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WO2024150435A1 - Terminal, procédé de communication sans fil et station de base - Google Patents

Terminal, procédé de communication sans fil et station de base Download PDF

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Publication number
WO2024150435A1
WO2024150435A1 PCT/JP2023/000878 JP2023000878W WO2024150435A1 WO 2024150435 A1 WO2024150435 A1 WO 2024150435A1 JP 2023000878 W JP2023000878 W JP 2023000878W WO 2024150435 A1 WO2024150435 A1 WO 2024150435A1
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information
model
dataset
data set
data
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English (en)
Japanese (ja)
Inventor
春陽 越後
浩樹 原田
リュー リュー
チーピン ピ
ルフア ヨウ
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NTT Docomo Inc
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NTT Docomo Inc
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Priority to PCT/JP2023/000878 priority patent/WO2024150435A1/fr
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

Definitions

  • This disclosure relates to terminals, wireless communication methods, and base stations in next-generation mobile communication systems.
  • LTE Long Term Evolution
  • UMTS Universal Mobile Telecommunications System
  • Non-Patent Document 1 LTE-Advanced (3GPP Rel. 10-14) was specified for the purpose of achieving higher capacity and greater sophistication over LTE (Third Generation Partnership Project (3GPP (registered trademark)) Release (Rel.) 8, 9).
  • LTE 5th generation mobile communication system
  • 5G+ 5th generation mobile communication system
  • 6G 6th generation mobile communication system
  • NR New Radio
  • E-UTRA Evolved Universal Terrestrial Radio Access
  • E-UTRAN Evolved Universal Terrestrial Radio Access Network
  • one of the objectives of this disclosure is to provide a terminal, a wireless communication method, and a base station that can realize appropriate data collection/model monitoring operations.
  • a terminal has a receiving unit that receives information about a dataset that can be transmitted by a network, a transmitting unit that transmits a dataset request based on the information about the dataset, and a control unit that controls reception of the dataset transferred based on the dataset request.
  • data collection/model monitoring can be performed appropriately.
  • FIG. 1 is a diagram illustrating an example of a framework for managing AI models.
  • 2A and 2B are diagrams showing an example of AI-based beam prediction.
  • 3A and 3B are diagrams showing an example of a sub-use case of AI-based CSI feedback.
  • 4A and 4B are diagrams illustrating an example of a Type 1 training procedure.
  • FIG. 5 illustrates an example of a Type 2 training procedure.
  • FIG. 6 is a diagram showing an example of a procedure for transferring a data set from a UE to a NW.
  • FIG. 7 is a diagram showing an example of a procedure for transferring a data set from a NW to a UE.
  • FIG. 8 is a diagram showing an example of a data set transfer procedure according to embodiment 1-1.
  • FIG. 9 is a diagram showing an example of a data set transfer procedure according to the first and second embodiments.
  • FIG. 10 is a diagram showing an example of a data set transfer procedure according to the first to third embodiments.
  • 11A and 11B are diagrams showing an example of input information to the encoder.
  • 12A and 12B are diagrams illustrating an example of DFT-based extraction of input information.
  • FIG. 13 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment.
  • FIG. 14 is a diagram illustrating an example of the configuration of a base station according to an embodiment.
  • FIG. 15 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment.
  • FIG. 16 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment.
  • FIG. 17 is a diagram illustrating an example of a vehicle according to an embodiment.
  • AI Artificial Intelligence
  • ML machine learning
  • CSI channel state information
  • UE user equipment
  • BS base stations
  • CSI channel state information
  • UE user equipment
  • beam management e.g., improving accuracy, prediction in the time/space domain
  • position measurement e.g., improving position estimation/prediction
  • the AI model may output at least one piece of information such as an estimate, a prediction, a selected action, a classification, etc. based on the input information.
  • the UE/BS may input channel state information, reference signal measurements, etc. to the AI model, and output highly accurate channel state information/measurements/beam selection/position, future channel state information/radio link quality, etc.
  • AI may be interpreted as an object (also called a target, object, data, function, program, etc.) having (implementing) at least one of the following characteristics: - Estimation based on observed or collected information; - making choices based on observed or collected information; - Predictions based on observed or collected information.
  • estimation, prediction, and inference may be interpreted as interchangeable. Also, in this disclosure, estimate, predict, and infer may be interpreted as interchangeable.
  • an object may be, for example, an apparatus such as a UE or a BS, or a device. Also, in the present disclosure, an object may correspond to a program/model/entity that operates in the apparatus.
  • an AI model may be interpreted as an object having (implementing) at least one of the following characteristics: - Producing estimates by feeding information, - Predicting estimates by providing information - Discover features by providing information, - Select an action by providing information.
  • an AI model may refer to a data-driven algorithm that applies AI techniques to generate a set of outputs based on a set of inputs.
  • AI model, model, ML model, predictive analytics, predictive analysis model, tool, autoencoder, encoder, decoder, neural network model, AI algorithm, scheme, etc. may be interchangeable.
  • AI model may be derived using at least one of regression analysis (e.g., linear regression analysis, multiple regression analysis, logistic regression analysis), support vector machine, random forest, neural network, deep learning, etc.
  • autoencoder may be interchangeably referred to as any autoencoder, such as a stacked autoencoder or a convolutional autoencoder.
  • the encoder/decoder of this disclosure may employ models such as Residual Network (ResNet), DenseNet, and RefineNet.
  • encoder encoding, encoding/encoded, modification/alteration/control by an encoder, compressing, compress/compressed, generating, generate/generated, etc. may be read as interchangeable terms.
  • decoder decoding, decode/decoded, modification/alteration/control by a decoder, decompressing, decompress/decompressed, reconstructing, reconstruct/reconstructed, etc.
  • decompressing decompress/decompressed, reconstructing, reconstruct/reconstructed, etc.
  • a layer for an AI model
  • a layer may be interpreted as a layer (input layer, intermediate layer, etc.) used in an AI model.
  • a layer in the present disclosure may correspond to at least one of an input layer, intermediate layer, output layer, batch normalization layer, convolution layer, activation layer, dense layer, normalization layer, pooling layer, attention layer, dropout layer, fully connected layer, etc.
  • methods for training an AI model may include supervised learning, unsupervised learning, reinforcement learning, federated learning, and the like.
  • Supervised learning may refer to the process of training a model from inputs and corresponding labels.
  • Unsupervised learning may refer to the process of training a model without labeled data.
  • Reinforcement learning may refer to the process of training a model from inputs (i.e., states) and feedback signals (i.e., rewards) resulting from the model's outputs (i.e., actions) in the environment with which the model interacts.
  • terms such as generate, calculate, derive, etc. may be interchangeable.
  • terms such as implement, operate, operate, execute, etc. may be interchangeable.
  • terms such as train, learn, update, retrain, etc. may be interchangeable.
  • terms such as infer, after-training, live use, actual use, etc. may be interchangeable.
  • terms such as signal and signal/channel may be interchangeable.
  • Figure 1 shows an example of a framework for managing an AI model.
  • each stage related to the AI model is shown as a block.
  • This example is also expressed as life cycle management of an AI model.
