WO2023199439A1 - Terminal, procédé de communication radio et station de base - Google Patents

Terminal, procédé de communication radio et station de base Download PDF

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Publication number
WO2023199439A1
WO2023199439A1 PCT/JP2022/017735 JP2022017735W WO2023199439A1 WO 2023199439 A1 WO2023199439 A1 WO 2023199439A1 JP 2022017735 W JP2022017735 W JP 2022017735W WO 2023199439 A1 WO2023199439 A1 WO 2023199439A1
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model
information
training
information regarding
trained
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PCT/JP2022/017735
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English (en)
Japanese (ja)
Inventor
春陽 越後
浩樹 原田
眞人 谷口
天楊 閔
壮輝 渡邊
拓実 藤塚
ヤジュオ グァン
一成 中村
リュー リュー
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株式会社Ｎｔｔドコモ
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Priority to PCT/JP2022/017735 priority Critical patent/WO2023199439A1/fr
Publication of WO2023199439A1 publication Critical patent/WO2023199439A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
    • H04W88/02—Terminal devices

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
  • AI model training/model inference at a terminal can be appropriately controlled.
  • UE user equipment
  • the specific details of the control have not yet been studied. If this control is not appropriately defined, appropriate overhead reduction/highly accurate channel estimation/highly efficient resource utilization cannot be achieved, and there is a risk that improvement in communication throughput/communication quality may be suppressed.
  • one of the purposes of the present disclosure is to provide a terminal, a wireless communication method, and a base station that can realize suitable overhead reduction/channel estimation/resource utilization.
  • a terminal receives NW training model information regarding a model (NW training model) trained by a network (Network (NW)) or reference model information regarding a reference model that is a base of the NW training model. It has a receiving section, a control section that further trains a model determined based on the NW training model information or the reference model information, and a transmitting section that transmits information regarding the trained model.
  • suitable overhead reduction/channel estimation/resource utilization can be achieved.
  • FIG. 1 is a diagram illustrating an example of an AI model management framework.
  • FIG. 2 is a diagram showing an example of an AI model.
  • FIG. 3 is a diagram illustrating an example of specifying an AI model according to the first embodiment.
  • FIG. 4 is a diagram illustrating an example of updating the AI model according to the first embodiment.
  • FIG. 5 is a diagram showing an example of fine tuning.
  • FIG. 6 is a diagram illustrating an example of transfer learning.
  • FIG. 7 is a diagram illustrating an example of updating the AI model according to the first embodiment.
  • FIG. 8 is a diagram illustrating an example of specifying an AI model according to the second embodiment.
  • FIG. 9 is a diagram illustrating an example of updating the AI model according to the second embodiment.
  • FIG. 1 is a diagram illustrating an example of an AI model management framework.
  • FIG. 2 is a diagram showing an example of an AI model.
  • FIG. 3 is a diagram illustrating an example of specifying an AI model according to the first embodiment.
  • FIG. 10 is a diagram illustrating an example of updating the AI model according to the second embodiment.
  • FIG. 11 is a diagram illustrating an example of specifying an AI model according to the third embodiment.
  • FIG. 12 is a diagram illustrating an example of specifying an AI model according to the third embodiment.
  • 13A-13B are diagrams illustrating an example of UE training AI model information according to the third embodiment.
  • 14A-14B are diagrams illustrating an example of UE training AI model information according to the third embodiment.
  • FIG. 15 is a diagram illustrating an example of exchange of AI model information regarding federated learning.
  • 16A to 16C are diagrams showing examples of updated AI model information.
  • 17A and 17B are diagrams illustrating an example of learning using knowledge distillation.
  • FIG. 18 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment.
  • FIG. 19 is a diagram illustrating an example of the configuration of a base station according to an embodiment.
  • FIG. 20 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment.
  • FIG. 21 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment.
  • FIG. 22 is a diagram illustrating an example of a vehicle according to an embodiment.
  • AI Artificial Intelligence
  • ML machine learning
  • improved Channel State Information Reference Signal 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 performance feedback (feeding back model performance to the entity training the model), and output (feeding back model performance to the actor). (Providing the output of the model).
  • the actor stage includes action triggers (e.g., deciding whether to trigger an action on other entities), feedback (e.g., feeding back information necessary for training data/inference data/performance feedback), etc. May include.
  • action triggers e.g., deciding whether to trigger an action on other entities
  • feedback e.g., feeding back information necessary for training data/inference data/performance feedback
  • training of a model for mobility optimization may be performed, for example, in Operation, Administration and Maintenance (Management)/gNodeB (gNB) in a network (NW).
  • Management Administration and Maintenance
  • gNB gNodeB
  • NW network
  • the former has advantages in interoperability, large storage capacity, operator manageability, and model flexibility (e.g., feature engineering).
  • model flexibility e.g., feature engineering
  • 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.
  • AI model training can be performed in the UE. This is because the UE may be able to directly obtain data that cannot be directly used by the gNB/OAM. For example, model training of an autoencoder for CSI feedback requires complete downlink CSI information (or channel information), but only the UE can directly obtain this information.
  • the UE needs to transmit complete downlink CSI information (or channel information), but the communication overhead is large.
  • the UE needs to maintain complete downlink CSI information (or channel information), but the UE's storage/computation resources are scarce compared to gNB/OAM. .
  • the present inventors conceived of a suitable control method for model training/model inference. Note that each embodiment of the present disclosure may be applied when AI/prediction is not used.
  • a terminal User Equipment (UE)
  • BS Base Station
  • UE User Equipment
  • BS Base Station
  • UE User Equipment
  • inference mode the accuracy of the trained ML model trained in the training mode may be verified.
  • 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.
  • 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.
  • the training mode may correspond to a mode in which the UE/BS transmits/receives signals for the ML model (in other words, an operation mode during the training period).
  • the inference mode may correspond to a mode in which the UE/BS implements an ML model (e.g., implements a trained ML model to predict an output) (in other words, an operating mode during the inference period). good.
  • the training mode may mean a mode in which a specific signal transmitted in the inference mode has a large overhead (for example, a large amount of resources).
  • training mode may mean a mode that refers to a first configuration (for example, a first DMRS configuration, a first CSI-RS configuration, a first CSI reporting configuration).
  • inference mode refers to a mode that refers to a second configuration different from the first configuration (e.g., second DMRS configuration, second CSI-RS configuration, second CSI reporting configuration). You may.
  • the first setting at least one of measurement-related time resources, frequency resources, code resources, and ports (antenna ports) may be set more than in the second setting.
  • the CSI report settings may include settings related to an autoencoder.
  • the relevant entities are 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.
  • A/B and “at least one of A and B” may be read interchangeably. Furthermore, in the present disclosure, “A/B/C” may mean “at least one of A, B, and C.”
  • Radio Resource Control RRC
  • RRC parameters RRC parameters
  • RRC messages upper layer parameters, fields, Information Elements (IEs), settings, etc.
  • IEs Information Elements
  • CE Medium Access Control Element
  • update command activation/deactivation command, etc.
  • the upper layer signaling may be, for example, Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information, etc., or a combination thereof.
  • RRC Radio Resource Control
  • MAC Medium Access Control
  • MAC signaling may use, for example, a MAC Control Element (MAC CE), a MAC Protocol Data Unit (PDU), or the like.
  • Broadcast information includes, for example, a master information block (MIB), a system information block (SIB), a minimum system information (RMSI), and other system information ( Other System Information (OSI)) may also be used.
  • MIB master information block
  • SIB system information block
  • RMSI minimum system information
  • OSI Other System Information
  • the physical layer signaling may be, for example, downlink control information (DCI), uplink control information (UCI), etc.
  • DCI downlink control information
  • UCI uplink control information
  • an index an identifier (ID), an indicator, a resource ID, etc.
  • ID an identifier
  • indicator an indicator
  • resource ID a resource ID
  • sequences, lists, sets, groups, groups, clusters, subsets, etc. may be used interchangeably.
  • a panel, a UE panel, a panel group, a beam, a beam group, a precoder, an uplink (UL) transmitting entity, a transmission/reception point (TRP), a base station, and a spatial relation information (SRI) are described.
  • SRS resource indicator SRI
  • control resource set CONtrol REsource SET (CORESET)
  • Physical Downlink Shared Channel PDSCH
  • codeword CW
  • Transport Block Transport Block
  • TB transport Block
  • RS reference signal
  • antenna port e.g. demodulation reference signal (DMRS) port
  • antenna port group e.g.
