel　matrix）Ｈを推定する。ＵＥは、推定されたチャネル行列に基づいて決定されるインデックス（ＰＭＩ）をフィードバックする。 The UE performs channel estimation using the received RS and estimates a channel matrix H. The UE feeds back an index (PMI) that is determined based on the estimated channel matrix.
　ＰＭＩは、ＵＥが、ＵＥに対する下り（downlink（ＤＬ））送信に用いるに適切と考えるプリコーダ行列（単に、プリコーダともいう）を示してもよい。ＰＭＩの各値は、一つのプリコーダ行列に対応してもよい。ＰＭＩの値のセットは、プリコーダコードブック（単に、コードブックともいう）と呼ばれる異なるプリコーダ行列のセットに対応してもよい。 The PMI may indicate a precoder matrix (also simply referred to as a precoder) that the UE considers appropriate for use in downlink (DL) transmission to the UE. Each value of PMI may correspond to one precoder matrix. A set of PMI values may correspond to a different set of precoder matrices, referred to as a precoder codebook (also simply referred to as a codebook).
　空間ドメイン（space　domain）において、ＣＳＩ報告は一以上のタイプのＣＳＩを含んでもよい。例えば、当該ＣＳＩは、シングルビームの選択に用いられる第１のタイプ（タイプ１ＣＳＩ）及びマルチビームの選択に用いられる第２のタイプ（タイプ２ＣＳＩ）の少なくとも一つを含んでもよい。シングルビームは、単一のレイヤ、マルチビームは、複数のビームと言い換えられてもよい。また、タイプ１ＣＳＩは、マルチユーザmultiple　input　multiple　output（ＭＩＭＯ）を想定せず、タイプ２ＣＳＩは、マルチユーザＭＩＭＯを想定してもよい。 In the space domain, a CSI report may include one or more types of CSI. For example, the CSI may include at least one of a first type (type 1 CSI) used for single beam selection and a second type (type 2 CSI) used for multi beam selection. A single beam may be expressed as a single layer, and a multibeam may be expressed as a plurality of beams. Further, type 1 CSI does not assume multi-user multiple input multiple output (MIMO), and type 2 CSI may assume multi-user MIMO.
　上記コードブックは、タイプ１ＣＳＩ用のコードブック（タイプ１コードブック等ともいう）と、タイプ２ＣＳＩ用のコードブック（タイプ２コードブック等ともいう）を含んでもよい。また、タイプ１ＣＳＩは、タイプ１シングルパネルＣＳＩ及びタイプ１マルチパネルＣＳＩを含んでもよく、それぞれ異なるコードブック（タイプ１シングルパネルコードブック、タイプ１マルチパネルコードブック）が規定されてもよい。 The codebook may include a codebook for type 1 CSI (also referred to as type 1 codebook, etc.) and a codebook for type 2 CSI (also referred to as type 2 codebook, etc.). Further, type 1 CSI may include type 1 single panel CSI and type 1 multi-panel CSI, and different codebooks (type 1 single panel codebook, type 1 multi-panel codebook) may be defined for each.
　本開示において、タイプ１及びタイプＩは互いに読み替えられてもよい。本開示において、タイプ２及びタイプＩＩは互いに読み替えられてもよい。 In this disclosure, Type 1 and Type I may be read interchangeably. In this disclosure, Type 2 and Type II may be interchanged.
　上り制御情報（ＵＣＩ）タイプは、Hybrid　Automatic　Repeat　reQuest　ACKnowledgement（ＨＡＲＱ－ＡＣＫ）、スケジューリング要求（scheduling　request（ＳＲ））、ＣＳＩ、の少なくとも１つを含んでもよい。ＵＣＩは、ＰＵＣＣＨによって運ばれてもよいし、ＰＵＳＣＨによって運ばれてもよい。 The uplink control information (UCI) type may include at least one of Hybrid Automatic Repeat Request ACKnowledgement (HARQ-ACK), scheduling request (SR), and CSI. UCI may be carried by PUCCH or PUSCH.
　Ｒｅｌ．１５　ＮＲにおいて、ＵＣＩは、ワイドバンドＰＭＩフィードバック用の１つのＣＳＩパートを含むことができる。ＣＳＩ報告＃ｎは、もし報告される場合にＰＭＩワイドバンド情報を含む。 Rel. In 15 NR, the UCI may include one CSI part for wideband PMI feedback. CSI report #n includes PMI wideband information if reported.
　Ｒｅｌ．１５　ＮＲにおいて、ＵＣＩは、サブバンドＰＭＩフィードバック用の２つのＣＳＩパートを含むことができる。ＣＳＩパート１は、ワイドバンドＰＭＩ情報を含む。ＣＳＩパート２は、１つのワイドバンドＰＭＩ情報と幾つかのサブバンドＰＭＩ情報とを含む。ＣＳＩパート１及びＣＳＩパート２は、分離されて符号化される。
（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。 Rel. In 15 NR, the UCI may include two CSI parts for subband PMI feedback. CSI part 1 includes wideband PMI information. CSI part 2 includes one wideband PMI information and some subband PMI information. CSI part 1 and CSI part 2 are encoded separately.
(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),
- 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）基地局の情報（例えば、基地局／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 base station serving (or serving) (e.g. base station/cell identifier (ID)), BS-UE distance, direction/angle of the BS (UE) as seen from the UE (BS), It includes at least one of the coordinates of the BS (UE) as seen from the UE (BS) (e.g., X/Y/Z axis coordinates, etc.), the specific address of the UE (e.g., Internet Protocol (IP) address), etc. But that's fine. 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）を含んでもよい。 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.).
　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).
　図３は、ＡＩモデルの一例を示す図である。本例は、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を含むＡＩモデルを示す。このように、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。なお、図３は、左から右の順に処理が進んでいくＡＩモデルであってもよい。 FIG. 3 is a diagram showing an example of an AI model. This example shows an AI model that includes ResNet as model component #1, a transformer model as model component #2, a dense layer, and a normalization layer. In this way, one AI model may be included as a component of another AI model. Note that FIG. 3 may be an AI model in which processing progresses from left to right.
　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 referred to as AI model related information (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.
（情報の受信）
　本開示では、ＵＥは、ＮＷ（基地局、ｇＮＢ）から、上位レイヤシグナリング／物理レイヤシグナリング（例えば、ＲＲＣシグナリング／ＭＡＣ　ＣＥ／ＤＣＩ）を用いて各種の情報（例えば、設定／指示に関する情報）を受信してもよい。 (Reception of information)
In the present disclosure, the UE receives various information (for example, information regarding settings/instructions) from the NW (base station, gNB) using upper layer signaling/physical layer signaling (for example, RRC signaling/MAC CE/DCI). You may receive it.
　ＭＡＣ　ＣＥは、サブヘッダに新しいLogical　Channel　ID（ＬＣＩＤ）を有していてもよい。既存のＭＡＣ　ＣＥが拡張されてもよい。例えば、新しいオクテットが導入されてもよい。 The MAC CE may have a new Logical Channel ID (LCID) in the subheader. An existing MAC CE may be expanded. For example, new octets may be introduced.