  • the data collection stage corresponds to the stage of collecting data for generating/updating an AI model.
  • the data collection stage may include data organization (e.g., determining which data to transfer for model training/model inference), data transfer (e.g., transferring data to an entity (e.g., UE, gNB) that performs model training/model inference), etc.
  • data collection may refer to a process in which data is collected by a network node, management entity, or UE for the purpose of AI model training/data analysis/inference.
  • process and procedure may be interpreted as interchangeable.
  • collection may also refer to obtaining a data set (e.g., usable as input/output) for training/inference of an AI model based on measurements (channel measurements, beam measurements, radio link quality measurements, position estimation, etc.).
  • offline field data may be data collected from the field (real world) and used for offline training of an AI model.
  • online field data may be data collected from the field (real world) and used for online training of an AI model.
  • model training is performed based on the data (training data) transferred from the collection stage.
  • This stage may include data preparation (e.g., performing data preprocessing, cleaning, formatting, conversion, etc.), model training/validation, model testing (e.g., checking whether the trained model meets performance thresholds), model exchange (e.g., transferring the model for distributed learning), model deployment/update (deploying/updating the model to the entities that will perform model inference), etc.
  • AI model training may refer to a process for training an AI model in a data-driven manner and obtaining a trained AI model for inference.
  • AI model validation may refer to a sub-process of training to evaluate the quality of an AI model using a dataset different from the dataset used to train the model. This sub-process helps select model parameters that generalize beyond the dataset used to train the model.
  • AI model testing may refer to a sub-process of training to evaluate the performance of the final AI model using a dataset different from the dataset used for model training/validation. Note that testing, unlike validation, does not necessarily require subsequent model tuning.
  • model inference is performed based on the data (inference data) transferred from the collection stage.
  • This stage may include data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model inference, model monitoring (e.g., monitoring the performance of model inference), model performance feedback (feeding back model performance to the entity performing the model training), and output (providing model output to the actor).
  • AI model inference may refer to the process of using a trained AI model to produce a set of outputs from a set of inputs.
  • a UE side model may refer to an AI model whose inference is performed entirely in the UE.
  • a network side model may refer to an AI model whose inference is performed entirely in the network (e.g., gNB).
  • a one-sided model may refer to a UE-side model or a network-side model.
  • a two-sided model may refer to a pair of AI models where joint inference is performed.
  • joint inference may include AI inference where the inference is performed jointly across the UE and the network, e.g., a first part of the inference may be performed first by the UE and the remaining part by the gNB (or vice versa).
  • AI model monitoring may refer to the process of monitoring the inference performance of an AI model, and may be interchangeably read as model performance monitoring, performance monitoring, etc.
  • model registration may refer to making a model executable (registering) through assigning a version identifier to the model and compiling it into the specific hardware used in the inference phase.
  • Model deployment may refer to distributing (or activating at) a fully developed and tested run-time image (or an image of the execution environment) of the model to the target (e.g., UE/gNB) where inference will be performed.
  • Actor stages may include action triggers (e.g., deciding whether to trigger an action on another entity), feedback (e.g., feeding back information needed for training data/inference data/performance feedback), etc.
  • action triggers e.g., deciding whether to trigger an action on another entity
  • feedback e.g., feeding back information needed for training data/inference data/performance feedback
  • training of a model for mobility optimization may be performed in, for example, Operation, Administration and Maintenance (Management) (OAM) in a network (NW)/gNodeB (gNB).
  • OAM Operation, Administration and Maintenance
  • NW network
  • gNodeB gNodeB
  • In the former case interoperability, large capacity storage, operator manageability, and model flexibility (feature engineering, etc.) are advantageous.
  • the latency of model updates and the absence of data exchange for model deployment are advantageous.
  • Inference of the above model may be performed in, for example, a gNB.
  • the entity performing the training/inference may be different.
  • the function of the AI model may include beam management, beam prediction, autoencoder (or information compression), CSI feedback, positioning, etc.
  • the OAM/gNB may perform model training and the gNB may perform model inference.
  • a Location Management Function may perform model training and the LMF may perform model inference.
  • the OAM/gNB/UE may perform model training and the gNB/UE may perform model inference (jointly).
  • the OAM/gNB/UE may perform model training and the UE may perform model inference.
  • model activation may mean activating an AI model for a particular function.
  • Model deactivation may mean disabling an AI model for a particular function.
  • Model switching may mean deactivating a currently active AI model for a particular function and activating a different AI model.
  • Model transfer may also refer to distributing an AI model over the air interface. This may include distributing either or both of the parameters of the model structure already known at the receiving end, or a new model with the parameters. This may also include a complete model or a partial model.
  • Model download may refer to model transfer from the network to the UE.
  • Model upload may refer to model transfer from the UE to the network.
  • AI-based beam prediction As a use case of utilizing the AI model, spatial domain downlink (DL) beam prediction or temporal DL beam prediction using a one-sided AI model in the UE or NW is being considered.
  • DL spatial domain downlink
  • BM Beam Management
  • FIG. 2A and 2B are diagrams showing an example of AI-based beam prediction.
  • FIG. 2A shows spatial domain DL beam prediction.
  • the UE may measure a spatially sparse (or thick) beam, input the measurement results, etc., to an AI model, and output a predicted result of the beam quality of a spatially dense (or thin) beam.
  • Figure 2B shows temporal DL beam prediction.
  • the UE may measure the time series of beams, input the measurement results, etc. into an AI model, and output the predicted beam quality of the future beam.
  • spatial domain DL beam prediction may be referred to as BM case 1
  • temporal DL beam prediction may be referred to as BM case 2.
  • temporal DL beam prediction may be referred to as, for example, time domain CSI prediction.
  • the beams associated with the output (prediction result) of the AI model may be referred to as set of beams A.
  • the beams associated with the input of the AI model may be referred to as set of beams B.
  • set A may correspond to beams selected from the predicted beams.
  • the resources for set A may be referred to as resources for beam prediction, resources for beam reporting, resources included in the CSI report, set A, resources of set A, second (or first) set, second (or first) resources, etc.
  • set B may correspond to a beam whose measurement results are used as input (for the AI model/function for prediction).
  • Resources for set B may be referred to as resources for beam measurements, resources for beam prediction input, set B, resources of set B, first (or second) set, resources of first (or second) set, etc.
  • Candidates for input to the AI model for BM Case 1/2 include L1-RSRP (Layer 1 Reference Signal Received Power), assistance information (e.g., beam shape information, UE position/direction information, transmit beam usage information), Channel Impulse Response (CIR) information, and corresponding DL transmit/receive beam IDs.
  • L1-RSRP Layer 1 Reference Signal Received Power
  • assistance information e.g., beam shape information, UE position/direction information, transmit beam usage information
  • CIR Channel Impulse Response
  • Possible outputs of the AI model for BM Case 1 include the IDs of the top K (K is an integer) transmit/receive beams, the predicted L1-RSRP of these beams, the probability that each beam is in the top K, and the angles of these beams.
  • the candidates for the output of the AI model in BM Case 2 include predicted beam failures.