  • DMRS port group groups (e.g., spatial relationship groups, Code Division Multiplexing (CDM) groups, reference signal groups, CORESET groups, Physical Uplink Control Channel (PUCCH) groups, PUCCH resource groups), resources (e.g., reference signal resources, SRS resource), resource set (for example, reference signal resource set), CORESET pool, downlink Transmission Configuration Indication state (TCI state) (DL TCI state), uplink TCI state (UL TCI state), unified TCI Unified TCI state, common TCI state, quasi-co-location (QCL), QCL assumption, etc. may be read interchangeably.
  • groups e.g., spatial relationship groups, Code Division Multiplexing (CDM) groups, reference signal groups, CORESET groups, Physical Uplink Control Channel (PUCCH) groups, PUCCH resource groups
  • resources e.g., reference signal resources, SRS resource
  • resource set for example, reference signal resource set
  • CORESET pool downlink Transmission Configuration Indication state (TCI state) (DL TCI state), up
  • CSI-RS Non Zero Power (NZP) CSI-RS, Zero Power (ZP) CSI-RS, and CSI Interference Measurement (CSI-IM) are: They may be read interchangeably. Additionally, the CSI-RS may include other reference signals.
  • NZP Non Zero Power
  • ZP Zero Power
  • CSI-IM CSI Interference Measurement
  • RS to be measured/reported may mean RS to be measured/reported for CSI reporting.
  • timing, time, time, slot, subslot, symbol, subframe, etc. may be read interchangeably.
  • direction, axis, dimension, domain, polarization, polarization component, etc. may be read interchangeably.
  • the RS may be, for example, a CSI-RS, an SS/PBCH block (SS block (SSB)), or the like.
  • the RS index may be a CSI-RS resource indicator (CSI-RS resource indicator (CRI)), an SS/PBCH block resource indicator (SS/PBCH block indicator (SSBRI)), or the like.
  • estimation, prediction, and inference may be used interchangeably.
  • estimate the terms “estimate,” “predict,” and “infer” may be used interchangeably.
  • autoencoder, encoder, decoder, etc. may be replaced with at least one of a model, ML model, neural network model, AI model, AI algorithm, etc. Further, the autoencoder may be interchanged with any autoencoder such as a stacked autoencoder or a convolutional autoencoder.
  • the encoder/decoder of the present disclosure may adopt models such as Residual Network (ResNet), DenseNet, RefineNet, etc.
  • encoder encoding, encoding, modification/change/control by encoder, etc.
  • decoder decoding, decoding, modification/change/control by decoder, etc.
  • channel measurement/estimation includes, for example, a channel state information reference signal (CSI-RS), a synchronization signal (SS), a synchronization signal/broadcast channel (Synchronization Signal/Physical It may be performed using at least one of a Broadcast Channel (SS/PBCH) block, a demodulation reference signal (DMRS), a measurement reference signal (Sounding Reference Signal (SRS)), and the like.
  • CSI-RS channel state information reference signal
  • SS synchronization signal
  • SS/PBCH Broadcast Channel
  • DMRS demodulation reference signal
  • SRS Sounding Reference Signal
  • CSI includes a channel quality indicator (CQI), a precoding matrix indicator (PMI), and a CSI-RS resource indicator (CRI).
  • CQI channel quality indicator
  • PMI precoding matrix indicator
  • CRI CSI-RS resource indicator
  • SSBRI SS/PBCH Block Resource Indicator
  • LI Layer Indicator
  • RI Rank Indicator
  • L1-RSRP Reference in Layer 1 Signal received power (Layer 1 Reference Signal Received Power), L1-RSRQ (Reference Signal Received Quality), L1-SINR (Signal to Interference plus Noise Ratio), L1-SNR (Signal to Noise Ratio) ) etc.
  • L1-RSRP Reference in Layer 1 Signal received power
  • L1-RSRQ Reference Signal Received Quality
  • L1-SINR Signal Received Quality
  • L1-SNR Synignal to Noise Ratio
  • 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 layers of the present disclosure include an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolution layer, an activation layer, a dense layer, a normalization layer, a pooling layer, an attention layer, a dropout layer, It may correspond to at least one of the fully connected layers.
  • AI model 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), - 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 e.g. immutable value, floating point number
  • Quantization interval (quantization step size) of input/output data for example
  • 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 base station/cell identifier ID
  • BS-UE distance e.g., X/Y/Z axis coordinates, etc.
  • IP Internet Protocol
  • the location information of the UE is not limited to information based on the location of the BS, but may be information based on a specific point.
  • the location information may include information regarding its own implementation (for example, location/position/orientation of antennas, location/orientation of antenna panels, number of antennas, number of antenna panels, etc.).
  • the location information may include mobility information.
  • the mobility information may include information indicating at least one of the mobility type, the moving speed of the UE, the acceleration of the UE, the moving direction of the UE, and the like.
  • the mobility types are fixed location UE, movable/moving UE, no mobility UE, low mobility UE, and medium mobility UE.
  • 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, ⁇ 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. 2 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. 2 may be an AI model in which processing proceeds in order 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, KL Divergence Such)), 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)), Cross Entropy Loss, NLLLos
  • ⁇ Parameters for example, layers, weights
  • How to train/update the AI model e.g. (recommended) number of epochs, batch size, number of data used for training.
  • the inference information for the AI model may include information regarding branch pruning of a decision tree, parameter quantization, etc.
  • the performance information regarding the AI model may include information regarding the expected value of a loss function defined for the AI model.
  • 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 called an AI model index) for identifying the 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 first embodiment relates to controlling the use of a reference AI model.
  • the UE may receive reference AI model information from the NW.
  • the UE may deploy (may make available or deploy) the reference AI model based on the received reference AI model information.
  • FIG. 3 is a diagram illustrating an example of specifying an AI model according to the first embodiment.
  • AI models #1 to #3 are defined in the standard.
  • the UE that has received AI model information indicating AI model #3 from the NW may deploy AI model #3.
  • the UE may use the reference AI model based on the received reference AI model information (for example, specified by the received reference AI model information) as is for inference.
  • the UE may train a reference AI model based on the received reference AI model information.
  • training/updating of the AI model in the UE may be performed based on a data set (for example, data obtained from measurement results) possessed by the UE.
  • FIG. 4 is a diagram illustrating an example of updating the AI model according to the first embodiment.
  • AI models #1-#2 are defined in the standard.
  • the UE that has received AI model information indicating AI model #2 from the NW may deploy AI model #2. Further, the UE may update the AI model #2.
  • FIG. 5 is a diagram showing an example of fine tuning.
  • FIG. 6 is a diagram illustrating an example of transfer learning.
  • the entity that performs training uses the parameters of the trained AI model in the source domain as the initial parameters (pre-training AI model) of the AI model in the target domain. , derive a trained AI model in the target domain. That is, all the layers of the trained AI model in the source domain may be transferred to all the layers of the AI model in the target domain, and some may be used as is without being updated (even if they are fixed (frozen)). good).
  • transfer learning (which may also be called network-based deep transfer learning)
  • an entity performing training reuses some trained layers in the source domain as layers in the target domain. Make use of it.
  • These layers in the figure, the front (left part) layer
  • the remaining layers in the target domain may have no corresponding layers in the source domain and may be newly trained (or fine-tuned) in the target domain.
  • the UE does not need to update parameters (e.g., layers, weights) that should be frozen for training that are obtained from reference AI model information during training.
  • the UE may update parameters to be updated (fine-tuned) obtained from reference AI model information during training.
  • the UE may perform training using parameters that should be initial parameters for training (should be used as initial parameters) obtained from reference AI model information.
  • FIG. 7 is a diagram showing an example of updating the AI model according to the first embodiment.
  • the UE is instructed by the NW to update reference AI model #2.
  • the instructions include information indicating that the first three layers should be frozen and the last two layers should be fine-tuned.
  • the UE may derive an updated AI model #2 in which the first three layers of the reference AI model #2 are frozen and the last two layers are fine-tuned.
  • the reference AI model can be appropriately used in the UE.
  • the second embodiment relates to controlling the use of an AI model trained by a NW (hereinafter also referred to as an NW-trained AI model).
  • the NW may train a reference AI model.
  • the NW may send AI model information regarding the trained reference AI model (in other words, the NW training AI model) to the UE.