　ＤＣＩは、既存のＤＣＩフィールド又は新規に導入されたＤＣＩフィールドを有していてもよい。ＤＣＩは、既存のRadio　Network　Temporary　Identifier（ＲＮＴＩ）又は新規に導入されたＲＮＴＩによりCyclic　Redundancy　Check（ＣＲＣ）スクランブルされてもよい。は、既存のＤＣＩフォーマット（ＤＣＩフォーマット０＿０～０＿２、１＿０～１＿２、２＿０～２＿６、３＿０～３＿１）又は新規に導入されたＤＣＩフォーマットが適用されＤＣＩフォーマットでもよい。 The DCI may have an existing DCI field or a newly introduced DCI field. The DCI may be Cyclic Redundancy Check (CRC) scrambled by an existing Radio Network Temporary Identifier (RNTI) or a newly introduced RNTI. may be a DCI format to which an existing DCI format (DCI formats 0_0 to 0_2, 1_0 to 1_2, 2_0 to 2_6, 3_0 to 3_1) or a newly introduced DCI format is applied.
　ＵＥは、以下の周期的（periodically）、半永続的（semi-persistent）（ＵＥ又はｇＮＢの指示によりトリガされる）、非周期的（aperiodic）（ＵＥ又はｇＮＢの指示によりトリガされる）なタイプでＮＷから情報を受信してもよい。 The UE can be of the following types: periodic, semi-persistent (triggered by UE or gNB instruction), aperiodic (triggered by UE or gNB instruction). The information may be received from the NW.
（情報の報告）
　本開示では、ＵＥは、ＮＷ（基地局、ｇＮＢ）に、上位レイヤシグナリング（例えばＲＲＣメッセージ）、ＭＡＣ　ＣＥ、ＵＣＩの少なくても１つを用いて各種の情報を送信（報告）してもよい。 (Reporting information)
In the present disclosure, the UE may transmit (report) various information to the NW (base station, gNB) using at least one of upper layer signaling (e.g., RRC message), MAC CE, and UCI. .
　ＭＡＣ　ＣＥは、サブヘッダに新しいLogical　Channel　ID（ＬＣＩＤ）を有していてもよい。既存のＭＡＣ　ＣＥが拡張されてもよい。例えば、新しいオクテットが導入されてもよい。 The MAC CE may have a new Logical Channel ID (LCID) in the subheader. An existing MAC CE may be expanded. For example, new octets may be introduced.
　ＵＣＩは、ＰＵＣＣＨ又はＰＵＳＣＨのいずれかにより送信されてもよい。 The UCI may be transmitted by either PUCCH or PUSCH.
　ＵＥは、周期的、半永続的（ＵＥ又はｇＮＢの指示によりトリガされる）、非周期的（ＵＥ又はｇＮＢの指示によりトリガされる）なタイプでＮＷに情報を送信してもよい。 The UE may transmit information to the NW in a periodic, semi-permanent (triggered by the UE or gNB instruction), or aperiodic (triggered by the UE or gNB instruction) type.
（ＡＩベースのＣＳＩフィードバック）
　代表的なサブユースケースとして、２サイドＡＩモデルによる空間－周波数領域ＣＳＩ圧縮が検討されている。 (AI-based CSI feedback)
As a typical sub-use case, space-frequency domain CSI compression using a two-sided AI model is being considered.
　図４は、ＡＩベースのＣＳＩフィードバックの例を示す図である。ＵＥは、ＣＳＩに関する測定結果等に対して前処理、ＡＩ／ＭＬベースＣＳＩ生成、後処理を行い、エンコードされたビット（ＣＳＩフィードバック情報）をＮＷ（基地局）に送信する。ＮＷ（基地局）は、受信したビットに対して、前処理、ＡＩ／ＭＬベースＣＳＩ再構成、後処理を行い、ＣＳＩ（チャネル／プリコーディング行列）を取得する。 FIG. 4 is a diagram showing an example of AI-based CSI feedback. The UE performs pre-processing, AI/ML-based CSI generation, and post-processing on measurement results related to CSI, and transmits encoded bits (CSI feedback information) to the NW (base station). The NW (base station) performs pre-processing, AI/ML-based CSI reconstruction, and post-processing on the received bits to obtain a CSI (channel/precoding matrix).
　図５は、ＣＳＩ報告の概要を示す図である。ＵＥは、受信したＣＳＩ－ＲＳに対してチャネル測定を行い、ＣＲＩを計算する。ＵＥは、ＣＳＩパラメータ（報告される場合）間のＬＩ、ＣＱＩ、ＰＭＩ、ＲＩ依存関係を想定して、ＣＳＩパラメータを計算してもよい。例えば、ＬＩは、報告されるＣＱＩ、ＰＭＩ、ＲＩ及びＣＲＩを条件として計算される。ＣＱＩは、報告されたＰＭＩ、ＲＩ、及びＣＲＩを条件として計算される。ＰＭＩは、報告されるＲＩ及びＣＲＩを条件として算出される。ＲＩは、報告されるＣＲＩを条件として算出される。 FIG. 5 is a diagram showing an overview of the CSI report. The UE performs channel measurements on the received CSI-RS and calculates the CRI. The UE may calculate the CSI parameters assuming LI, CQI, PMI, RI dependencies between the CSI parameters (if reported). For example, LI is calculated conditional on the reported CQI, PMI, RI and CRI. CQI is calculated conditional on reported PMI, RI, and CRI. PMI is calculated conditional on reported RI and CRI. RI is calculated conditional on the reported CRI.
　図６は、ランク及びレイヤとＡＩ／ＭＬモデルの関係の例を示す図である。受信アンテナ数＞１、Ｒａｎｋ＞１の場合、固有ベクトルのＣＳＩ圧縮のＡＩ／ＭＬモデルは、複数のモードを持つことになる。例えば、モード１として、レイヤ毎のモデルが適用される。モード２として、ランク毎のモデルが適用される。ランクは、レイヤの数に該当する。 FIG. 6 is a diagram showing an example of the relationship between ranks and layers and AI/ML models. When the number of receiving antennas is >1 and the Rank is >1, the AI/ML model for eigenvector CSI compression will have multiple modes. For example, in mode 1, a model for each layer is applied. As mode 2, a model for each rank is applied. The rank corresponds to the number of layers.
　受信アンテナ数＞１、Ｒａｎｋ＞１の場合、ＣＳＩ圧縮にランク毎のモデルを採用すると、ＡＩ／ＭＬモデルとして複数の選択肢が存在する。例えば、３Ｄ－convolution　neural　networks（ＣＮＮ）などの３Ｄ　ＡＩ／ＭＬモデルが選択される。３Ｄ　ＡＩ／ＭＬモデルを用いることより、レイヤ毎の相関を用いた圧縮が可能となる。又は、前処理済み２Ｄ　ＡＩ／ＭＬモデルが選択される。２Ｄ　ＡＩ／ＭＬモデルは、３Ｄ　ＡＩ／ＭＬモデルに対して、２Ｄアテンションによる機能統合及びトランスフォーマーによる処理を行うことにより取得される。 When the number of receiving antennas>1 and the Rank>1, if a model for each rank is adopted for CSI compression, there are multiple options as the AI/ML model. For example, a 3D AI/ML model such as 3D-convolution neural networks (CNN) is selected. By using a 3D AI/ML model, compression using correlation for each layer becomes possible. Alternatively, a preprocessed 2D AI/ML model is selected. A 2D AI/ML model is obtained by performing functional integration using 2D attention and processing using a transformer on a 3D AI/ML model.
（エンコーダの選択）
　図７は、エンコーダ選択の一例を示す図である。ＵＥは、２つのエンコーダ（エンコーダ＃１、＃２）を基地局（ｇＮＢ）から設定され、複数のエンコーダのうちのいずれかのエンコーダを選択する。 (encoder selection)
FIG. 7 is a diagram showing an example of encoder selection. The UE is configured with two encoders (encoders #1 and #2) from the base station (gNB), and selects one of the multiple encoders.