  • the UE measures the data and calculates the data based on the measurements.
  • the UE determines the data set to be used for training, it needs to request which reference signal (RS) it desires (which RS is desired).
  • RS reference signal
  • the other is for the UE to receive data/measurements from other entities and perform calculations based on the data/measurements.
  • new signaling optimized for data/measurement delivery is required.
  • the base station measures data and calculates data based on the measurements.
  • specific assistance information e.g., UE configuration information
  • the other is for the base station to receive data/measurements from other entities and perform calculations based on the data/measurements.
  • new signaling optimized for the delivery of data/measurements is required.
  • DL RS Request For data collection on the UE side, it is considered that DL RS requests are utilized to collect the desired data set for model training.
  • the assistance signaling may be, for example, a specific reference signal (e.g., a Positioning Reference Signal (PRS) or a Sounding Reference Signal (SRS)). At least one of the setting of the specific reference signal and an identifier of the setting may be used as the procedure.
  • a specific reference signal e.g., a Positioning Reference Signal (PRS) or a Sounding Reference Signal (SRS)
  • PRS Positioning Reference Signal
  • SRS Sounding Reference Signal
  • Figure 3A shows an example of AI-based CSI feedback.
  • the UE performs pre-processing, model generation, and post-processing on the CSI measurement results, etc., and transmits the encoded bits (CSI feedback information) to the NW (base station).
  • the NW base station
  • the NW performs pre-processing, model reconstruction, and post-processing on the received bits to obtain the CSI (channel/precoding matrix).
  • temporal CSI prediction in a UE-side model is being considered as one of the representative sub-use cases.
  • a type 1 training procedure may refer to a training procedure that involves training on one entity (joint training) and providing a model to the other entity.
  • FIG. 4A is a diagram showing an example of a Type 1 training procedure.
  • joint training is performed in the base station (gNB (NW)), and then the model is provided from the NW to the UE.
  • gNB base station
  • FIG. 4B is a diagram showing another example of a Type 1 training procedure.
  • joint training is performed in the NW, and then the NW provides a UE-side model to the UE, and the NW provides a NW-side model to the base station (gNB).
  • gNB base station
  • Type 2 training procedure may refer to a training procedure in which joint training is performed on the UE side and the NW side by exchanging gradients, activations, and target outputs.
  • FIG. 5 is a diagram showing an example of a Type 2 training procedure.
  • training is performed in each of an entity for the NW side model and an entity for the UE side model.
  • each entity exchanges gradients, activations, and target outputs to perform joint training.
  • a type 3 training procedure may refer to a training procedure in which a first training is performed on a first entity and then a second training is performed on a second entity (which may be referred to as sequential training).
  • the UE When the UE performs the first training as the first entity, first, the UE trains the encoder and the decoder.
  • the UE then delivers the input/output data set for the decoder to the second entity, the network.
  • the NW trains the decoder based on the received dataset.
  • the NW When the NW performs the first training as the first entity, first, the NW trains the encoder and the decoder.
  • the NW provides the input/output data set for the encoder to the second entity, the UE.
  • the UE trains the encoder based on the received dataset.
  • the AI/ML model is considered to deliver a data set for beam management and positioning.
  • the data set may be used to collect data for training the AI/ML model.
  • the data sets provided will include signaling/configuration/measurements/reports for data collection (e.g., assistance information/information on reference signals).
  • the dataset provided will include information regarding the request/reporting of training data (e.g., ground truth labels, measurements corresponding to model inputs, and information related to these).
  • the specification may provide signaling indicating the availability of data set transfer.
  • registration of the dataset may be performed.
  • the specification may also provide for signaling of requests for data sets.
  • the specification may also provide for signaling for the initiation and termination of data set transfer.
  • the specification may specify signaling that includes the data set.
  • Semi-supervised training involves learning from labeled and unlabeled datasets.
  • Approach 1 may be the determination of a feature set based on an unlabeled dataset.
  • a feature set (e.g., a method for extracting (obtaining) features from a dataset) is determined based on an unlabeled dataset/labeled dataset (step A1-1).
  • step A1-2 a model is trained based on the labeled dataset of the feature set determined in step A1-1 (step A1-2).
  • Approach 2 may be model-based label-free label computation.
  • step A2-1 a model is trained based on a labeled dataset.
  • the model trained in step A2-1 outputs labels estimated from the input of the unlabeled dataset (step A2-2).
  • step A2-2 if the confidence is higher than a certain value, the output in step A2-2 is regarded as a label (step A2-3). That is, a new labeled dataset is obtained based on the unlabeled dataset.
  • step A2-4 a model is trained based on the new labeled dataset obtained in step A2-3 (step A2-4).
  • Model Monitoring In future wireless communication systems (e.g., Rel. 18 and beyond), model monitoring is being considered for UE-side models.
  • Model monitoring of the network side is also being considered.
  • the UE side model monitoring is considered to include at least one of the following procedures: - Calculation of performance metrics (performance evaluation indicators). - Monitoring performance metrics. ⁇ Decision on model operation.
  • One approach is model monitoring based on data distribution.
  • model performance may be estimated based on the distribution of the model's inputs/outputs.
  • the model may perform poorly.
  • model monitoring based on data distributions may not require additional overhead of signals/measurements other than those used for model inference to calculate performance metrics.
  • model monitoring based on data distribution may be useful, especially for certain sub-use cases where additional signaling is required (e.g., CSI compression).
  • Input bases for model monitoring based on data distribution include at least one of the following: monitoring the validity of AI/ML inputs, out-of-distribution detection, drift detection of input data, SNR, and delay spread.
  • Detecting drift in output data is being considered as an output basis for model monitoring based on data distribution.
  • LCM Life Cycle Management
  • Feature-based LCM may allow activation/deactivation/switching/fallback based on individual features.
  • functionality may refer to the use of a model or the physical meaning of an input/output.
  • the other is model-ID-based LCM.
  • Model ID-based LCM may perform activation/deactivation/switching/fallback based on individual model IDs.
  • model monitoring it is being considered to include the properties of data samples in the information used in data collection operations.
  • UE/NW there has been insufficient consideration of the specific information content and the operation of UE/NW in such cases.
  • each embodiment of the present disclosure may be applied when AI is not used (e.g., when predictions are made using a function).
  • A/B and “at least one of A and B” may be interpreted as interchangeable. Also, in this disclosure, “A/B/C” may mean “at least one of A, B, and C.”
  • Radio Resource Control RRC
  • RRC parameters RRC parameters
  • RRC messages higher layer parameters, fields, information elements (IEs), settings, etc.
  • IEs information elements
  • CE Medium Access Control
  • update commands activation/deactivation commands, etc.
  • the higher layer signaling may be, for example, any one of Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information, other messages (e.g., messages from the core network such as positioning protocols (e.g., NR Positioning Protocol A (NRPPa)/LTE Positioning Protocol (LPP)) messages), or a combination of these.
  • RRC Radio Resource Control
  • MAC Medium Access Control
  • LPP LTE Positioning Protocol
  • the MAC signaling may use, for example, a MAC Control Element (MAC CE), a MAC Protocol Data Unit (PDU), etc.