  • the AI model information may be called NW training AI model information.
  • the UE may receive reference AI model information, which is the basis of the NW training AI model information, from the NW. Additionally, the UE may receive NW training AI model information from the NW.
  • the NW training AI model information may include any or a combination of the following information instead of or in addition to the AI model information already described: ⁇ (Index of) the reference AI model that was the basis of the NW training AI model, - Information on the difference between the NW training AI model and the reference AI model.
  • the UE may deploy (may make available or deploy) the NW training AI model based on the received NW training AI model information.
  • FIG. 8 is a diagram showing an example of specifying an AI model according to the second embodiment.
  • reference AI models #1-#2 are defined in the standard. NW trains reference AI model #2 and derives NW training AI model #2.
  • the NW notifies the UE of AI model information indicating the reference AI model #2, and also notifies the information of the NW training AI model #2 (for example, updated difference information).
  • the UE that has received these can determine the NW training AI model #2 by considering the updated difference information regarding the reference AI model #2.
  • the UE may use the NW training AI model based on the received NW training AI model information for inference as is.
  • the UE may train the NW training AI model based on the received NW training AI model information.
  • FIG. 9 is a diagram illustrating an example of updating the AI model according to the second embodiment.
  • the UE similarly to FIG. 8, it is assumed that the UE has determined NW training AI model #2.
  • the UE may further update the NW training AI model #2.
  • the UE may update the same layers (the first three layers shown) as the original reference AI model #2 in the NW training AI model #2 based on the data set it has.
  • the UE does not need to update parameters (e.g., layers, weights) that should be frozen for training that are obtained from the NW training AI model information during training.
  • the UE may update the parameters to be updated obtained from the NW training AI model information during training.
  • the UE may perform training using parameters that should be initial parameters for training (should be used as initial parameters) obtained from the NW training AI model information.
  • FIG. 10 is a diagram illustrating an example of updating the AI model according to the second embodiment.
  • the UE is instructed by the NW to update the NW training AI model #2.
  • the instructions include information indicating that the first three layers should be frozen and the last two layers should be fine-tuned.
  • the UE may derive an updated AI model #2 in which the first three layers of the NW training AI model #2 are frozen and the last two layers are fine-tuned.
  • the NW training AI model can be appropriately used in the UE.
  • the third embodiment relates to controlling the use of an AI model trained by the UE (hereinafter also referred to as UE-trained AI model).
  • the UE may derive the UE training AI model by training the reference AI model/NW training AI model.
  • the UE transmits AI model information regarding the UE training AI model (hereinafter also referred to as UE training AI model information) through physical layer signaling (e.g., UCI), upper layer signaling (e.g., RRC signaling, MAC CE), specific signal/ It may be transmitted to the NW using a channel or a combination thereof.
  • the UE training AI model information may include any or a combination of the following information instead of or in addition to the AI model information already mentioned: ⁇ Reference AI model/NW training AI model (index of) that was the basis of the UE training AI model, - Information on the difference of the UE training AI model from the reference AI model/NW training AI model.
  • FIG. 11 is a diagram illustrating an example of specifying an AI model according to the third embodiment.
  • the UE has derived a UE training AI model.
  • the UE transmits UE training AI model information (which may include performance information of the AI model) to the NW.
  • the NW may apply the UE training AI model indicated by the information.
  • FIG. 12 is a diagram illustrating an example of specifying an AI model according to the third embodiment.
  • the UE derives a UE training AI model from some reference AI model. The differences between these models are some of the layers colored in the diagram.
  • the UE transmits UE training AI model information to the NW.
  • the information may include information on the reference AI model, information on the difference, and (performance information on the AI model).
  • the NW can determine a UE training AI model by considering the updated difference information regarding the reference AI model indicated by the information.
  • the UE may report information regarding which weights/layers are updated (e.g. in the UE training AI model information).
  • FIGS. 13A-13B and 14A-14B are diagrams showing examples of UE training AI model information according to the third embodiment.
  • the UE trains an autoencoder (encoder/decoder) and optionally reports UE training AI model information about the decoder to the NW.
  • FIG. 13A corresponds to the case where the UE does not train an AI model, and since both the encoder and decoder have not been updated, UE training AI model information is not transmitted to the NW.
  • FIG. 13B corresponds to the case where only the encoder is updated.
  • the UE does not need to include decoder information in the UE training AI model information, the UE may include and transmit information regarding the estimated performance of the updated model, for example.
  • the case of FIG. 13B can be used when a change in the statistical properties of the input can be adequately dealt with simply by modifying the encoder.
  • FIG. 14A corresponds to the case where the encoder and part of the decoder are updated.
  • the UE may transmit the UE training AI model information by including decoder information (updated part information), for example, including information regarding the estimated performance of the updated model.
  • decoder information updated part information
  • the case of FIG. 14A may be used when relatively small modifications of the decoder are sufficient to respond to changes in the statistical properties of the input.
  • FIG. 14B corresponds to the case where all the encoders and decoders are updated.
  • the UE may include decoder information in the UE training AI model information, for example, information regarding the estimated performance of the updated model, a reference model report, etc., and transmit the information.
  • the case of FIG. 14B may be used when a relatively large modification of the decoder is required in response to changes in the statistical properties of the input.
  • the UE reports in FIG. 13B when the performance is sufficient in FIG. 13B, reports in FIG. 14A when the performance in FIG. 14A is sufficient, and reports in FIG. 14A when the performance is insufficient in FIG. 14A. In this case, it is preferable to apply the report in FIG. 14B. In this way, the UE may dynamically control the communication overhead associated with AI model notification.
  • the transmission of the UE training AI model information in the third embodiment may be used for distributed learning performed using a server and a large number of clients in the NW.
  • the UE training AI model information may be referred to as client training AI model information.
  • Distributed learning may be federated learning, including at least one of the following steps: ⁇ Each client reports resources of computation to the server. ⁇ The server provides the global AI model to the selected client, - Each selected client trains a local AI model based on the global AI model and its own data; ⁇ Each selected client reports the parameters of the local AI model to the server, - The server combines the reported local AI models to update the global AI model and provides the updated global AI model to one or more clients (eg, all clients).
  • FIG. 15 is a diagram illustrating an example of exchange of AI model information regarding federated learning.
  • the server sends the AI model information of the global AI model G i to the client (UE in this example).
  • the client updates the global AI model G i and obtains the local AI model L i based on the data set it owns.
  • the client feeds back (reports) updated information i regarding the local AI model L i to the server.
  • the server updates the global AI model G i to obtain the global AI model G i+1 based on update information i from one or more clients. Federated learning may proceed in this manner.
  • the NW may transmit AI model information regarding the global AI model to the UE.
  • the AI model information may be called global AI model information.
  • Global AI model information may include reference AI model information.
  • Global AI model information may only include information on any or a combination of the following: ⁇ (Index of) the reference AI model that is the basis of the global AI model, ⁇ Global AI model (index), ⁇ Information on the difference between the global AI model and the reference AI model.
  • the AI model information of the global AI model G i+1 may include information on a difference from the AI model (information) of the global AI model G i .
  • the update information i may include a model ID related to the global AI model/local AI model, or may include information on the difference from the AI model (information) of the global AI model G i , or may include information on the difference from the AI model (information) of the global AI model G i, which will be described later in the fourth embodiment. This may correspond to AI model information that is updated.
  • the UE may report intermediate information (eg, gradients, features, etc.) of the local AI model L i in or instead of the update information i.
  • intermediate information eg, gradients, features, etc.
  • the server may be gNB/OAM/LMF/UE
  • the client may be UE/gNB.
  • the UE is a client
  • the gNB is a client
  • server, NW, gNB, OAM, LMF, and UE may be interchanged.
  • client, NW, UE, and gNB may be interchanged.
  • gNB/UE may be replaced with a device in another network/core network.
  • a client may report information about resources for AI model training in the current iteration (hereinafter also referred to as resource information) to a server (eg, gNB).
  • the resource information may include information regarding at least one of the following: ⁇ Capabilities related to training AI models (e.g. computational resources); ⁇ Information on available datasets (e.g. structure/type/format/amount of datasets); - Information about the client (e.g. UE status).
  • the above computational resources include, for example, at least one of the available number of floating-point operations per second (FLOPS), Central Processing Unit (CPU)/Graphics Processing Unit (GPU) performance, available memory amount, etc. You may also indicate one.