　ＵＥは、例えば上位レイヤシグナリング／物理レイヤシグナリング（ＲＲＣ／ＭＡＣ　ＣＥ／ＤＣＩ）、又は基地局から受信したその他の情報（例えば、オートエンコーダの目標精度、目標圧縮率）に基づいて、使用するエンコーダを選択してもよい。 The UE determines which encoder to use based on, for example, upper layer signaling/physical layer signaling (RRC/MAC CE/DCI) or other information received from the base station (e.g., target accuracy of autoencoder, target compression rate). You may choose.
　ＵＥは、特定のルールに基づいて使用するエンコーダを選択し、エンコーダインデックスなどの選択されたエンコーダに関する情報をＢＳに報告してもよい（例えば、ＣＳＩレポートにおけるＣＳＩパート１／ＭＡＣ　ＣＥ／ＲＲＣを用いて）。 The UE may select the encoder to use based on certain rules and report information about the selected encoder, such as the encoder index, to the BS (e.g. using CSI part 1/MAC CE/RRC in the CSI report). hand).
　例えば、基地局から通知されたエンコーダ（エンコーダ＃１、＃２）の利用ではターゲット性能を達成できない場合、エンコーダを使用しないと決定してもよい。その場合、ＵＥは、上位レイヤシグナリング／物理レイヤシグナリング（ＲＲＣ／ＭＡＣ　ＣＥ／ＤＣＩ）、又は特定のルールに基づいて、入力情報に対して既存のＰＭＩ計算を行い、当該ＰＭＩを示す情報（ＣＳＩフィードバック）を、アンテナから送信してもよい（図７の実線）。 For example, if the target performance cannot be achieved by using the encoders (encoders #1 and #2) notified by the base station, it may be determined not to use the encoders. In that case, the UE performs existing PMI calculations on the input information based on upper layer signaling/physical layer signaling (RRC/MAC CE/DCI) or specific rules, and calculates information indicating the PMI (CSI feedback) based on the input information. ) may be transmitted from the antenna (solid line in FIG. 7).
（分析）
　ＡＩ／ＭＬベース（ＡＩ／ＭＬモデルを用いた）ＣＳＩフィードバック（ＡＩ支援ＣＳＩフィードバック）が検討されている。例えば、チャンネル行列（Ｈ）を圧縮し、報告することが検討されている。しかし、例えば、以下のようなことが明確になっていない。
・ＲＩをどのように報告するか。
・各ランクレベルの入力をどのように構成するか。
・各ランクレベルに対応するＡＩ／ＭＬモデルをどのように設定するか、想定するか。 (analysis)
AI/ML-based (using AI/ML models) CSI feedback (AI-assisted CSI feedback) is being considered. For example, compressing and reporting the channel matrix (H) is being considered. However, for example, the following points are not clear:
- How to report RI.
- How to structure the input for each rank level.
・How to set up or envisage AI/ML models corresponding to each rank level.
　ＡＩ／ＭＬモデルで固有ベクトル（Ｗ）を圧縮し、報告することが検討されている。かし、例えば、以下のようなことが明確になっていない。
・「レイヤ毎」と「ランク毎」のモードはどのように決定するか。
・レイヤ毎モードでは、各レイヤに対応するＡＩ／ＭＬモデルをどのように設定し、想定するか。
・ランク毎モードでは、ＲＩをどのように報告するか。
・ランク毎モードでは、各ランクレベルに対応するＡＩ／ＭＬモデルをどのように設定し、想定するか。 Compressing and reporting eigenvectors (W) in AI/ML models is being considered. However, for example, the following things are not clear.
・How do you decide between "layer-by-layer" and "rank-by-rank" modes?
・In layer-by-layer mode, how should the AI/ML model corresponding to each layer be set and assumed?
- How to report RI in rank-by-rank mode.
・In the rank-based mode, how should we set and envision the AI/ML model corresponding to each rank level?
　しかしながら、これらを適切に規定しなければ、ＡＩを用いた適切なＣＳＩフィードバックを実現できないおそれがある。これにより、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。 However, unless these are properly defined, there is a risk that appropriate CSI feedback using AI may not be realized. As a result, appropriate overhead reduction/highly accurate channel estimation/highly efficient resource utilization cannot be achieved, and there is a possibility that improvement in communication throughput/communication quality may be suppressed.
　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、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及びＣＳＩ干渉測定（CSI　Interference　Measurement（ＣＳＩ－ＩＭ））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 the present disclosure, CSI-RS, Non Zero Power (NZP) CSI-RS, Zero Power (ZP) CSI-RS, and CSI Interference Measurement (CSI-IM) are: They may be read interchangeably. Additionally, the CSI-RS may include other reference signals.
　また、本開示において、エンコーダ、エンコーディング、エンコード、エンコーダによる修正／変更／制御などは、互いに読み替えられてもよい。また、本開示において、デコーダ、デコーディング、デコード、デコーダによる修正／変更／制御などは、互いに読み替えられてもよい。 Furthermore, in the present disclosure, the terms encoder, encoding, encoding, modification/change/control by encoder, etc. may be read interchangeably. Furthermore, in the present disclosure, decoder, decoding, decoding, modification/change/control by decoder, etc. may be read interchangeably.
　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, 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、（エンコーダについての）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。本開示のレイヤは、入力層、中間層、出力層、バッチ正規化層、畳み込み層、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 (for encoders) may be interchanged with layers (input layer, intermediate layer, etc.) used in the AI model. The layer of the present disclosure may correspond to at least one of an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolution layer, a dropout layer, a fully connected layer, and the like.
　本開示において、プリコーディング行列についてのレイヤは、Multi　Input　Multi　Output（ＭＩＭＯ）レイヤ、ストリームなどと互いに読み替えられてもよい。 In the present disclosure, a layer regarding a precoding matrix may be interchanged with a Multi Input Multi Output (MIMO) layer, stream, 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.
　本開示において、ランク、ＲＩは、互いに読み替えられてもよい。ネットワーク（ＮＷ）、基地局、ｇＮＢは、互いに読み替えられてもよい。本開示において、入力、入力情報は、互いに読み替えられてもよい。 In the present disclosure, rank and RI may be read interchangeably. Network (NW), base station, and gNB may be read interchangeably. In the present disclosure, input and input information may be read interchangeably.
（無線通信方法）
＜第０の実施形態＞
　ＵＥは、ＡＩベースのＣＳＩフィードバックのためのＡＩ／ＭＬモデルに対する入力（入力情報）を決定する。そして、決定した入力をＡＩ／ＭＬモデルに適用し、ＡＩ／ＭＬモデルの出力（出力情報）を用いた、ＣＳＩに関する情報（例えば図４におけるエンコードされたビット）を基地局に送信する。入力として、以下の選択肢が挙げられる（図８）。ｐは前処理、ｑは量子化を意味する。各入力（Ｈ，Ｗ，ｐ－Ｈ…）は、１以上のＡＩ／ＭＬモデル（ＡＩ／ＭＬ　＃１～Ｎ）に対応する。
・チャネル行列（Ｈ）。
・プリコーディング行列／固有ベクトル（Ｗ）。
・前処理／量子化されたＨ（ｐ－Ｈ、Ｑ－Ｈ、Ｑｐ－Ｈ）。
・前処理／量子化されたＷ（Ｑ－Ｗ、ｐ－Ｗ、Ｑｐ－Ｗ）。 (Wireless communication method)
<0th embodiment>
The UE determines inputs to the AI/ML model for AI-based CSI feedback. Then, the determined input is applied to the AI/ML model, and information regarding the CSI (for example, encoded bits in FIG. 4) using the output (output information) of the AI/ML model is transmitted to the base station. The following options are available as input (Figure 8). p means preprocessing, and q means quantization. Each input (H, W, pH...) corresponds to one or more AI/ML models (AI/ML #1 to N).