  • the broadcast information may be, for example, a Master Information Block (MIB), a System Information Block (SIB), Remaining Minimum System Information (RMSI), Other System Information (OSI), etc.
  • MIB Master Information Block
  • SIB System Information Block
  • RMSI Remaining Minimum System Information
  • OSI 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
  • ignore, drop, abort, cancel, puncture, rate match, postpone, do not transmit, etc. may be read as interchangeable.
  • configuration information In this disclosure, configuration information, setting (of certain information), and set (of certain information) may be interpreted as interchangeable.
  • "setting" means a collection of information related to "!, and as this is a description for convenience, the name is not limited to this.
  • step UN1 the data set may be identified.
  • step UN1 the UE may transmit information to the NW regarding the data set that can be provided to the NW.
  • the information may be, for example, information about a dataset related to the meta information, or information about a dataset related to the model information (generative model).
  • the meta information is described in more detail below.
  • step UN2 a data set request may be received.
  • the UE may receive a request/instruction to transfer the data set from the NW.
  • step UN3 the data set may be transferred/provided.
  • the UE may transfer/provide the data set to the NW.
  • the UE may instruct the network to complete the data set transfer.
  • the UE may also receive from the network the completion of data set reception.
  • the data set transfer from the NW to the UE may be performed according to the following steps NU1 to NU3 (see FIG. 7).
  • step NU1 the data set may be identified.
  • the UE may receive information from the NW regarding data sets that can be provided by the NW.
  • the information may be, for example, information about a dataset related to the meta information, or information about a dataset related to the model information (generative model).
  • the meta information is described in more detail below.
  • step NU2 a data set request may be sent.
  • the UE may send a request/instruction to the NW to transfer the data set.
  • the UE may then receive a data set transfer instruction (e.g., an acknowledgment).
  • step NU3 the data set may be transferred/provided.
  • the UE may receive the data set from the network.
  • the UE may terminate the provision/reception of the data set.
  • the UE may also receive a notification from the NW regarding the end of the reception of the data set.
  • the first embodiment relates to identifying a data set.
  • the first embodiment is broadly divided into embodiments 1-1 to 1-3.
  • the UE/NW may conform to at least one of the following embodiments 1-1 to 1-3.
  • the UE/base station/LMF may report/send dataset information (assistance information for identifying the dataset) regarding the dataset that may be transferred to the base station/LMF.
  • the information may be transmitted by the methods described in Supplementary Note 3 below.
  • LMF may be interpreted as other network entities (e.g., OAM).
  • the dataset information may include dataset-specific settings, which are described in more detail below.
  • the UE/base station/LMF may (implicitly) report/send which datasets the base station/LMF can forward based on the model information associated with the datasets.
  • the UE may report information about a generative model in the CSI compression.
  • the UE may then indicate/suggest that the UE may forward a data set generated by the corresponding generative model.
  • FIG. 8 is a diagram showing an example of a data set transfer procedure according to embodiment 1-1.
  • the UE transmits assistance information for identifying a data set to the base station.
  • the UE receives a data set request from the base station.
  • the UE may receive dataset information (assistance information for identifying a dataset) regarding a dataset that may be transmitted from the NW (base station/LMF).
  • NW base station/LMF
  • the information may be transmitted by the methods described in Supplementary Note 2 below.
  • the dataset information may include dataset-specific settings, which are described in more detail below.
  • the UE may (implicitly) determine which data sets can be transferred from the NW based on model information in the NW.
  • the UE may receive information about a reconstruction model at the base station in CSI compression. The UE may then determine that it can receive a data set generated by the corresponding reconstruction model.
  • FIG. 9 is a diagram showing an example of a data set transfer procedure according to embodiment 1-2.
  • the UE receives assistance information for identifying a data set from the base station. Then, the UE transmits a data set request to the base station.
  • the UE may be preconfigured with information regarding one or more data set requirements.
  • the UE may receive/configure (may be configured) one or more configurations (pre-configuration(s)) indicating information about the data sets that the UE can trigger/request.
  • configurations pre-configuration(s)
  • the UE may receive/configure (may be configured) the data set request setting pre-configuration(s).
  • FIG. 10 is a diagram showing an example of a data set transfer procedure according to embodiments 1-3.
  • the UE receives a data set request pre-configuration from the base station.
  • the UE then transmits a data set request to the base station.
  • Dataset information setting The following describes the dataset information setting(s).
  • the dataset information settings may include at least one of the information/parameters described below.
  • a dataset information setting may include information/parameters indicating the ID of the dataset information setting.
  • the ID may be provided for at least one of: each dataset, each data sample set within a dataset, and each data sample.
  • the dataset information settings may include parameters related to the dataset information.
  • the parameters associated with the dataset information may include at least one of the information/parameters described below.
  • Parameters related to the dataset information may include, for example, parameters indicating a meta ID/meta information associated with the dataset.
  • Different meta IDs may be assigned to multiple sets of data samples within a dataset.
  • the meta information is described in more detail below.
  • the parameters related to the dataset information may include, for example, parameters indicating a property ID/property information associated with the dataset.
  • Different property IDs may be assigned to multiple sets of data samples within a dataset.
  • the property ID will be described in detail in the fourth embodiment below.
  • Parameters related to the dataset information may include, for example, a parameter indicating the type of dataset.
  • the type may, for example, indicate the functionality/sub-use case associated with the dataset (e.g., CSI prediction).
  • Parameters related to the dataset information may include, for example, parameters indicating model information.
  • the dataset may be generated based on a model.
  • the dataset may be generated by a generative/reconstruction model in training of a particular type (e.g., type 3).
  • the dataset may be used to train a model.
  • model information may be at least one of the pieces of information described in Supplementary Note 1 below.
  • Parameters related to the dataset information may include, for example, parameters indicating feature value(s) of the dataset.
  • features may refer to component values of a data sample. Features are described in more detail below.
  • a data sample may include one or more features.
  • a label may not be considered a feature of a dataset. If a label is provided explicitly and separately from a feature, the feature may be determined to be a value associated with a model input.
  • Parameters related to the dataset information may include, for example, a parameter indicating the label value of the dataset.
  • a label may refer to information based on (e.g., information derived from) the output of a model (e.g., the nominal/target output).
  • Parameters related to the dataset information may include, for example, information related to the time (time domain) associated with the dataset.
  • the parameters associated with the dataset information may be, for example, parameters indicative of the quality of the data samples in the dataset that may be transferred.
  • the parameter indicating the quality of the data sample may indicate, for example, the quality requirements of the feature/label values for the data samples in the dataset (e.g., the percentage of confidence achieved by each data sample in the dataset, which may be expressed as X% (X is an arbitrary number)).
  • Parameters related to the dataset information may include, for example, information regarding the quantization method/resolution associated with values (e.g., feature/label values) in the dataset.
  • the quantization method/resolution may be specified for each feature/label.
  • At least one of the above parameters may be associated with at least one of the entire data set, a portion of the data set, and a data sample within the data set.
  • the meta information received by the UE may include at least one of information regarding network configuration/deployment, information regarding the environment, information regarding the AL/ML model on the network side, and information regarding the model required by the network.