  • FLOPS floating-point operations per second
  • CPU Central Processing Unit
  • GPU Graphics Processing Unit
  • the information regarding the client may include information regarding at least one of the following: - Information regarding the type of client (device) (e.g., device model name/model number, communication chip name/model number, etc.); - Estimated result/quality (e.g. Block Error Rate (BLER)/CQI/RSRP/RSRQ/SINR), the client's will to process the training (e.g. whether one or more clients are willing to process the training of the AI model); ⁇ Remaining amount of data that the client can exchange with the server (for example, the amount of data that the client can exchange), -Battery power remaining.
  • device e.g., device model name/model number, communication chip name/model number, etc.
  • Estimated result/quality e.g. Block Error Rate (BLER)/CQI/RSRP/RSRQ/SINR
  • BLER Block Error Rate
  • CQI/RSRP/RSRQ/SINR Block Error Rate
  • the UE training AI model can be appropriately utilized.
  • the fourth embodiment relates to information on the difference between one AI model and another AI model (information on the difference between another AI model with respect to one AI model).
  • the difference information may be called updated AI model information.
  • the updated AI model information may be used, for example, as at least one of the following information already mentioned: ⁇ Information on the difference between the pre-update AI model and the post-update AI model, ⁇ Difference information of the NW training AI model from the reference AI model, - Information on the difference of the UE training AI model from the reference AI model/NW training AI model.
  • the updated AI model information may include weight information expressed as an absolute value, or may include weight information expressed as a difference value between the pre-update AI model and the post-update AI model.
  • FIGS. 16A to 16C are diagrams showing examples of updated AI model information. This example shows updated AI model information when the NW training AI model is trained from the reference model shown in FIG. 16A.
  • FIG. 16B shows weight information expressed as an absolute value and included in the updated AI model information.
  • the last layer has not been updated, so it does not need to be reported.
  • the UE may not expect to report/receive AI model information that has not been updated.
  • FIG. 16C shows weight information expressed as a difference value, included in the updated AI model information. Since the difference value is basically expected to be smaller than the absolute value, a reduction in the amount of information can be expected. Further, if the amount of information in FIG. 16C is the same as that in FIG. 16B, the granularity of each weight information can be reduced, and a more detailed model can be expressed.
  • the updated AI model information does not need to include information on the structure of the AI model. This is because the UE and base station can determine the AI model based on the same AI model structure by exchanging only weight information.
  • the signaling overhead for AI model deployment can be appropriately reduced.
  • a fifth embodiment relates to knowledge distillation.
  • a single large model or multiple ensembled models with good predictive accuracy is prepared as a teacher model, and the knowledge of the teacher model is transferred to a student model that is lightweight and easy to deploy. ) is used for learning.
  • the student model obtained through distillation is expected to be as accurate as the teacher model and more lightweight.
  • FIGS. 17A and 17B are diagrams showing an example of learning using knowledge distillation.
  • an AI model is trained so that the output (output from layer m) obtained by inputting a certain data set (learning data) is close to the correct answer.
  • the variables/values before one or more functions may be called logits, and the one or more The variables/values after the function is applied may be called Probs.
  • the sum of the probuses for all possible correct answer classes is 1.
  • the teacher's output (probus) is used as a soft target, and the loss between the student's probus and the soft target (also called soft target loss, distillation loss, etc.) is reduced. Learning takes place in this way.
  • the one or more functions may be, for example, a softmax function with temperature.
  • the soft target may correspond to information regarding the probability that the input belongs to each class.
  • the soft target loss may be calculated by taking the cross entropy based on the teacher model p i and the student model p i .
  • the soft target loss is calculated as - ⁇ i p i log (q i ). It's okay.
  • the UE may receive AI model information regarding the student model (hereinafter also referred to as student model information).
  • the UE may obtain an output (train the student model) using a student model specified based on the received student model information, and may report information regarding the output to the NW. This step may be called re-training the student model.
  • Student model information may include any or a combination of the following information, instead of or in addition to the AI model information already described: ⁇ Student model (index), ⁇ Teacher model (index), ⁇ Information about the dataset used to calculate the teacher model/student model, ⁇ Soft target value of teacher model/student model, ⁇ The value of the output from the layer with the teacher model/student model, ⁇ Value of loss function of teacher model/student model, ⁇ Soft target values for teacher model/student model, ⁇ The value of the output from a layer for the teacher model/student model, - Value of loss function for teacher model/student model.
  • the information regarding the data set may correspond to at least one of, for example, a batch of data, an index indicating the data set (or data), the data set (or data) itself, etc.
  • the information on the value of the loss function may include information on one or both of a soft target loss and a hard target loss.
  • the UE transmits information about the student model to be retrained (hereinafter also referred to as retraining student model information) through physical layer signaling (e.g. UCI), upper layer signaling (e.g. RRC signaling, MAC CE), specific signals/channels. , or a combination thereof, may be used to transmit to the NW.
  • Retraining student model information may include any or a combination of the following information, instead of or in addition to the AI model information already described: ⁇ Student model (index) that is the basis of the retraining student model, information about the knowledge of the retrained student model (e.g. soft targets, outputs from certain layers), ⁇ Information about the dataset used for retraining, ⁇ Intermediate information of the retrained student model (e.g. gradients, features, etc.), - Information about hard target losses for retraining student models.
  • ⁇ Student model (index) that is the basis of the retraining student model
  • the information regarding the dataset may include information regarding the content of data included in the dataset.
  • the NW may further update the student model based on the soft target specified by the information regarding the student model to be retrained and the soft target of the teacher data. Note that this update of the NW may also be referred to as retraining.
  • the UE may use the teacher model information to retrain the student model.
  • the NW can once again distill knowledge by reporting retraining student model information to the NW. can be used to generate more accurate/lighter student models.
  • Any notification from the NW to the UE in the above embodiments may be based on physical layer signaling (e.g. DCI), higher layer signaling (e.g. RRC signaling, MAC CE), specific signals/channels (e.g. PDCCH, PDSCH, ref. signals), or a combination thereof.
  • physical layer signaling e.g. DCI
  • higher layer signaling e.g. RRC signaling, MAC CE
  • specific signals/channels e.g. PDCCH, PDSCH, ref. signals
  • the MAC CE may be identified by including a new logical channel ID (LCID) in the MAC subheader.
  • LCID logical channel ID
  • the above notification When the above notification is performed by a DCI, the above notification includes a specific field of the DCI, a radio network temporary identifier (Radio Network Temporary Identifier (RNTI)), the format of the DCI, etc.
  • RNTI Radio Network Temporary Identifier
  • any notification from the NW to the UE in the above embodiments may be performed periodically, semi-persistently, or aperiodically.
  • the encoder/decoder may be interchanged with the AI model deployed at the UE/base station. That is, the present disclosure is not limited to the case of using an autoencoder, but may be applied to the case of inference using any model. Furthermore, the object that the UE/base station compresses using the encoder in the present disclosure is not limited to CSI (or channel/precoding matrix), but may be any information.
  • At least one of the embodiments described above may be applied only to UEs that have reported or support a particular UE capability.
  • the particular UE capability may indicate at least one of the following: - supporting specific processing/operation/control/information for at least one of the above embodiments; Supporting acquisition/reporting of channel characteristic information (e.g. LOS, NLOS, location information); ⁇ Maximum FLOPs of AI model that UE can deploy, ⁇ Maximum number of parameters of AI model that UE can deploy, ⁇ Reference model supported by UE, ⁇ Layers/algorithms/functions supported by the UE, ⁇ Calculating ability, ⁇ Data collection ability.
  • channel characteristic information e.g. LOS, NLOS, location information
  • the above-mentioned specific UE capability may be a capability that is applied across all frequencies (commonly regardless of frequency), or may be a capability for each frequency (for example, cell, band, BWP). , capability for each frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or for each subcarrier spacing (SCS). It may be the ability of
  • 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 embodiment described above by upper layer signaling.
  • the specific information may be information indicating that the use of the AI model is enabled, arbitrary RRC parameters for a specific release (for example, Rel. 18), or the like.
  • the UE does not support at least one of the specific UE capabilities or is not configured with the specific information, for example, Rel. 15/16 operations may be applied.
  • the UE may be used for (for compression of) transmission of information between the UE and the base station other than CSI feedback.
  • the UE generates information related to location (or positioning)/information related to location estimation in a location management function (LMF) according to at least one of the above-described embodiments (e.g., using an encoder). You may report it to the network.