- Channel matrix (H).
- Precoding matrix/eigenvector (W).
- Preprocessed/quantized H (p-H, Q-H, Qp-H).
- Preprocessed/quantized W (QW, pW, Qp-W).
　ＵＥは、仕様に定義された又はＮＷ（基地局）によって設定された特定のルールに従って入力を決定し、その決定した入力を報告してもよい。当該設定は、上述の（情報の受信）の例が適用されてもよい。報告には、上述の（情報の報告）の例が適用されてもよい。ＵＥは、例えば、ＳＩＮＲ＜閾値である場合、入力としてＨ（又は、前処理／後処理／量子化されたＨ）を決定してもよい。ＵＥは、例えば、上りリンクのリソースが限られている場合、Ｑ－Ｗ（量子化されたＷ）を入力として決定してもよい。 The UE may determine the input according to specific rules defined in the specifications or set by the NW (base station) and may report the determined input. The above-mentioned example (reception of information) may be applied to this setting. The example of (information reporting) described above may be applied to the report. The UE may, for example, determine H (or pre-processed/post-processed/quantized H) as input if SINR<threshold. The UE may determine QW (quantized W) as input, for example, when uplink resources are limited.
　ＵＥは、入力を、ＮＷ（基地局）から設定されてもよい。当該設定は、上述の（情報の受信）の例によりＵＥに送信されてもよい。 The UE may be configured with input from the NW (base station). The configuration may be sent to the UE according to the example of (receiving information) above.
＜第１の実施形態＞
　ＵＥは、以下のステップ１～３に示すように、前処理として、チャネル行列（Ｈ）にランク適応（Rank　adaptation）を行ってもよい。図９Ａは、前処理の概要を示す図である。図９Ｂは、ランク適応の詳細を示す図である。
ステップ１：ＵＥは、Ｈを分解して最高ランクＲと特異行列Λ（Ｒ）を取得する。
ステップ２：ＵＥは、ランク適応を行い、適応されたランクＲａ（＜Ｒ）と対応する特異行列Λ′（Ｒａ）を取得する。
ステップ３：ＵＥは、Ｈと等価なチャネル行列（Ｈａ）を、Λ′（Ｒａ）を用いて再構成する。 <First embodiment>
The UE may perform rank adaptation on the channel matrix (H) as pre-processing, as shown in steps 1 to 3 below. FIG. 9A is a diagram showing an overview of preprocessing. FIG. 9B is a diagram showing details of rank adaptation.
Step 1: The UE decomposes H to obtain the highest rank R and singular matrix Λ(R).
Step 2: The UE performs rank adaptation and obtains the singular matrix Λ′(R a ) corresponding to the adapted rank R a (<R).
Step 3: The UE reconstructs a channel matrix (H a ) equivalent to H using Λ′(R a ).
　本実施形態によれば、ＵＥは、ランクを用いた前処理を行うことにより、ランクに応じた適切なチャネル行列を取得することができる。 According to this embodiment, the UE can obtain an appropriate channel matrix according to the rank by performing preprocessing using the rank.
＜第２の実施形態＞
　ＵＥは、ＡＩ／ＭＬモデルの入力としてチャネル行列（Ｈ）、又は前処理／量子化されたＨ（ｐ－Ｈ、Ｑ－Ｈ、Ｑｐ－Ｈ）が選択された場合、当該入力に対応するＲＩ情報（ランク情報）を送信してもよいし、送信しなくてもよい。前処理は、第１の実施形態に示す処理であってもよい。 <Second embodiment>
If the channel matrix (H) or preprocessed/quantized H (p-H, Q-H, Qp-H) is selected as the input of the AI/ML model, the UE Information (rank information) may or may not be transmitted. The pre-processing may be the process shown in the first embodiment.
　図１０は、前処理されたＨの適用例を示す図である。図１０において、ＵＥは、Ｈを前処理して得られたＨａをＡＩ／ＭＬモデルに入力し、エンコードされたビットを取得する。このＨａに対応するランクＲａが、本実施形態のＲＩに相当する。 FIG. 10 is a diagram showing an example of application of preprocessed H. In FIG. 10, the UE inputs H a obtained by preprocessing H to the AI/ML model and obtains encoded bits. The rank R a corresponding to this H a corresponds to the RI of this embodiment.
［オプション１］
　ＵＥは、ＲＩ（＝Ｒａ）情報を送信してよい。ＵＥは、ＲＩ情報を、既存の仕様と同様の方法により送信してもよい。ＵＥは、ＲＩ情報を、Ｈａ（又は前処理／量子化されたＨａ）と共に圧縮し、送信してもよい。 [Option 1]
The UE may transmit RI (=R a ) information. The UE may transmit the RI information in a manner similar to existing specifications. The UE may compress and transmit the RI information together with H a (or preprocessed/quantized H a ).
［オプション２］
　ＵＥは、ＲＩ（＝Ｒａ）情報を送信しなくてもよい。この場合、ＮＷ（基地局）は、ＡＩ／ＭＬモデルの入出力、又は選択／報告されたＡＩ／ＭＬモデル情報（後述の第３の実施形態参照）、又は報告されたＡＩベースのＣＳＩフィードバックのペイロードサイズ（後述の第４の実施形態参照）からＲＩ情報を推測してもよい。 [Option 2]
The UE may not transmit RI (=R a ) information. In this case, the NW (base station) controls the input/output of the AI/ML model, the selected/reported AI/ML model information (see the third embodiment described below), or the reported AI-based CSI feedback. The RI information may be inferred from the payload size (see the fourth embodiment described below).
　本実施形態によれば、ＲＩの情報有無について明確になる。ＲＩ情報を圧縮したり、送信を省略することにより、通信容量を削減することができる。 According to this embodiment, the presence or absence of RI information becomes clear. Communication capacity can be reduced by compressing RI information or omitting transmission.
＜ＡＩモデル関連情報＞
　図１１は、ＡＩモデル関連情報の送信の一例を示す図である。本例では、ＵＥは、図示されるセルに侵入し、このセル（ＢＳ）に対して初期アクセス／ハンドオーバーを実施する。ＢＳは、ＡＩモデルの推論をサポートするか否かの能力を当該ＵＥに尋ねるメッセージを送信する。ＵＥは、これについての能力情報を送信する。 <AI model related information>
FIG. 11 is a diagram illustrating an example of transmission of AI model related information. In this example, the UE enters the illustrated cell and performs initial access/handover to this cell (BS). The BS sends a message asking the UE for its ability to support AI model inference or not. The UE sends capability information about it.
　ＡＩモデルの推論をサポートすることを示すＵＥ能力が報告されると、ＮＷ（基地局（ＢＳ））は適切なＡＩモデルを選択し、選択したＡＩモデルとともに、当該ＡＩモデルに関連する情報（以下、ＡＩモデル関連情報、単に関連情報などとも呼ぶ）を、ＵＥに転送してもよい。 When the UE capability indicating support for AI model inference is reported, the NW (base station (BS)) selects an appropriate AI model and, together with the selected AI model, information related to the AI model (hereinafter referred to as , AI model related information (also referred to simply as related information) may be transferred to the UE.
　図１２は、ＡＩモデル関連情報の一例を示す図である。関連情報は、モデルＩＤ、モデル機能、モデルの入力／出力、適用範囲などの情報の少なくとも１つを含んでもよい。 FIG. 12 is a diagram showing an example of AI model related information. The related information may include at least one of information such as model ID, model function, model input/output, and application range.