  • Information regarding network settings/layout may include, for example, information regarding antenna settings.
  • the information regarding the antenna configuration may indicate, for example, at least one of the following: horizontal/vertical antenna element/panel count, number of ports, antenna spacing, antenna position, panel position, transceiver unit (TxRU) mapping.
  • Information regarding network configuration/layout may include, for example, information regarding beam configuration.
  • Information regarding beam settings may include, for example, at least one of the beam width, the number of beams, and the beam direction.
  • Information regarding network configuration/layout may include, for example, information regarding TRP.
  • Information regarding beam settings may include, for example, at least one of the altitude of the TRP and the relative position of the multi-TRP.
  • Information about the environment may include, for example, information about the deployment scenario.
  • the information regarding the deployment scenario may indicate, for example, at least one of Urban Macro (UMa), Urban Micro (Umi), and Indoor Hotspot (InH).
  • UMa Urban Macro
  • Umi Urban Micro
  • InH Indoor Hotspot
  • Information about the environment may include, for example, information about indoors or outdoors.
  • Information regarding indoor or outdoor may indicate, for example, indoor/outdoor probability.
  • Information about the environment may be, for example, information about objects around the UE/base station.
  • the information about objects around the UE/base station may, for example, indicate the location of the objects around the UE/base station.
  • Information about the environment may include, for example, the scenario setting format (meta information) described below.
  • a use case using an AI model may be associated with a scenario-setting format that consists of long-term features.
  • scenario setting format may be interchangeably read as meta information, meta information format, scenario and configuration format, scenario configuration format, scenario format, setting format, use case format, environment format, meta format, etc.
  • format may be interchangeably read as type, mode, data, setting, etc.
  • the above features may include a combination of one or more of the following elements: - Scenario/model (Urban Macro (UMa), Urban Micro (Umi), indoor, outdoor, indoor hotspot (InH), etc.). Frequency/frequency range. Numerology (or subcarrier spacing). Distribution/set of generic channel parameters (e.g., inter-site distances (ISD), gNB height, delay spread, angular spread, Doppler spread, etc.) in a single scenario/model. UE distribution. UE speed. ⁇ UE orbit. Number of transmit/receive beams. -UE rotation pattern. - gNB/UE antenna configuration (e.g., transmit and receive antenna vectors). Number of cells/sectors. Bandwidth. UE payload.
  • Urban Macro UMa
  • Urban Micro User Planarcom
  • InH indoor hotspot
  • Frequency/frequency range Numerology (or subcarrier spacing). Distribution/set of generic channel parameters (e.g., inter-site distances (ISD
  • Channel quality (e.g., RSRP, SINR).
  • PCI Physical Cell ID
  • GCI Global Cell ID
  • ARFCN Absolute Radio Frequency Channel Number
  • LOS Probability of Line Of Site (LOS)/Non-Line Of Site (NLOS).
  • the UE may be expected to be configured/registered with a model whose associated scenario configuration format matches the UE's configuration/status.
  • the UE may also be expected to activate models whose associated scenario configuration format matches the UE's configuration/status.
  • the correspondence between the use case and the scenario configuration format may be specified in a standard, and information regarding the correspondence may be notified to the UE.
  • the features included in the scenario configuration format corresponding to the use case may be specified in a standard, and information regarding the features may be notified to the UE.
  • the information regarding the AL/ML model on the NW side may include, for example, information regarding a paired model available on the NW side.
  • Information about paired models available on the network side may indicate, for example, a paired decoder for CSI compression.
  • Information regarding the AL/ML model on the network side may include, for example, information regarding pre-processing/post-processing available on the network side.
  • Information regarding pre-processing/post-processing available on the network side may include, for example, at least one of quantization/dequantization processing, DFT transformation, IDFT transformation, FFT transformation, and IFFT transformation.
  • the information regarding the model requested by the NW may include, for example, the information described in at least one of options A to H below.
  • the UE may report information regarding the capabilities of the model.
  • the model functionality may be, for example, at least one of temporal beam prediction, spatial domain beam prediction, temporal CSI prediction, spatial CSI prediction, direct AI positioning, and AI-assisted positioning.
  • the UE may report information regarding the expected time offset.
  • the time offset may be, for example, the time offset between the predicted CSI timing and the latest CSI-RS occasion that is later than the CSI reference resource.
  • the UE may report the number of predicted beams.
  • the number of predicted beams may be the number of the top K predicted beams, which are the model inference results.
  • the UE may report at least one of the compression rate of the CSI and the bit length of the coded bits.
  • the UE may report information of at least one AI/ML model as described in Supplementary Note 1 below.
  • the UE may report meta-information for which the reported model is applicable.
  • a dataset of meta IDs/meta information may be collected that is trained for the corresponding model.
  • the UE may report computable performance metrics in the monitoring model.
  • the UE may report priorities (priority levels) for multiple AI/ML models when multiple AI/ML models are reported.
  • Option H allows the UE to prioritize specific AI/ML models based on the UE's status (e.g., power consumption/computational resources) and the model's complexity.
  • the network may or may not follow the reported priority.
  • features/labels may refer to features/labels included in a dataset for a particular model/function (e.g., CSI compression/temporal CSI prediction/beam management/positioning).
  • the features/labels may include at least one of those listed below.
  • the features may include at least one of the inputs of the generative model (e.g., nominal inputs), a precoding matrix, and a channel matrix (e.g., a matrix representing the amplitude and phase or only the phase at each antenna).
  • the generative model e.g., nominal inputs
  • a precoding matrix e.g., a matrix representing the amplitude and phase or only the phase at each antenna.
  • a channel matrix e.g., a matrix representing the amplitude and phase or only the phase at each antenna.
  • the features may be, for example, parameters indicating the coefficients (e.g., amplitude/phase) of the channel matrix/precoding matrix for each antenna port/subband/DFT base(s)/TRP.
  • each of the parameters is a parameter for X (where the number of Xs is N)
  • the parameters may be determined to be N features.
  • the features may include the inputs (e.g., nominal inputs) of the reconstruction model.
  • the labels may include at least one of the output of the generative model (e.g., nominal output/target output), the output of the reconstruction model (e.g., nominal output/target output), a precoding matrix, and a channel matrix (e.g., a matrix representing the amplitude and phase or only the phase at each antenna).
  • the generative model e.g., nominal output/target output
  • the output of the reconstruction model e.g., nominal output/target output
  • a precoding matrix e.g., a matrix representing the amplitude and phase or only the phase at each antenna.
  • the feature may be, for example, a parameter indicating the coefficients (e.g., amplitude/phase) of the channel matrix/precoding matrix for each antenna port/subband/DFT base(s)/TRP.
  • the features/labels may include at least one of those described below.
  • the features/labels may include at least one of the following: parameters indicating the coefficients (e.g., amplitude/phase) of the channel matrix/precoding matrix per antenna port/subband/DFT base(s)/TRP, and a parameter indicating the time corresponding to the data sample.