  • the information may be channel impulse response (CIR) information for each subband/antenna port. By reporting this, the base station can estimate the location of the UE without reporting the angle/time difference of received signals.
  • CIR channel impulse response
  • a receiving unit that receives NW training model information regarding a model (NW training model) trained by a network (Network (NW)) and reference model information regarding a reference model that is a base of the NW training model;
  • a terminal comprising: a control unit that determines the NW training model based on the NW training model information and the reference model information.
  • NW training model information indicates a difference between the NW training model and the reference model, and the reference model information indicates an index regarding the reference model.
  • the terminal according to Supplementary Note 1 or 2 wherein the control unit further trains the NW training model.
  • the NW training model information includes information on parameters to be frozen for training, The terminal according to any one of Supplementary Notes 1 to 3, wherein the control unit further trains the NW training model, and does not update the parameters.
  • a receiving unit that receives NW training model information regarding a model (NW training model) trained by a network (Network (NW)) or reference model information regarding a reference model that is the base of the NW training model; a control unit that further trains a model determined based on the NW training model information or the reference model information;
  • a terminal comprising: a transmitter configured to transmit information regarding the trained model.
  • the terminal according to supplementary note 1, wherein the information regarding the trained model includes information regarding which weights or layers are updated.
  • a receiver for receiving student model information regarding the student model in knowledge distillation A terminal comprising: a transmitter that transmits information regarding output based on the student model information.
  • a terminal comprising: a transmitter that transmits information regarding output based on the student model information.
  • the terminal according to appendix 1 or 2 The terminal according to appendix 1 or 2, wherein the student model information includes a value of a loss function.
  • the student model information includes information on one or both of soft target loss and hard target loss.
  • 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. 18 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment.
  • the wireless communication system 1 may be a system that realizes communication using Long Term Evolution (LTE), 5th generation mobile communication system New Radio (5G NR), etc. specified by the Third Generation Partnership Project (3GPP). .
  • LTE Long Term Evolution
  • 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
  • Each CC may be included in at least one of a first frequency band (Frequency Range 1 (FR1)) and a second frequency band (Frequency Range 2 (FR2)).
  • Macro cell C1 may be included in FR1
  • small cell C2 may be included in FR2.
  • FR1 may be a frequency band below 6 GHz (sub-6 GHz)
  • FR2 may be a frequency band above 24 GHz (above-24 GHz). Note that the frequency bands and definitions of FR1 and FR2 are not limited to these, and FR1 may correspond to a higher frequency band than FR2, for example.
  • the user terminal 20 may communicate using at least one of time division duplex (TDD) and frequency division duplex (FDD) in each CC.
  • TDD time division duplex
  • FDD frequency division duplex
  • the plurality of base stations 10 may be connected by wire (for example, optical fiber, X2 interface, etc. compliant with Common Public Radio Interface (CPRI)) or wirelessly (for example, NR communication).
  • wire for example, optical fiber, X2 interface, etc. compliant with Common Public Radio Interface (CPRI)
  • NR communication for example, when NR communication is used as a backhaul between base stations 11 and 12, base station 11, which is an upper station, is an Integrated Access Backhaul (IAB) donor, and base station 12, which is a relay station, is an IAB donor. May also be called a node.
  • IAB Integrated Access Backhaul
  • the base station 10 may be connected to the core network 30 via another base station 10 or directly.
  • the core network 30 may include, for example, at least one of Evolved Packet Core (EPC), 5G Core Network (5GCN), Next Generation Core (NGC), and the like.
  • EPC Evolved Packet Core
  • 5GCN 5G Core Network
  • NGC Next Generation Core
  • 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. 19 is a diagram illustrating an example of the configuration of a base station according to an embodiment.
  • the base station 10 includes a control section 110, a transmitting/receiving section 120, a transmitting/receiving antenna 130, and a transmission line interface 140. Note that one or more of each of the control unit 110, the transmitting/receiving unit 120, the transmitting/receiving antenna 130, and the transmission path interface 140 may be provided.
  • this example mainly shows functional blocks that are characteristic of the present embodiment, and it may be assumed that the base station 10 also has other functional blocks necessary for wireless communication. A part of the processing of each unit described below may be omitted.
  • the control unit 110 controls the entire base station 10.
  • the control unit 110 can be configured from a controller, a control circuit, etc., which will be explained based on common recognition in the technical field related to the present disclosure.
  • the control unit 110 may control signal generation, scheduling (e.g., resource allocation, mapping), and the like.
  • the control unit 110 may control transmission and reception, measurement, etc. using the transmitting/receiving unit 120, the transmitting/receiving antenna 130, and the transmission path interface 140.
  • the control unit 110 may generate data, control information, a sequence, etc. to be transmitted as a signal, and may transfer the generated data to the transmitting/receiving unit 120.
  • the control unit 110 may perform communication channel call processing (setting, release, etc.), status management of the base station 10, radio resource management, and the like.
  • the transmitting/receiving section 120 may include a baseband section 121, a radio frequency (RF) section 122, and a measuring section 123.
  • the baseband section 121 may include a transmission processing section 1211 and a reception processing section 1212.
  • the transmitter/receiver unit 120 includes a transmitter/receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transmitter/receiver circuit, etc., which are explained based on common understanding in the technical field related to the present disclosure. be able to.
  • the transmitting/receiving section 120 may be configured as an integrated transmitting/receiving section, or may be configured from a transmitting section and a receiving section.
  • the transmitting section may include a transmitting processing section 1211 and an RF section 122.
  • the reception section may include a reception processing section 1212, an RF section 122, and a measurement section 123.
  • the transmitting/receiving antenna 130 can be configured from an antenna described based on common recognition in the technical field related to the present disclosure, such as an array antenna.
  • the transmitter/receiver 120 may transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc.
  • the transmitter/receiver 120 may receive the above-mentioned uplink channel, uplink reference signal, and the like.
  • the transmitting/receiving unit 120 may form at least one of a transmitting beam and a receiving beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.
  • digital beamforming e.g., precoding
  • analog beamforming e.g., phase rotation
  • the transmitting/receiving unit 120 (transmission processing unit 1211) performs Packet Data Convergence Protocol (PDCP) layer processing, Radio Link Control (RLC) layer processing (for example, RLC retransmission control), Medium Access Control (MAC) layer processing (for example, HARQ retransmission control), etc. may be performed to generate a bit string to be transmitted.
  • PDCP Packet Data Convergence Protocol
  • RLC Radio Link Control
  • MAC Medium Access Control
  • HARQ retransmission control for example, HARQ retransmission control
  • the transmitting/receiving unit 120 performs channel encoding (which may include error correction encoding), modulation, mapping, filter processing, and discrete Fourier transform (DFT) on the bit string to be transmitted.
  • a baseband signal may be output by performing transmission processing such as processing (if necessary), Inverse Fast Fourier Transform (IFFT) processing, precoding, and digital-to-analog conversion.
  • IFFT Inverse Fast Fourier Transform
  • the transmitting/receiving unit 120 may perform modulation, filter processing, amplification, etc. on the baseband signal in a radio frequency band, and may transmit the signal in the radio frequency band via the transmitting/receiving antenna 130. .
  • the transmitting/receiving section 120 may perform amplification, filter processing, demodulation into a baseband signal, etc. on the radio frequency band signal received by the transmitting/receiving antenna 130.
  • the transmitting/receiving unit 120 (reception processing unit 1212) performs analog-to-digital conversion, fast Fourier transform (FFT) processing, and inverse discrete Fourier transform (IDFT) on the acquired baseband signal. )) processing (if necessary), applying reception processing such as filter processing, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing and PDCP layer processing, User data etc. may also be acquired.
  • FFT fast Fourier transform
  • IDFT inverse discrete Fourier transform
  • the transmitting/receiving unit 120 may perform measurements regarding the received signal.
  • the measurement unit 123 may perform Radio Resource Management (RRM) measurement, Channel State Information (CSI) measurement, etc. based on the received signal.
  • the measurement unit 123 measures received power (for example, Reference Signal Received Power (RSRP)), reception quality (for example, Reference Signal Received Quality (RSRQ), Signal to Interference plus Noise 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, other base stations 10, etc., and transmits and receives user data (user plane data) for the user terminal 20, control plane It is also possible to acquire and transmit data.
  • 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.