　図１２に示すように、モデルＩＤは、整数、文字列などを含んでもよい。モデル機能は、例えば、「最良のＣＳＩ－ＲＳを推定する」というＡＩモデルの機能の説明を含んでもよい。モデルの入力は、ＳＳＢ＃１－＃ｎのＲＳＲＰであってもよい。モデルの出力は、最良のＣＳＩ－ＲＳインデックス（ＣＳＩ－ＲＳリソースＩＤ）であってもよい。適用範囲は、例えば、ＡＩ支援技術に対応するセル（ＡＩ支援ビーム推定に基づくビームレポートの対象とできるセル、ＡＩに基づく予測／推定を用いてもよいセル）がセル＃１－＃３であることを示してもよい。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で示されてもよい。 As shown in FIG. 12, the model ID may include an integer, a character string, etc. The model functionality may include, for example, a description of the AI model's functionality of "estimate the best CSI-RS." The input of the model may be the RSRP of SSB #1-#n. The output of the model may be the best CSI-RS index (CSI-RS resource ID). The scope of application is, for example, cells that support AI-assisted technology (cells that can be subject to beam reports based on AI-assisted beam estimation, cells that may use prediction/estimation based on AI) are cells #1 to #3. It may also be shown that The coverage may be indicated by physical cell ID, serving cell index, etc.
　図１２に示すように、ＡＩモデル関連情報は、モデルＩＤと、他の関連情報（例えば、モデルの入力／出力など）を含んでもよいし、モデルＩＤのみを含んでもよい。 As shown in FIG. 12, the AI model related information may include the model ID and other related information (for example, model input/output, etc.), or may include only the model ID.
　ＵＥは、特定のルールに基づいて、モデルＩＤと、関連情報とを関連づけてもよい。例えば、ＵＥは、特定のルール及び受信したモデルＩＤに基づいて、適用するＡＩ／ＭＬモデルを決定してもよい。つまり、モデルＩＤは、他の関連情報にマップされてもよい。又は、ＵＥは、基地局から、モデルＩＤに対応する関連情報が何であるかを示す情報を受信してもよい。 The UE may associate the model ID with related information based on specific rules. For example, the UE may decide which AI/ML model to apply based on specific rules and the received model ID. That is, the model ID may be mapped to other related information. Alternatively, the UE may receive information from the base station indicating what the relevant information corresponding to the model ID is.
＜第３の実施形態＞
　ＵＥは、ＡＩ／ＭＬモデルの入力として、チャネル行列（Ｈ）又は前処理／量子化されたＨを選択する場合、ＲＩに基づいて、使用する適切なＡＩ／ＭＬモデルを選択してもよい。 <Third embodiment>
If the UE selects the channel matrix (H) or preprocessed/quantized H as input for the AI/ML model, it may select the appropriate AI/ML model to use based on the RI.
　［オプション１］
　ＵＥは、仕様に定義された又はＮＷ（基地局）によって設定された特定のルールに従ってＡＩ／ＭＬモデルを決定（選択）し、その決定したＡＩ／ＭＬを報告してもよい。設定には、上述の（情報の受信）の例が適用されてもよい。報告には、上述の（情報の報告）の例が適用されてもよい。報告には、上述のＡＩモデル関連情報が含まれてもよい。 [Option 1]
The UE may determine (select) an AI/ML model according to specific rules defined in the specifications or set by the NW (base station) and report the determined AI/ML. The example of (reception of information) described above may be applied to the settings. The example of (information reporting) described above may be applied to the report. The report may include the AI model related information described above.
　図１３は、ＡＩ／ＭＬモデルの選択の例を示す図である。ＵＥは、例えば、ＲＳＲＰ＜閾値である場合（Ｒａｎｋ１送信に限定）、ＡＩ／ＭＬモデルとして、２Ｄ－ＣＮＮ又はトランスフォーマーを決定してもよい。ＵＥは、例えば、ＲＳＲＰ≧閾値である場合（Ｒａｎｋ２以上）、ＡＩ／ＭＬモデルとして、３Ｄ－ＣＮＮ、又は、２Ｄアテンション及びトランスフォーマーを決定してもよい。ＵＥは、例えば、ＳＩＮＲに基づいてＡＩ／ＭＬモデルを決定してもよい。 FIG. 13 is a diagram showing an example of AI/ML model selection. For example, if RSRP<threshold (limited to Rank 1 transmission), the UE may determine 2D-CNN or a transformer as the AI/ML model. For example, if RSRP≧threshold (Rank 2 or higher), the UE may determine 3D-CNN or 2D attention and transformer as the AI/ML model. The UE may determine the AI/ML model based on the SINR, for example.
　［オプション２］
　ＵＥは、基地局から、ＡＩ／ＭＬモデルを設定されてもよい。当該設定は、上述の（情報の受信）の例が適用されてもよい。例えば、ＵＥは、ＲＩに対応するＡＩ／ＭＬモデルを設定され、ＲＩを報告してもよい。報告には、上述の（情報の報告）の例が適用されてもよい。ＵＥは、使用可能なＡＩ／ＭＬモデルのセット（複数のＡＩ／ＭＬ）が設定され、ＲＩ値に従って当該セットのうちの１つのＡＩ／ＭＬ選択し、選択したＡＩ／ＭＬモデルを報告してもよい。当該報告には、上述の（情報の報告）の例が適用されてもよい。 [Option 2]
The UE may be configured with an AI/ML model from the base station. The above-mentioned example (reception of information) may be applied to this setting. For example, the UE may be configured with an AI/ML model corresponding to the RI and may report the RI. The example of (information reporting) described above may be applied to the report. The UE may be configured with a set of available AI/ML models (multiple AI/MLs), select one AI/ML from the set according to the RI value, and report the selected AI/ML model. good. The above-mentioned example of (information report) may be applied to the report.
　本実施形態によれば、ＵＥは、ＲＩに応じた適切なＡＩ／ＭＬモデルを選択することができる。 According to this embodiment, the UE can select an appropriate AI/ML model according to the RI.
＜第４の実施形態＞
　ＵＥは、ＡＩ／ＭＬモデルの入力として、チャネル行列（Ｈ）又は前処理／量子化されたＨを選択した場合、ランク（ＲＩ）に基づいて、ＡＩ／ＭＬモデルの出力に適したペイロードサイズ（出力ビット）を決定してもよい。ＵＥは、例えば、ランクが大きいほど大きいペイロードサイズを決定してもよい。 <Fourth embodiment>
If the UE selects the channel matrix (H) or the preprocessed/quantized H as the input of the AI/ML model, it determines the appropriate payload size (H) for the output of the AI/ML model based on the rank (RI). output bits). The UE may, for example, determine a larger payload size for a higher rank.
　ＵＥは、異なるペイロードサイズを持つ複数のビットを出力可能なＡＩ／ＭＬモデルを適用する場合、ランクに関する情報に従って、どの出力ビットを報告するか決定してもよい。ＵＥは、当該出力ビットを、仕様で定義された一定の規則ルール、ＮＷにより設定されたルールにより決定してもよい。 When applying an AI/ML model that can output multiple bits with different payload sizes, the UE may decide which output bits to report according to information regarding the rank. The UE may determine the output bit according to certain rules defined in the specifications, rules set by the NW.
　図１４は、ＲＩ毎のペイロードサイズの例を示す図である。図１４の例では、ＡＩ／ＭＬ　＃２が選択され、ＲＩ＝２の場合、ペイロードサイズ＝１７５ビットであり、ＲＩ＝３の場合、ペイロードサイズ＝２５５ビットであり、ＲＩ＝４の場合、ペイロードサイズ＝３２５ビットである。 FIG. 14 is a diagram showing an example of payload size for each RI. In the example of FIG. 14, AI/ML #2 is selected, and when RI=2, payload size=175 bits, when RI=3, payload size=255 bits, and when RI=4, payload Size=325 bits.