  • the features may include at least one of the following: signal reception quality (e.g., (L1-)RSRP/SINR)/CIR (Channel Impulse response) and a parameter indicating the time corresponding to the data sample.
  • signal reception quality e.g., (L1-)RSRP/SINR
  • CIR Channel Impulse response
  • the RSRP/SINR/CIR may be, for example, the RSRP/SINR/CIR for each RS/beam of a particular set (e.g., set B).
  • the CIR may refer to, for example, parameters indicating coefficients (e.g., amplitude/phase) for each time domain sample/antenna port.
  • the labels may include at least one of the following: signal reception quality (e.g., (L1-)RSRP/SINR), top 1 beam, top K beams (K is an integer equal to or greater than 2), probability of top 1 beam (top 1), top K probability, and top K/1 probability for the top K beams.
  • the signal reception quality (e.g., (L1-)RSRP/SINR) may be the signal reception quality for a particular set (e.g., set A). If set B is a subset of set A, set B may not be transmitted in the same data set.
  • the top beam may be indicated by a beam/RS index that achieves a particular (e.g., maximum) RSRP/SINR.
  • the top K beams may be indicated by the beam/RS index that achieves a particular (e.g., the K highest) RSRP/SINR.
  • the probability of the top 1 beam (top 1), top K probabilities, and top K/1 probabilities for the top K beams may correspond to a particular set (e.g., set A).
  • the features may include at least one of the following: CIR, power delay profile (PDP), and a parameter indicating the time corresponding to the data sample/time domain sample.
  • CIR CIR
  • PDP power delay profile
  • the CIR may refer to, for example, parameters indicating coefficients (e.g., amplitude/phase) for each time domain sample/antenna port/TRP.
  • the PDP may refer to, for example, parameters indicating the power intensity per time domain sample/antenna port/TRP.
  • the features/labels may include at least one of the following: - LOS/NLOS discrimination (per TRP/per PRS) (soft/hard values of LOS/NLOS discrimination). Timing of arrival (ToA) (per TRP). - Receive (Rx) - Transmit (Tx) time difference (per TRP). AoA (per TRP) (DL/UL AoA). AoD (DL/UL AoA) (per TRP). Number of wavelengths between the TRP and the UE. - Rx-Tx phase difference between TRP and UE.
  • RSTD Reference Signal Time Difference
  • TRP Time Difference of Arrival
  • the label may include the UE's position (e.g., UE's position coordinates).
  • the UE transmits information (CSI feedback) from an antenna, including encoded bits that are output by inputting input information to an encoder.
  • the base station obtains reconstructed input information that is output by inputting the received CSI feedback bits to a corresponding decoder.
  • the input information may be information about a precoding matrix.
  • the precoding matrix may include information about precoding coefficients (elements of the precoding matrix) for each subband/antenna port/MIMO layer (for simplicity, referred to as an option 1.1 matrix), or may include information obtained by IDFT from the precoding coefficients (for simplicity, referred to as an option 1.2 matrix).
  • the latter information is expected to contribute to speeding up the calculations of the encoder, since it can make the matrix sparser than the former information by converting the precoding matrix into the angle/delay domain.
  • the base station can apply DFT to it to obtain the original channel coefficients.
  • the UE calculates the precoding matrix, improving compatibility with existing standards that perform control based on the precoding matrix.
  • FIGS. 11A and 11B are diagrams showing an example of input information to an encoder.
  • a precoding matrix for each MIMO layer is shown.
  • FIG. 11A shows a matrix for option 1.1, which corresponds to, for example, a precoding matrix of the number of antenna ports x the number of subbands.
  • Each element may indicate a precoding weight (coefficient).
  • FIG. 11B shows a matrix equivalent to FIG. 11A.
  • the matrix sandwiched between two-dimensional DFT (2D-DFT) matrices indicates a matrix for option 1.2, which corresponds to, for example, a precoding matrix of the number of antenna ports x the number of subbands.
  • 2D-DFT two-dimensional DFT
  • the coefficients of the matrix of option 1.2 shown in the center of FIG. 11B related to the DFT base of the 2D-DFT matrix shown in the left of FIG. 11B (shown by dashed and solid lines) and the DFT base of the 2D-DFT matrix shown in the right of FIG. 11B (shown by dashed and solid lines) may be the coefficients of the corresponding components when the 2D-DFT matrix shown in the left of FIG. 11B is multiplied by the 2D-DFT matrix shown in the right of FIG. 11B (the coefficients related to the solid-line portion of the DFT base of the 2D-DFT matrix shown in the left of FIG. 11B and the solid-line portion of the DFT base of the 2D-DFT matrix shown in the right of FIG. 11B are the hatched portions of the matrix of option 1.2 shown in the center of FIG. 11B).
  • the UE obtains (extracts) the input information This extraction may be performed for each DFT base.
  • the DFT base may correspond to a vector that can be calculated based on at least one of the same DFT components (or the same DFT coefficients/DFT indexes) and an oversampling factor, and may correspond to a row/column of the above-mentioned channel matrix/precoding matrix.
  • one DFT base may correspond to one vector (one row vector or column vector) in a 2D DFT matrix.
  • a DFT basis may be a vector containing components of an equation (eg, Equation 1 below) corresponding to parameters such as DFT index/oversampling factor.
  • Equation 1 Equation 1
  • the DFT index may be an index for identifying the DFT base
  • the sampling point may be an index indicating a particular sample
  • the sampling point may take values of 0, 1, ..., (number of samples) * (oversampling factor) - 1.
  • the DFT index may be called a DFT base index, etc.
  • the DFT index may correspond to the row number/column number of the channel matrix/precoding matrix.
  • FIGS. 12A and 12B are diagrams showing an example of extraction of input information based on DFT bases.
  • FIG. 12A shows the selected DFT bases.
  • FIG. 12A shows the case where two DFT bases are selected for rows (angle domain, antenna port domain) and one DFT base is selected for columns (delay domain, subband domain).
  • the input information may correspond to an element where the row-wise DFT base and the column-wise DFT base intersect, and with respect to FIG. 12A, the elements in the hatched portion shown in FIG. 12B may be determined as the input information.
  • the UE may transmit information regarding the DFT base to the NW.
  • the UE may transmit the information by including it in the CSI report, or may transmit it separately from the CSI report.
  • the information regarding the DFT base may be information regarding how many DFT bases are selected (reported) in a specific CSI report/CSI Part 1/CSI Part 2/CSI Part X, or may be information regarding which DFT base is selected (reported) in a specific CSI report/CSI Part 1/CSI Part 2/CSI Part X (information for identifying the DFT base).
  • CSI part X may refer to a newly defined CSI part other than CSI part 1/2.
  • Information corresponding to the output from the encoder described in this disclosure may be included in CSI part 1/2/X.
  • the UE may transmit information to the network regarding how many DFT bases are selected (reported) in a particular CSI part (e.g., CSI part 1).
  • the UE may transmit information regarding how many DFT bases are selected (reported) in a CSI report (a certain CSI part (e.g., CSI part 2) of the CSI report) using another CSI part (e.g., CSI part 1).