  • control unit 110 may train a reference model.
  • the transmitting/receiving unit 120 may transmit information regarding the trained model and reference model information regarding the reference model to the user terminal 20.
  • the transmitting/receiving unit 120 may transmit reference model information regarding a reference model or information regarding a model trained based on the reference model to the user terminal 20.
  • the transmitting/receiving unit 120 may receive information regarding the reference model or a model further trained based on the model.
  • the transmitting/receiving unit 120 may transmit student model information regarding the student model in knowledge distillation to the user terminal 20.
  • the transmitter/receiver 120 may receive information regarding output based on the student model information.
  • FIG. 20 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment.
  • the user terminal 20 includes a control section 210, a transmitting/receiving section 220, and a transmitting/receiving antenna 230. Note that one or more of each of the control unit 210, the transmitting/receiving unit 220, and the transmitting/receiving antenna 230 may be provided.
  • this example mainly shows functional blocks that are characteristic of the present embodiment, and it may be assumed that the user terminal 20 also has other functional blocks necessary for wireless communication. A part of the processing of each unit described below may be omitted.
  • the control unit 210 controls the entire user terminal 20.
  • the control unit 210 can be configured from a controller, a control circuit, etc., which will be explained based on common recognition in the technical field related to the present disclosure.
  • the control unit 210 may control signal generation, mapping, etc.
  • the control unit 210 may control transmission and reception using the transmitting/receiving unit 220 and the transmitting/receiving antenna 230, measurement, and the like.
  • the control unit 210 may generate data, control information, sequences, etc. to be transmitted as a signal, and may transfer the generated data to the transmitting/receiving unit 220.
  • the transmitting/receiving section 220 may include a baseband section 221, an RF section 222, and a measuring section 223.
  • the baseband section 221 may include a transmission processing section 2211 and a reception processing section 2212.
  • the transmitting/receiving unit 220 can be configured from a transmitter/receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measuring circuit, a transmitting/receiving circuit, etc., which are explained based on common recognition in the technical field related to the present disclosure.
  • the transmitting/receiving section 220 may be configured as an integrated transmitting/receiving section, or may be configured from a transmitting section and a receiving section.
  • the transmitting section may include a transmitting processing section 2211 and an RF section 222.
  • the reception section may include a reception processing section 2212, an RF section 222, and a measurement section 223.
  • the transmitting/receiving antenna 230 can be configured from an antenna, such as an array antenna, as described based on common recognition in the technical field related to the present disclosure.
  • the transmitter/receiver 220 may receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc.
  • the transmitter/receiver 220 may transmit the above-mentioned uplink channel, uplink reference signal, and the like.
  • the transmitting/receiving unit 220 may form at least one of a transmitting beam and a receiving beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.
  • digital beamforming e.g., precoding
  • analog beamforming e.g., phase rotation
  • the transmission/reception unit 220 (transmission processing unit 2211) performs PDCP layer processing, RLC layer processing (e.g. RLC retransmission control), MAC layer processing (e.g. , HARQ retransmission control), etc., to generate a bit string to be transmitted.
  • RLC layer processing e.g. RLC retransmission control
  • MAC layer processing e.g. , HARQ retransmission control
  • the transmitting/receiving unit 220 (transmission processing unit 2211) performs channel encoding (which may include error correction encoding), modulation, mapping, filter processing, DFT processing (as necessary), and IFFT processing on the bit string to be transmitted. , precoding, digital-to-analog conversion, etc., and output a baseband signal.
  • DFT processing may be based on the settings of transform precoding.
  • the transmitting/receiving unit 220 transmits the above processing in order to transmit the channel using the DFT-s-OFDM waveform.
  • DFT processing may be performed as the transmission processing, or if not, DFT processing may not be performed as the transmission processing.
  • the transmitting/receiving unit 220 may perform modulation, filter processing, amplification, etc. on the baseband signal in a radio frequency band, and may transmit the signal in the radio frequency band via the transmitting/receiving antenna 230. .
  • the transmitting/receiving section 220 may perform amplification, filter processing, demodulation into a baseband signal, etc. on the radio frequency band signal received by the transmitting/receiving antenna 230.
  • the transmission/reception unit 220 (reception processing unit 2212) performs analog-to-digital conversion, FFT processing, IDFT processing (if necessary), filter processing, demapping, demodulation, and decoding (error correction) on the acquired baseband signal. (which may include decoding), MAC layer processing, RLC layer processing, and PDCP layer processing may be applied to obtain user data and the like.
  • the transmitting/receiving unit 220 may perform measurements regarding the received signal.
  • the measurement unit 223 may perform RRM measurement, CSI measurement, etc. based on the received signal.
  • the measurement unit 223 may measure received power (for example, RSRP), reception quality (for example, RSRQ, SINR, SNR), signal strength (for example, RSSI), propagation path information (for example, CSI), and the like.
  • the measurement results may be output to the control unit 210.
  • the transmitting unit and receiving unit of the user terminal 20 in the present disclosure may be configured by at least one of the transmitting/receiving unit 220 and the transmitting/receiving antenna 230.
  • the transmitting/receiving unit 220 receives NW training model information regarding a model (NW training model) trained by a network (Network (NW)) and reference model information regarding a reference model that is the base of the NW training model. It's okay.
  • the control unit 210 may determine the NW training model based on the NW training model information and the reference model information.
  • the NW training model information may indicate a difference between the NW training model and the reference model, and the reference model information may indicate an index regarding the reference model.
  • the control unit 210 may further train the NW training model.
  • the NW training model information includes information on parameters to be frozen for training, and the control unit 210 further trains the NW training model, in which case the parameters do not need to be updated.
  • the transmitting/receiving unit 220 may also receive NW training model information regarding a model (NW training model) trained by a network (Network (NW)) or reference model information regarding a reference model that is the base of the NW training model. .
  • the control unit 210 may further train the model determined based on the NW training model information or the reference model information.
  • the transmitting/receiving unit 220 may transmit information regarding the trained model.
  • the information regarding the trained model may include information regarding which weights or layers are updated.
  • the information regarding the trained model may include update information of the global model in federated learning.
  • the transmitter 220 may transmit information about resources for model training in the current iteration of federated learning.
  • the transmitting/receiving unit 220 may receive student model information regarding student models in knowledge distillation.
  • the transmitter/receiver 220 may transmit information regarding output based on the student model information.
  • the information regarding the output may include information regarding the soft target of the student model.
  • the student model information may include a value of a loss function.
  • the student model information may include information on one or both of soft target loss and hard target loss.
  • 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. 21 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment.
  • the base station 10 and user terminal 20 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc. .
  • the hardware configuration of the base station 10 and the user terminal 20 may be configured to include one or more of each device shown in the figure, or may be configured not to include some of the devices.
  • processor 1001 may be implemented using one or more chips.
  • Each function in the base station 10 and the user terminal 20 is performed by, for example, loading predetermined software (program) onto hardware such as a processor 1001 and a memory 1002, so that the processor 1001 performs calculations and communicates via the communication device 1004. This is achieved by controlling at least one of reading and writing data in the memory 1002 and storage 1003.
  • predetermined software program
  • the processor 1001 operates an operating system to control the entire computer.
  • the processor 1001 may be configured by a central processing unit (CPU) that includes interfaces with peripheral devices, a control device, an arithmetic unit, registers, and the like.
  • CPU central processing unit
  • the above-mentioned control unit 110 (210), transmitting/receiving unit 120 (220), etc. may be realized by the processor 1001.
  • the processor 1001 reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 to the memory 1002, and executes various processes in accordance with these.
  • programs program codes
  • software modules software modules
  • data etc.
  • the control unit 110 may be realized by a control program stored in the memory 1002 and operated in the processor 1001, and other functional blocks may also be realized in the same way.
  • the memory 1002 is a computer-readable recording medium, and includes at least one of Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically EPROM (EEPROM), Random Access Memory (RAM), and other suitable storage media. It may be composed of one. Memory 1002 may be called a register, cache, main memory, or the like.
  • the memory 1002 can store executable programs (program codes), software modules, and the like to implement a wireless communication method according to an embodiment of the present disclosure.
  • the storage 1003 is a computer-readable recording medium, such as a flexible disk, a floppy (registered trademark) disk, a magneto-optical disk (for example, a compact disk (CD-ROM), etc.), a digital versatile disk, removable disk, hard disk drive, smart card, flash memory device (e.g., card, stick, key drive), magnetic stripe, database, server, or other suitable storage medium. It may be configured by Storage 1003 may also be called an auxiliary storage device.