［オプション１］
　ＵＥは、どの出力ビットが報告されるかに関する情報を報告してもよい。当該情報は、例えば、対応するＲＩ、ペイロードサイズ、又はビットインデックスなどである。 [Option 1]
The UE may report information regarding which output bits are reported. The information is, for example, the corresponding RI, payload size, or bit index.
［オプション２］
　ＵＥは、どの出力ビットが報告されるかに関する情報を報告しなくてもよい。ＮＷは、受信したビット基づいて、どのビットが報告されたかを判断するためのブラインドデコードを行ってもよい。 [Option 2]
The UE may not report information regarding which output bits are reported. The NW may perform blind decoding to determine which bits were reported based on the received bits.
　本実施形態によれば、ＵＥは、ＲＩに応じた適切なペイロードサイズ（出力ビット）を決定することができる。 According to this embodiment, the UE can determine an appropriate payload size (output bits) according to the RI.
＜第５の実施形態＞
　ＵＥは、ＡＩ／ＭＬモデルの入力として、プリコーディング行列／固有ベクトル（Ｗ）又は前処理／量子化されたＷが選択された場合、ＡＩ（ＡＩ／ＭＬモデル）ベースのＣＳＩフィードバックにレイヤ毎モード又はランク毎モードを使用することを決定してもよい。レイヤ毎モード、ランク毎モードとして、図６の例が適用されてもよい。ＵＥは、例えば、ＵＥ能力に基づいてレイヤ毎モード又はランク毎モードを決定してもよい。 <Fifth embodiment>
The UE applies layer-by-layer mode or You may decide to use per-rank mode. The example of FIG. 6 may be applied as the layer-by-layer mode and rank-by-rank mode. The UE may decide on a per-layer mode or a per-rank mode based on UE capabilities, for example.
　レイヤ毎モードは、レイヤ毎のＡＩ／ＭＬモデル（例えば、レイヤ毎に異なる／独立したＡＩ／ＭＬモデル）を使用するモードを意味する。同様に、ランク毎モードは、ランク毎のＡＩ／ＭＬモデル（例えば、ランク毎に異なる／独立したＡＩ／ＭＬモデル）を使用するモードを意味する。 The layer-by-layer mode means a mode that uses a layer-by-layer AI/ML model (for example, a different/independent AI/ML model for each layer). Similarly, per-rank mode refers to a mode that uses a per-rank AI/ML model (eg, a different/independent AI/ML model for each rank).
［オプション１］
　ＵＥは、仕様に定義された特定のルール、ＮＷ（基地局）によって設定されたルールに従ってモードを決定し、決定内容を報告してもよい。報告には、上述の（情報の報告）の例が適用されてもよい。例えば、ＵＥの計算能力又はＵＥの記憶容量が閾値より低い場合、レイヤ毎モードを適用し、そうでない場合、ランク毎モードを適用してもよい。 [Option 1]
The UE may decide the mode according to specific rules defined in the specifications, rules set by the NW (base station), and report the decision. The example of (information reporting) described above may be applied to the report. For example, if the computing power of the UE or the storage capacity of the UE is lower than a threshold, the per-layer mode may be applied, otherwise the per-rank mode may be applied.
［オプション２］
　ＵＥは、ＮＷ（基地局）から、レイヤ毎モード又はランク毎モードを設定されてもよい。当該設定には、上述の（情報の受信）の例が適用されてもよい。ＵＥは、ＮＷに対して、計算能力および記憶容量を報告してもよい。 [Option 2]
The UE may be set to a per-layer mode or a per-rank mode from the NW (base station). The above-mentioned example (receiving information) may be applied to this setting. The UE may report computing and storage capacity to the NW.
　本実施形態によれば、ＡＩ（ＡＩ／ＭＬモデル）ベースのＣＳＩフィードバックにレイヤ毎モード又はランク毎モードのいずれを使用するか適切に決定することができる。 According to this embodiment, it is possible to appropriately determine whether to use the layer-by-layer mode or the rank-by-rank mode for AI (AI/ML model)-based CSI feedback.
＜第６の実施形態＞
　ＵＥは、ＡＩ／ＭＬモデルの入力として、プリコーディング行列／固有ベクトル（Ｗ）又は前処理／量子化されたプリコーディング行列／固有ベクトル（Ｗ）が選択され、ランク毎モードが決定された場合、第２～第４の実施形態は再利用されてもよい。つまり、第２～第４の実施形態のチャネル行列（Ｈ）は、プリコーディング行列／固有ベクトル（Ｗ）に置き換えられてもよい。 <Sixth embodiment>
If a precoding matrix/eigenvector (W) or a preprocessed/quantized precoding matrix/eigenvector (W) is selected as an input of the AI/ML model and a per-rank mode is determined, the UE ~The fourth embodiment may be reused. That is, the channel matrix (H) of the second to fourth embodiments may be replaced with a precoding matrix/eigenvector (W).
　ＵＥは、Ｗ又は前処理／量子化されたＷが選択され、ランク毎モードが決定された場合、ＲＩ情報を送信してもよいし、送信しなくてもよい（第２の実施形態の再利用）。 The UE may or may not transmit the RI information when W or preprocessed/quantized W is selected and the per-rank mode is determined. use).
　ＵＥは、Ｗ又は前処理／量子化されたＷが選択され、ランク毎モードが決定された場合、ＲＩに従って、使用する適切なＡＩ／ＭＬモデルを選択してもよい（第３の実施形態の再利用）。 The UE may select the appropriate AI/ML model to use according to the RI if W or preprocessed/quantized W is selected and per-rank mode is determined (as in the third embodiment). Reuse).
　ＵＥは、Ｗ又は前処理／量子化されたＷが選択され、ランク毎モードが決定された場合、ＲＩに従って、ＡＩ／ＭＬモデルの出力に適したペイロードサイズ（出力ビット）を選択してもよい（第４の実施形態の再利用）。 The UE may select an appropriate payload size (output bits) for the output of the AI/ML model according to the RI if W or preprocessed/quantized W is selected and per-rank mode is determined. (Reuse of the fourth embodiment).
＜第７の実施形態＞
　ＵＥは、ＡＩ／ＭＬモデルの入力として、プリコーディング行列／固有ベクトル（Ｗ）又は、前処理／量子化されたプリコーディング行列／固有ベクトル（Ｗ）が選択され、レイヤ毎モードが決定された場合、レイヤ毎のＡＩ／ＭＬモデルを決定（選択）してもよい。この場合、以下のオプション１，２が適用されてもよい。 <Seventh embodiment>
If a precoding matrix/eigenvector (W) or a preprocessed/quantized precoding matrix/eigenvector (W) is selected as the input of the AI/ML model and the mode for each layer is determined, the UE You may decide (select) an AI/ML model for each. In this case, options 1 and 2 below may be applied.
［オプション１］
　ＵＥは、仕様で定義された一定の規則ルール、ＮＷにより設定されたルールにより、ＡＩ／ＭＬモデルを決定（選択）し、報告してもよい。当該設定には、上述の（情報の受信）の例が適用されてもよい。当該報告には、上述の（情報の報告）の例が適用されてもよい。 [Option 1]
The UE may decide (select) an AI/ML model and report it according to certain rules defined in the specifications and rules set by the NW. The above-mentioned example (receiving information) may be applied to this setting. The above-mentioned example of (information report) may be applied to the report.