  • the UE may determine the number of elements to extract (e.g., DFT bases corresponding to the elements to extract)/elements to extract (e.g., DFT bases corresponding to the elements to extract) based on a specific rule or based on information about the received DFT bases.
  • elements to extract e.g., DFT bases corresponding to the elements to extract
  • elements to extract e.g., DFT bases corresponding to the elements to extract
  • Information regarding the DFT base that the UE transmits/receives may be notified to the UE using physical layer signaling (e.g., DCI, UCI), higher layer signaling (e.g., RRC signaling, MAC CE), specific signals/channels, or a combination of these, or may be UE capabilities.
  • physical layer signaling e.g., DCI, UCI
  • higher layer signaling e.g., RRC signaling, MAC CE
  • specific signals/channels e.g., a combination of these, or may be UE capabilities.
  • the beam information may include information indicating the L1-RSRP, which may correspond to (be measured/predicted based on) a resource, and which may be associated with a time instance/duration.
  • the beam information may include information indicating the top-X probability.
  • the top-X probability of a resource among one or more resources may mean the probability/confidence/confidence interval that the RSRP or SINR corresponding to the resource is equal to or greater than the Xth largest RSRP or SINR among the RSRPs or SINRs corresponding to the one or more resources.
  • This confidence interval may be an arbitrary percentage (e.g., 95%) confidence interval.
  • the beam information may include information indicating the top-X'/1 probability.
  • the top-X'/1 probability for one or more resources may mean the probability/confidence/confidence interval that at least one of the RSRPs corresponding to X' resources is the maximum among the RSRPs or SINRs corresponding to the one or more resources.
  • This confidence interval may be an arbitrary percentage (e.g., 95%) confidence interval. Note that, for the same value of X', if different top-X'/1 probabilities are obtained depending on how the resources are selected, one of these values (e.g., the maximum value) may be determined as the top-X'/1 probability.
  • the information indicating the L1-RSRP, the top X probability, or the top X'/1 probability may include information indicating the difference from another L1-RSRP, the top X probability, or the top X'/1 probability (difference information).
  • the information indicating the L1-RSRP, the top X probability, the top X'/1 probability, or the difference information therefor may be quantized information (quantization information).
  • the quantization information may correspond to information in which the L1-RSRP, the top X probability, the top X'/1 probability, or the difference therefor is represented by a specific number of bits divided by a specific quantization resolution (e.g., dB step size) for a specific expressible range (the value indicated by the bit corresponds to one of the steps (divisions)).
  • the quantized information of the difference information is preferably represented with a smaller number of bits than the quantized information of non-differential information (e.g., the quantized resolution is lower or the expressible range is narrower than the quantized information of non-differential information), but it may be represented with the same or a larger number of bits.
  • the information regarding X, X', the specific range, the specific quantization resolution, the specific number, etc. may be notified to the UE by the network, may be specified in a standard, may be derived from a model (associated model) used for beam prediction, or may be determined based on other information within the same reporting instance.
  • L1-RSRP, the above-mentioned top-X probability, the above-mentioned top-X'/1 probability, etc. may be interchangeably read as input candidates (e.g., CIR) for the AI model of the above-mentioned BM case 1/2.
  • the above information may be determined based on at least one of the following: parameters received using at least one of the methods described in Supplementary Note 2 below, parameters predefined in the specification, and parameters derived from an associated beam prediction model.
  • dataset identification settings may be read as a pre-configuration of a dataset requirement.
  • Dataset specific settings may include at least one of the information/parameters described below.
  • the dataset specific settings may include at least one of information/parameters indicating the dataset information settings of the dataset that may be transferred/requested and the dataset information settings of the dataset that may be transferred/requested.
  • the information may be, for example, a dataset information setting ID.
  • the information may also be, for example, parameters related to the dataset information setting.
  • the dataset-specific settings may include information/parameters regarding at least one of the following: dataset-specific information and pre-settings for the dataset request.
  • the information/parameters may include at least one of the information/parameters listed below: - ID indicating data set information (setting/signaling)/data set request pre-setting information. Information indicating how many data samples may be transferred/requested. Information indicating how the dataset is transferred (eg, periodic/semi-persistent/non-periodic) (information indicating the type of dataset transfer). Information indicating what features are being transferred/requested (dimension of the dataset). Information indicating what quantization method/resolution is applied to the values of the data set that may be transferred.
  • the information/parameters regarding at least one of the dataset specific information and the pre-configuration for the dataset request may include information indicating how many data samples may be transferred/requested per transmission (transmission instance).
  • the second embodiment relates to a dataset request for a dataset transfer.
  • the second embodiment is broadly divided into embodiments 2-1 to 2-4.
  • the UE/NW may conform to at least one of the following embodiments 2-1 to 2-4.
  • the UE/base station/LMF may request/instruct the base station/LMF to transfer the data set information setting.
  • the request/instruction may be made in accordance with at least one of the methods described in Supplementary Note 3 below.
  • the UE may determine/judge the transfer of data set information that may be indicated/requested based on at least one of the methods defined in the first embodiment and the specification above.
  • the UE/NW may follow at least one of options 2-1-1 and 2-1-2 below.
  • the UE/base station/LMF may send a data set request setting to the base station/LMF.
  • the UE/base station/LMF may request/indicate one of the presets for the data set request.
  • the UE/base station/LMF may send an ID associated with the pre-configuration of the data set request.
  • instructions regarding a data set request to the network can be appropriately issued.
  • the UE/base station/LMF may receive a response to the data set request.
  • the response may be sent according to at least one of the methods described in Supplementary Note 2 below.
  • the data set request may be sent in the manner described in embodiment 2-1 above.
  • the UE/base station/LMF may receive an acknowledgement/reject corresponding to the data set request. At this time, the UE/base station/LMF may also receive the reason for the rejection.
  • the UE may receive a request/instruction from the base station/LMF regarding the transfer of the data set information.
  • the request/instruction may be sent according to at least one of the methods described in Supplementary Note 2 below.
  • the UE may receive a data set request setting from the base station/LMF.
  • instructions regarding a data set request can be appropriately given to the UE.
  • the UE may send/report a response to the data set request.
  • the response may be sent according to at least one of the methods described in Supplementary Note 3 below.
  • the data set request may be sent in the manner described in embodiment 2-3 above.
  • the UE may send/report an acknowledgement/reject corresponding to the data set request. At this time, the UE may also send the reason for the rejection.
  • the dataset request settings may include at least one of the information/parameters described below.
  • the dataset request setting may include at least one of information/parameters indicating the dataset information setting of the dataset for which transfer is requested/instructed, and the dataset information setting of the dataset for which transfer is requested/instructed.
  • the information may be, for example, a dataset information setting ID.
  • the information may also be, for example, parameters related to the dataset information setting.
  • the dataset request settings may include information/parameters regarding the dataset request information.
  • the information/parameters may include at least one of the information/parameters listed below: An ID indicating data set request information (setting/signaling). A parameter indicating how many data samples are requested. Information indicating which dataset features are requested to be transferred (dimension of the dataset). - Information indicating the data set transfer method (eg, the desired data set transfer method). - Information indicating the requested quality of the data samples included in the dataset. - Information indicating whether the label dataset is included. Information indicating the quantization method/resolution for the requested data set.