  • a computer-readable recording medium such as a flexible disk, a floppy (registered trademark) disk, a magneto-optical disk (for example, a compact disk (CD-ROM), etc.), a digital versatile disk, removable disk, hard disk drive, smart card, flash memory device (e.g., card, stick, key drive), magnetic stripe, database, server, or other suitable storage medium. It may be configured by Storage 1003 may also be called an auxiliary storage device.
  • the communication device 1004 is hardware (transmission/reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc., for example.
  • the communication device 1004 includes, for example, a high frequency switch, a duplexer, a filter, a frequency synthesizer, etc. in order to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). It may be configured to include.
  • FDD frequency division duplex
  • TDD time division duplex
  • the transmitter/receiver 120 (220) may be physically or logically separated into a transmitter 120a (220a) and a receiver 120b (220b).
  • the input device 1005 is an input device (eg, keyboard, mouse, microphone, switch, button, sensor, etc.) that accepts input from the outside.
  • the output device 1006 is an output device (for example, a display, a speaker, a light emitting diode (LED) lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may have an integrated configuration (for example, a touch panel).
  • each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information.
  • the bus 1007 may be configured using a single bus, or may be configured using different buses for each device.
  • the base station 10 and user terminal 20 also include a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. It may be configured to include hardware, and a part or all of each functional block may be realized using the hardware. For example, processor 1001 may be implemented using at least one of these hardwares.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • PLD programmable logic device
  • FPGA field programmable gate array
  • channel, symbol and signal may be interchanged.
  • the signal may be a message.
  • the reference signal may also be abbreviated as RS, and may be called a pilot, pilot signal, etc. depending on the applicable standard.
  • a component carrier CC may be called a cell, a frequency carrier, a carrier frequency, or the like.
  • a radio frame may be composed of one or more periods (frames) in the time domain.
  • Each of the one or more periods (frames) constituting a radio frame may be called a subframe.
  • a subframe may be composed of one or more slots in the time domain.
  • a subframe may have a fixed time length (eg, 1 ms) that does not depend on numerology.
  • the numerology may be a communication parameter applied to at least one of transmission and reception of a certain signal or channel.
  • Numerology includes, for example, subcarrier spacing (SCS), bandwidth, symbol length, cyclic prefix length, transmission time interval (TTI), number of symbols per TTI, and radio frame structure. , a specific filtering process performed by the transceiver in the frequency domain, a specific windowing process performed by the transceiver in the time domain, etc.
  • a slot may be composed of one or more symbols (Orthogonal Frequency Division Multiplexing (OFDM) symbols, Single Carrier Frequency Division Multiple Access (SC-FDMA) symbols, etc.) in the time domain. Further, a slot may be a time unit based on numerology.
  • OFDM Orthogonal Frequency Division Multiplexing
  • SC-FDMA Single Carrier Frequency Division Multiple Access
  • a slot may include multiple mini-slots. Each minislot may be made up of one or more symbols in the time domain. Furthermore, a mini-slot may also be called a sub-slot. A minislot may be made up of fewer symbols than a slot.
  • PDSCH (or PUSCH) transmitted in time units larger than minislots may be referred to as PDSCH (PUSCH) mapping type A.
  • PDSCH (or PUSCH) transmitted using minislots may be referred to as PDSCH (PUSCH) mapping type B.
  • Radio frames, subframes, slots, minislots, and symbols all represent time units when transmitting signals. Other names may be used for the radio frame, subframe, slot, minislot, and symbol. Note that time units such as frames, subframes, slots, minislots, and symbols in the present disclosure may be read interchangeably.
  • one subframe may be called a TTI
  • a plurality of consecutive subframes may be called a TTI
  • one slot or one minislot may be called a TTI.
  • at least one of the subframe and TTI may be a subframe (1ms) in existing LTE, a period shorter than 1ms (for example, 1-13 symbols), or a period longer than 1ms. It may be.
  • the unit representing the TTI may be called a slot, minislot, etc. instead of a subframe.
  • TTI refers to, for example, the minimum time unit for scheduling in wireless communication.
  • a base station performs scheduling to allocate radio resources (frequency bandwidth, transmission power, etc. that can be used by each user terminal) to each user terminal on a TTI basis.
  • radio resources frequency bandwidth, transmission power, etc. that can be used by each user terminal
  • the TTI may be a transmission time unit of a channel-coded data packet (transport block), a code block, a codeword, etc., or may be a processing unit of scheduling, link adaptation, etc. Note that when a TTI is given, the time interval (for example, the number of symbols) to which transport blocks, code blocks, code words, etc. are actually mapped may be shorter than the TTI.
  • one slot or one minislot is called a TTI
  • one or more TTIs may be the minimum time unit for scheduling.
  • the number of slots (minislot number) that constitutes the minimum time unit of the scheduling may be controlled.
  • a TTI having a time length of 1 ms may be called a normal TTI (TTI in 3GPP Rel. 8-12), normal TTI, long TTI, normal subframe, normal subframe, long subframe, slot, etc.
  • TTI TTI in 3GPP Rel. 8-12
  • normal TTI long TTI
  • normal subframe normal subframe
  • long subframe slot
  • TTI that is shorter than the normal TTI may be referred to as an abbreviated TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, minislot, subslot, slot, etc.
  • long TTI for example, normal TTI, subframe, etc.
  • short TTI for example, short TTI, etc. It may also be read as a TTI having the above TTI length.
  • a resource block is a resource allocation unit in the time domain and frequency domain, and may include one or more continuous subcarriers (subcarriers) in the frequency domain.
  • the number of subcarriers included in an RB may be the same regardless of the numerology, and may be 12, for example.
  • the number of subcarriers included in an RB may be determined based on numerology.
  • an RB may include one or more symbols in the time domain, and may have a length of one slot, one minislot, one subframe, or one TTI.
  • One TTI, one subframe, etc. may each be composed of one or more resource blocks.
  • one or more RBs include a physical resource block (Physical RB (PRB)), a sub-carrier group (SCG), a resource element group (REG), a PRB pair, and an RB. They may also be called pairs.
  • PRB Physical RB
  • SCG sub-carrier group
  • REG resource element group
  • PRB pair an RB. They may also be called pairs.
  • a resource block may be configured by one or more resource elements (REs).
  • REs resource elements
  • 1 RE may be a radio resource region of 1 subcarrier and 1 symbol.
  • Bandwidth Part (also called partial bandwidth, etc.) refers to a subset of consecutive common resource blocks (RB) for a certain numerology in a certain carrier.
  • the common RB may be specified by an RB index based on a common reference point of the carrier.
  • PRBs may be defined in a BWP and numbered within that BWP.
  • BWP may include UL BWP (BWP for UL) and DL BWP (BWP for DL).
  • BWP UL BWP
  • BWP for DL DL BWP
  • One or more BWPs may be configured within one carrier for a UE.
  • At least one of the configured BWPs may be active and the UE may not expect to transmit or receive a given signal/channel outside of the active BWP.
  • “cell”, “carrier”, etc. in the present disclosure may be replaced with "BWP”.
  • the structures of the radio frame, subframe, slot, minislot, symbol, etc. described above are merely examples.
  • the number of subframes included in a radio frame, the number of slots per subframe or radio frame, the number of minislots included in a slot, the number of symbols and RBs included in a slot or minislot, the number of symbols included in an RB The number of subcarriers, the number of symbols within a TTI, the symbol length, the cyclic prefix (CP) length, and other configurations can be changed in various ways.
  • radio resources may be indicated by a predetermined index.
  • data, instructions, commands, information, signals, bits, symbols, chips, etc. which may be referred to throughout the above description, may refer to voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any of these. It may also be represented by a combination of
  • information, signals, etc. may be output from the upper layer to the lower layer and from the lower layer to at least one of the upper layer.
  • Information, signals, etc. may be input and output via multiple network nodes.
  • Input/output information, signals, etc. may be stored in a specific location (for example, memory) or may be managed using a management table. Information, signals, etc. that are input and output can be overwritten, updated, or added. The output information, signals, etc. may be deleted. The input information, signals, etc. may be transmitted to other devices.
  • Notification of information is not limited to the aspects/embodiments described in this disclosure, and may be performed using other methods.