［オプション２］
　ＵＥは、ＮＷによって、ＡＩ／ＭＬモデルが設定されてもよい。当該設定には、上述の（情報の受信）の例が適用されてもよい。ＵＥは、１つのＡＩ／ＭＬモデルを設定されてもよい。ＵＥは、使用可能なＡＩ／ＭＬモデルのセット（複数のＡＩ／ＭＬモデル）を設定され、そのセットから１つのＡＩ／ＭＬモデルを選択し、報告してもよい。報告には、上述の（情報の報告）の例が適用されてもよい。 [Option 2]
The UE may be configured with an AI/ML model by the NW. The above-mentioned example (receiving information) may be applied to this setting. A UE may be configured with one AI/ML model. The UE is configured with a set of available AI/ML models (multiple AI/ML models) and may select and report one AI/ML model from the set. The example of (information reporting) described above may be applied to the report.
　ＵＥは、以下の（１）～（３）のいずれかの方法でＡＩ／ＭＬモデルを決定（選択）してもよい。
（１）ＵＥは、全てのレイヤにおいて同一のＡＩ／ＭＬモデルを選択する。
（２）ＵＥは、レイヤ毎に独立したＡＩ／ＭＬモデルを選択する。
（３）（１）と（２）の組み合わせ。 The UE may determine (select) an AI/ML model using any of the following methods (1) to (3).
(1) The UE selects the same AI/ML model in all layers.
(2) The UE selects an independent AI/ML model for each layer.
(3) A combination of (1) and (2).
　図１５は、第７の実施形態におけるＡＩ／ＭＬモデルの決定を示す図である。図１５に示す例では、ＵＥは、ＡＩ／ＭＬモデルの入力として、プリコーディング行列／固有ベクトル（Ｗ）を決定（選択）し、レイヤ毎にＡＩ／ＭＬモデルを決定（選択）する。レイヤ１，２のように複数のレイヤにおいて同じＡＩ／ＭＬモデル（ＡＩ／ＭＬ＃１）を決定してもよい。 FIG. 15 is a diagram showing determination of an AI/ML model in the seventh embodiment. In the example shown in FIG. 15, the UE determines (selects) a precoding matrix/eigenvector (W) as an input for the AI/ML model, and determines (selects) the AI/ML model for each layer. The same AI/ML model (AI/ML #1) may be determined in multiple layers such as layers 1 and 2.
　本実施形態によれば、レイヤ毎に適切なＡＩ／ＭＬモデルを決定（選択）することができる。 According to this embodiment, an appropriate AI/ML model can be determined (selected) for each layer.
＜補足＞
　上述の実施形態の少なくとも１つは、特定のＵＥ能力（UE　capability）を送信（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。 <Supplement>
At least one of the embodiments described above may apply only to UEs that have transmitted (reported) or support a particular UE capability.
　当該特定のＵＥ能力は、上記実施形態／オプションの少なくとも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、上記実施形態／オプションの少なくとも１つを適用することを示してもよい。 The specific UE capability may indicate supporting specific processing/operation/control/information for at least one of the above embodiments/options, applying at least one of the above embodiments/options. .
　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、ＢＷＰ、バンドコンビネーション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）、Feature　Set　Per　Component-carrier（ＦＳＰＣ）ごとの能力であってもよい。 In addition, the above-mentioned specific UE capabilities may be capabilities that are applied across all frequencies (commonly regardless of frequency), or capabilities that are applied per frequency (for example, per cell, band, BWP, or band combination). Alternatively, it may be a capability for each frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or it may be a capability for each frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or it may be a capability for each frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or it may be a capability for each frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2). ), or it may be an ability 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又は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を設定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／ＭＬモデルを用いたＣＳＩフィードバック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。 Also, at least one of the embodiments described above may be applied when the UE is configured with specific information related to the embodiments described above by upper layer signaling or physical layer signaling. For example, the specific information may be information indicating that CSI feedback using an AI/ML 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］
　Channel　State　Information　Reference　Signal（ＣＳＩ）フィードバックのためのArtificial　Intelligence（ＡＩ）／Machine　Learning（ＭＬ）モデルに対する入力を決定し、当該入力を当該ＡＩ／ＭＬモデルに適用する制御部と、
　前記ＡＩ／ＭＬモデルの出力を用いた、ＣＳＩに関する情報を送信する送信部と、
　を有する端末。
［付記２］
　前記制御部は、前記入力として、前処理及び量子化の少なくとも１つが行われたチャネル行列を選択し、
　前記送信部は、前記チャネル行列に対応するRank　Indicator（ＲＩ）情報を送信する
　付記１に記載の端末。
［付記３］
　前記制御部は、前記入力として、前処理及び量子化の少なくとも１つが行われたチャネル行列を選択した場合、Rank　Indicator（ＲＩ）に基づいて、前記ＡＩ／ＭＬモデルを決定する
　付記１又は付記２に記載の端末。
［付記４］
　前記制御部は、前記入力として、前処理及び量子化の少なくとも１つが行われたプリコーディング行列又は固有ベクトルを選択した場合、レイヤ毎の前記ＡＩ／ＭＬモデルを使用するモード、又はランク毎の前記ＡＩ／ＭＬモデルを使用するモードを使用することを決定する
　付記１から付記３のいずれかに記載の端末。 (Additional note)
Regarding one embodiment of the present disclosure, the following invention will be added.
[Additional note 1]
a control unit that determines an input to an Artificial Intelligence (AI)/Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback and applies the input to the AI/ML model;
a transmitting unit that transmits information regarding CSI using the output of the AI/ML model;
A terminal with
[Additional note 2]
The control unit selects, as the input, a channel matrix that has been subjected to at least one of preprocessing and quantization,
The terminal according to supplementary note 1, wherein the transmitter transmits Rank Indicator (RI) information corresponding to the channel matrix.
[Additional note 3]
When the control unit selects as the input a channel matrix that has undergone at least one of preprocessing and quantization, the control unit determines the AI/ML model based on a Rank Indicator (RI). Appendix 1 or 2 Terminals listed in .
[Additional note 4]
When the control unit selects as the input a precoding matrix or eigenvector that has been subjected to at least one of preprocessing and quantization, the control unit selects a mode in which the AI/ML model for each layer is used or the AI for each rank. /The terminal according to any one of Supplementary Notes 1 to 3, which determines to use a mode that uses a ML model.
（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１６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. 16 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 use 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. Further, 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, an 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. Furthermore, a Master Information Block (MIB) may be transmitted via the 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１７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. 17 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 transmitter/receiver 120 may form at least one of a transmit beam and a receive beam using digital beam forming (e.g., precoding), analog beam forming (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、参照情報（例えばＣＳＩ－ＲＳ）を送信してもよい。送受信部１２０は、前記参照信号に対応するChannel　State　Information　Reference　Signal（ＣＳＩ）フィードバックのためのArtificial　Intelligence（ＡＩ）／Machine　Learning（ＭＬ）モデルに対する入力が端末において決定され、当該入力が当該ＡＩ／ＭＬモデルに適用された場合、前記ＡＩ／ＭＬモデルの出力を用いた、ＣＳＩに関する情報を受信してもよい。 Note that the transmitter/receiver 120 may transmit reference information (for example, CSI-RS). The transmitting/receiving unit 120 determines an input to an Artificial Intelligence (AI)/Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback corresponding to the reference signal at the terminal, and inputs the input into the AI/ML model. When applied to a model, information regarding CSI using the output of the AI/ML model may be received.
（ユーザ端末）
　図１８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. 18 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.
　制御部２１０は、Channel　State　Information　Reference　Signal（ＣＳＩ）フィードバックのためのArtificial　Intelligence（ＡＩ）／Machine　Learning（ＭＬ）モデルに対する入力を決定し、当該入力を当該ＡＩ／ＭＬモデルに適用してもよい。送受信部２２０は、前記ＡＩ／ＭＬモデルの出力を用いた、ＣＳＩに関する情報を送信してもよい。 The control unit 210 may determine inputs to an Artificial Intelligence (AI)/Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback, and apply the inputs to the AI/ML model. The transmitter/receiver 220 may transmit information regarding CSI using the output of the AI/ML model.
　制御部２１０は、前記入力として、前処理及び量子化の少なくとも１つが行われたチャネル行列を選択してもよい。送受信部２２０は、前記チャネル行列に対応するRank　Indicator（ＲＩ）情報を送信してもよい。 The control unit 210 may select, as the input, a channel matrix that has undergone at least one of preprocessing and quantization. The transmitter/receiver 220 may transmit Rank Indicator (RI) information corresponding to the channel matrix.
　制御部２１０は、前記入力として、前処理及び量子化の少なくとも１つが行われたチャネル行列を選択した場合、Rank　Indicator（ＲＩ）に基づいて、前記ＡＩ／ＭＬモデルを決定してもよい。 When the control unit 210 selects a channel matrix that has undergone at least one of preprocessing and quantization as the input, the control unit 210 may determine the AI/ML model based on the Rank Indicator (RI).
　制御部２１０は、前記入力として、前処理及び量子化の少なくとも１つが行われたプリコーディング行列又は固有ベクトルを選択した場合、レイヤ毎の前記ＡＩ／ＭＬモデルを使用するモード、又はランク毎の前記ＡＩ／ＭＬモデルを使用するモードを使用することを決定してもよい。 When the control unit 210 selects as the input a precoding matrix or eigenvector that has been subjected to at least one of preprocessing and quantization, the control unit 210 selects a mode in which the AI/ML model for each layer is used or the AI for each rank. /ML model may be decided to be used.
（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１９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. 19 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 (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 a 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 in 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. that 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２０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. 20 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 unit 41, steering unit 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 done by a Mobility Management Entity (MME), a Serving-Gateway (S-GW), etc. (but not limited to these) 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. Further, 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」、「広い」、「狭い」、などを意味する文言は、原級、比較級及び最上級に限らず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」、「広い」、「狭い」などを意味する文言は、「ｉ番目に」（ｉは任意の整数）を付けた表現として、原級、比較級及び最上級に限らず互いに読み替えられてもよい（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 this disclosure, "good", "bad", "large", "small", "high", "low", "early", "slow", "wide", "narrow", etc. The words are not limited to the original, comparative, and superlative, and may be interpreted interchangeably. In addition, in this disclosure, words meaning "good", "bad", "large", "small", "high", "low", "early", "slow", "wide", "narrow", etc. may be interpreted as an expression with "the i-th" (i is any integer), not only in the elementary, comparative, and superlative, but also interchangeably (for example, "the highest" can be interpreted as "the i-th highest"). may be read interchangeably).
　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 (6)

  1. 　Channel　State　Information　Reference　Signal（ＣＳＩ）フィードバックのためのArtificial　Intelligence（ＡＩ）／Machine　Learning（ＭＬ）モデルに対する入力を決定し、当該入力を当該ＡＩ／ＭＬモデルに適用する制御部と、
    　前記ＡＩ／ＭＬモデルの出力を用いた、ＣＳＩに関する情報を送信する送信部と、
    　を有する端末。     A control unit that determines an input to an Artificial Intelligence (AI)/Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback and applies the input to the AI/ML model;
    a transmitting unit that transmits information regarding CSI using the output of the AI/ML model;
    A terminal with
  2. 　前記制御部は、前記入力として、前処理及び量子化の少なくとも１つが行われたチャネル行列を選択し、
    　前記送信部は、前記チャネル行列に対応するRank　Indicator（ＲＩ）情報を送信する
    　請求項１に記載の端末。     The control unit selects, as the input, a channel matrix that has been subjected to at least one of preprocessing and quantization,
    The terminal according to claim 1, wherein the transmitter transmits Rank Indicator (RI) information corresponding to the channel matrix.
  3. 　前記制御部は、前記入力として、前処理及び量子化の少なくとも１つが行われたチャネル行列を選択した場合、Rank　Indicator（ＲＩ）に基づいて、前記ＡＩ／ＭＬモデルを決定する
    　請求項１に記載の端末。     The control unit determines the AI/ML model based on a Rank Indicator (RI) when selecting, as the input, a channel matrix that has undergone at least one of preprocessing and quantization. terminal.
  4. 　前記制御部は、前記入力として、前処理及び量子化の少なくとも１つが行われたプリコーディング行列又は固有ベクトルを選択した場合、レイヤ毎の前記ＡＩ／ＭＬモデルを使用するモード、又はランク毎の前記ＡＩ／ＭＬモデルを使用するモードを使用することを決定する
    　請求項１に記載の端末。     When the control unit selects as the input a precoding matrix or eigenvector that has been subjected to at least one of preprocessing and quantization, the control unit selects a mode in which the AI/ML model for each layer is used or the AI for each rank. The terminal according to claim 1, wherein the terminal determines to use a mode that uses a /ML model.
  5. 　Channel　State　Information　Reference　Signal（ＣＳＩ）フィードバックのためのArtificial　Intelligence（ＡＩ）／Machine　Learning（ＭＬ）モデルに対する入力を決定し、当該入力を当該ＡＩ／ＭＬモデルに適用する工程と、
    　前記ＡＩ／ＭＬモデルの出力を用いた、ＣＳＩに関する情報を送信する工程と、
    　を有する端末の無線通信方法。     determining inputs to an Artificial Intelligence (AI)/Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback and applying the inputs to the AI/ML model;
    transmitting information regarding CSI using the output of the AI/ML model;
    A wireless communication method for a terminal having
  6. 　参照信号を送信する送信部と、
    　前記参照信号に対応するChannel　State　Information　Reference　Signal（ＣＳＩ）フィードバックのためのArtificial　Intelligence（ＡＩ）／Machine　Learning（ＭＬ）モデルに対する入力が端末において決定され、当該入力が当該ＡＩ／ＭＬモデルに適用された場合、前記ＡＩ／ＭＬモデルの出力を用いた、ＣＳＩに関する情報を受信する受信部と、
    　を有する基地局。     a transmitter that transmits a reference signal;
    An input to an Artificial Intelligence (AI)/Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback corresponding to the reference signal is determined at the terminal, and the input is applied to the AI/ML model. a receiving unit that receives information regarding CSI using the output of the AI/ML model;
    A base station with
PCT/JP2022/026446 2022-07-01 2022-07-01 Terminal, wireless communication method, and base station WO2024004194A1 (en)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2020519177A (en) * 2017-05-05 2020-06-25 エイ・ティ・アンド・ティ インテレクチュアル プロパティ アイ，エル．ピー． Multi-antenna transmission protocol for high Doppler conditions
US20200366326A1 (en) * 2019-05-15 2020-11-19 Huawei Technologies Co., Ltd. Systems and methods for signaling for ai use by mobile stations in wireless networks

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2020519177A (en) * 2017-05-05 2020-06-25 エイ・ティ・アンド・ティ インテレクチュアル プロパティ アイ，エル．ピー． Multi-antenna transmission protocol for high Doppler conditions
US20200366326A1 (en) * 2019-05-15 2020-11-19 Huawei Technologies Co., Ltd. Systems and methods for signaling for ai use by mobile stations in wireless networks

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