  • the notification of information in this disclosure may be physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), upper layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB), etc.), Medium Access Control (MAC) signaling), other signals, or a combination thereof It may be carried out by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), upper layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB), etc.), Medium Access Control (MAC) signaling), other signals, or a combination thereof It may be carried out by
  • the physical layer signaling may also be called Layer 1/Layer 2 (L1/L2) control information (L1/L2 control signal), L1 control information (L1 control signal), etc.
  • RRC signaling may be called an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.
  • MAC signaling may be notified using, for example, a MAC Control Element (CE).
  • CE MAC Control Element
  • notification of prescribed information is not limited to explicit notification, but may be made implicitly (for example, by not notifying the prescribed information or by providing other information) (by notification).
  • the determination may be made by a value expressed by 1 bit (0 or 1), or by a boolean value expressed by true or false. , may be performed by numerical comparison (for example, comparison with a predetermined value).
  • Software includes instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, whether referred to as software, firmware, middleware, microcode, hardware description language, or by any other name. , should be broadly construed to mean an application, software application, software package, routine, subroutine, object, executable, thread of execution, procedure, function, etc.
  • software, instructions, information, etc. may be sent and received via a transmission medium.
  • a transmission medium such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.
  • wired technology such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.
  • wireless technology such as infrared, microwave, etc.
  • Network may refer to devices (eg, base stations) included in the network.
  • precoding "precoding weight”
  • QCL quadsi-co-location
  • TCI state "Transmission Configuration Indication state
  • space space
  • spatial relation "spatial domain filter”
  • transmission power "phase rotation”
  • antenna port "antenna port group”
  • layer "number of layers”
  • Terms such as “rank”, “resource”, “resource set”, “resource group”, “beam”, “beam width”, “beam angle”, “antenna”, “antenna element”, and “panel” are interchangeable.
  • Base Station BS
  • Wireless base station Wireless base station
  • Fixed station NodeB
  • eNB eNodeB
  • gNB gNodeB
  • Access point "Transmission Point (TP)”, “Reception Point (RP)”, “Transmission/Reception Point (TRP)”, “Panel”
  • cell “sector,” “cell group,” “carrier,” “component carrier,” and the like
  • a base station is sometimes referred to by terms such as macrocell, small cell, femtocell, and picocell.
  • a base station can accommodate one or more (eg, three) cells. If a base station accommodates multiple cells, the overall coverage area of the base station can be partitioned into multiple smaller areas, and each smaller area is connected to a base station subsystem (e.g., an indoor small base station (Remote Radio Communication services can also be provided by the Head (RRH)).
  • a base station subsystem e.g., an indoor small base station (Remote Radio Communication services can also be provided by the Head (RRH)
  • RRH Remote Radio Communication services
  • the term “cell” or “sector” refers to part or all of the coverage area of a base station and/or base station subsystem that provides communication services in this coverage.
  • a base station transmitting information to a terminal may be interchanged with the base station instructing the terminal to control/operate based on the information.
  • MS Mobile Station
  • UE User Equipment
  • a mobile station is a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal. , handset, user agent, mobile client, client, or some other suitable terminology.
  • At least one of a base station and a mobile station may be called a transmitting device, a receiving device, a wireless communication device, etc.
  • a transmitting device may be called a transmitting device, a receiving device, a wireless communication device, etc.
  • the base station and the mobile station may be a device mounted on a moving object, the moving object itself, or the like.
  • the moving body refers to a movable object, and the moving speed is arbitrary, and naturally includes cases where the moving body is stopped.
  • the mobile objects include, for example, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, carts, rickshaws, and ships (ships and other watercraft). , including, but not limited to, airplanes, rockets, artificial satellites, drones, multicopters, quadcopters, balloons, and items mounted thereon.
  • the mobile object may be a mobile object that autonomously travels based on a travel command.
  • the moving object may be a vehicle (for example, a car, an airplane, etc.), an unmanned moving object (for example, a drone, a self-driving car, etc.), or a robot (manned or unmanned). ).
  • a vehicle for example, a car, an airplane, etc.
  • an unmanned moving object for example, a drone, a self-driving car, etc.
  • a robot manned or unmanned.
  • at least one of the base station and the mobile station includes devices that do not necessarily move during communication operations.
  • at least one of the base station and the mobile station may be an Internet of Things (IoT) device such as a sensor.
  • IoT Internet of Things
  • FIG. 22 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.
  • the operations performed by the base station may be performed by its upper node in some cases.
  • various operations performed for communication with a terminal may be performed by the base station, one or more network nodes other than the base station (e.g. It is clear that this can be performed by a Mobility Management Entity (MME), a Serving-Gateway (S-GW), etc. (though not limited thereto), or a combination thereof.
  • MME Mobility Management Entity
  • S-GW Serving-Gateway
  • Each aspect/embodiment described in this disclosure may be used alone, in combination, or may be switched and used in accordance with execution. 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
  • G Global System for Mobile Communications
  • CDMA2000 Ultra Mobile Broadband
  • 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, created, or defined based on these
  • the phrase “based on” does not mean “based solely on” unless explicitly stated otherwise. In other words, the phrase “based on” means both “based only on” and “based at least on.”
  • any reference to elements using the designations "first,” “second,” etc. does not generally limit the amount or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, reference to a first and second element does not imply that only two elements may be employed or that the first element must precede the second element in any way.
  • determining may encompass a wide variety of actions. For example, “judgment” can mean judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry ( For example, searching in a table, database, or other data structure), ascertaining, etc. may be considered to be “determining.”
  • judgment (decision) includes receiving (e.g., receiving information), transmitting (e.g., sending information), input (input), output (output), access ( may be considered to be “determining”, such as accessing data in memory (eg, accessing data in memory).
  • judgment is considered to mean “judging” resolving, selecting, choosing, establishing, comparing, etc. Good too.
  • judgment (decision) may be considered to be “judgment (decision)” of some action.
  • connection refers to any connection or coupling, direct or indirect, between two or more elements.
  • the coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connection” may be replaced with "access.”
  • microwave when two elements are connected, they may be connected using one or more electrical wires, cables, printed electrical connections, etc., as well as in the radio frequency domain, microwave can be considered to be “connected” or “coupled” to each other using electromagnetic energy having wavelengths in the light (both visible and invisible) range.
  • a and B are different may mean “A and B are different from each other.” Note that the term may also mean that "A and B are each different from C”. Terms such as “separate” and “coupled” may also be interpreted similarly to “different.”
  • words meaning "good”, “bad”, “large”, “small”, “high”, “low”, “early”, “slow”, etc. may be read interchangeably. (Not limited to original, comparative, and superlative).
  • words meaning "good”, “bad”, “large”, “small”, “high”, “low”, “early”, “slow”, etc. are replaced with “i-th”. They may be interchanged as expressions (not limited to the original, comparative, and superlative) (for example, “the highest” may be interchanged with “the i-th highest”).

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Le terminal selon un mode de réalisation de la présente divulgation comprend : une unité de réception pour recevoir des informations de modèle entraîné par NW relatives à un modèle entraîné au moyen d'un réseau (NW) (modèle entraîné par NW), ou des informations de modèle de référence relatives à un modèle de référence servant de base pour le modèle entraîné par NW ; une unité de commande pour entraîner en outre un modèle déterminé sur la base des informations de modèle entraîné par NW ou des informations de modèle de référence ; et une unité de transmission pour transmettre des informations relatives au modèle entraîné. Le mode de réalisation de la présente divulgation permet d'obtenir une réduction de surdébit, une estimation de canal et une utilisation de ressources appropriées.
PCT/JP2022/017735 2022-04-13 2022-04-13 Terminal, procédé de communication radio et station de base WO2023199439A1 (fr)

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Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
"3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA) and NR; Study on enhancement for Data Collection for NR and EN-DC (Release 17)", 3GPP TR 37.817, no. V17.0.0, 6 April 2022 (2022-04-06), pages 1 - 23, XP052146515 *
"3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Study on traffic characteristics and performance requirements for AI/ML model transfer in 5GS (Release 18)", 3GPP TR 22.874, no. V18.2.0, 24 December 2021 (2021-12-24), pages 1 - 111, XP052083489 *
LENOVO, INTEL: "Solution for KI#8: Support of federated learning for model training.", 3GPP TSG SA WG2 #150E, S2-2203375, 12 April 2022 (2022-04-12), XP052135566 *

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