  • the information may be specified/introduced separately for the labeled and unlabeled datasets.
  • a dataset request for dataset transfer can be made appropriately.
  • the third embodiment relates to the transfer of data sets.
  • the third embodiment is broadly divided into embodiment 3-1, which describes the transfer of a data set to a network, and embodiment 3-2, which describes the transfer of a data set to a UE.
  • the UE/base station/LMF may transfer the data set to the base station/LMF.
  • the transfer may be carried out in accordance with at least one of the methods described in Supplementary Note 3 below.
  • the UE/base station/LMF may send a data set transfer setting to the base station/LMF.
  • the node transferring the data set may send a message/signal regarding the end of the data set transfer.
  • the node eg, base station/LMF receiving (transferred) the data set may send a message/signal regarding the end of the data set transfer.
  • the message/signal regarding the completion of the data set transfer may include at least one of the following specific information/parameters:
  • the particular information/parameter may be, for example, information indicating the reason for terminating the data set transfer.
  • the information indicating the reason may be information indicating that the amount of data set transferred is sufficient, information indicating that there is a problem with the storage, etc.
  • the particular information/parameter may be, for example, information indicating the number of additional data samples required.
  • the node transmitting/receiving the data set may terminate the transmission of the data set after transmitting the additional data for the indicated number of data samples.
  • data set transfer to the network and its termination can be performed appropriately.
  • the UE may be forwarded the data set from the base station/LMF.
  • the UE may receive the data set from the base station/LMF.
  • the transfer/reception may be performed according to at least one of the methods described in Supplementary Note 2 below.
  • the UE may receive a data set transfer setting from the base station/LMF.
  • the node transferring the data set may send a message/signal regarding the end of the data set transfer.
  • the node (eg, UE) receiving (transferred to) the data set may send a message/signal regarding the end of the data set transfer.
  • the message/signal regarding the end of the data set transfer in embodiment 3-2 may include information/parameters included in the message/signal regarding the end of the data set transfer described in embodiment 3-1.
  • data set transfer to the UE and its termination can be performed appropriately.
  • the dataset transfer settings may include at least one of the information/parameters described below.
  • the dataset transfer settings may include at least one of information/parameters indicating the dataset information settings of the dataset to be transferred and the dataset information settings of the dataset to be transferred.
  • the information may be, for example, a dataset information setting ID.
  • the information may also be, for example, parameters related to the dataset information setting.
  • the dataset transfer settings may include information/parameters regarding dataset transfer information.
  • the information/parameters may include at least one of the information/parameters listed below: - ID indicating data set transfer information (setting/signaling). A parameter indicating how many (data) samples are included in the signaling of a data set transfer. - Information indicating which dataset features are included in the dataset transfer signaling (dataset dimension). Information indicating the quantization method/resolution to be applied to the requested dataset.
  • data sets can be transferred appropriately.
  • the fourth embodiment relates to properties of a data sample.
  • the fourth embodiment may be applied, for example, when model monitoring is implemented.
  • the fourth embodiment is broadly divided into embodiments 4-1 to 4-4.
  • the UE/NW may follow at least one of the embodiments 4-1 to 4-4.
  • property may be interpreted interchangeably as distribution/distribution characteristic.
  • the UE may transmit a data set information setting including specific information to the NW.
  • the UE may receive a data set information setting from the network that includes specific information.
  • the specific information may be information about the properties of the data sample (e.g., information indicating the properties of the data sample/property ID).
  • Information/property ID indicating the properties of a data sample may be associated with a particular feature/label.
  • the distribution of a particular feature may be different.
  • the UE/NW can learn multiple different distributions based on a data set.
  • the UE may be configured/instructed to detect properties regarding specific information.
  • the particular information may be, for example, information based on (e.g., derived from) UE measurements.
  • the information based on (e.g., derived from) UE measurements may refer to a monitored feature.
  • Information based on UE measurements may be features/labels.
  • the UE may detect whether the property of the particular information is classified as the same property as the property being set/indicated.
  • the property to be set/indicated may be interpreted as one or more of the multiple properties to be set/indicated.
  • the UE may detect a property of the particular information.
  • the UE may detect one property of the specific information from among the configured/instructed properties.
  • property detection can be performed appropriately.
  • the UE may be configured/instructed to report certain information properties.
  • the specific information may be at least one of information based on UE measurements, information derived from UE measurements, and monitored features. These may be interchangeable.
  • the NW can estimate performance according to whether the properties of the input/output samples match the desired properties for the model.
  • the UE may report whether the property of the particular information falls into the same category as the property being configured/indicated.
  • the property to be set/indicated may be interpreted as one or more of the multiple properties to be set/indicated.
  • the UE may report a bit (an X bit (e.g., X is 1)) indicating whether the distribution of the monitored feature is the same as the dataset.
  • a bit an X bit (e.g., X is 1)
  • the UE may report one property of that particular information.
  • the UE may detect one property of the specific information from among the configured/instructed properties.
  • the UE may report the property ID of the feature being monitored.
  • the UE may report property information. This can reduce the reporting frequency and suppress an increase in overhead.
  • the UE may be configured/instructed to report when a property of a monitored feature changes.
  • the configuration/instruction may be based on at least one of the methods described in Supplementary Note 2 below.
  • property reporting can be performed appropriately.
  • the UE may perform certain functionality/model operation(s) based on the properties of the particular information detected.
  • the specific information may be at least one of information based on UE measurements, information derived from UE measurements, and monitored features. These may be interchangeable.
  • the UE may be configured to perform a particular function/model of operation when the property of the monitored feature is a particular property.
  • the operation of the specific function/model may be selection/activation/deactivation/switching/fallback of the function/model.
  • the operation of the specific function/model may be configured for the UE or may be determined/judged based on rules predefined in the specifications.
  • the specific property may be configured for the UE or may be determined/judged based on rules predefined in the specifications.
  • the UE may report to the NW at least one of the following: that a particular function/model is currently being operated; that a particular function/model is currently being operated; and that a particular function/model has been operated.
  • the UE may not have to report to the NW at least one of the following: that a particular function/model is currently being operated, that a particular function/model is currently being operated, and that a particular function/model has been operated.
  • Option 4-4-2 can reduce the signaling overhead of the UE.
  • the UE may report properties of monitored features instead of at least one of: that a particular function/model is currently operating, that a particular function/model is currently operating, and that a particular function/model is currently operating.
  • the UE may be configured/instructed as to which option to apply using higher layer signaling/physical layer signaling, may determine which option to select according to rules predefined in the specifications, or may determine which option to select based on the UE capabilities.
  • the UE can appropriately report the operation of functions/models.
  • AI model information may mean information including at least one of the following: - AI model input/output information, - Pre-processing/post-processing information for input/output of AI models; ⁇ Information on the parameters of the AI model, - Training information for the AI model; - Inference information for AI models, ⁇ Performance information about the AI model.
  • the input/output information of the AI model may include information regarding at least one of the following: Content of input/output data (e.g. RSRP, SINR, amplitude/phase information in the channel matrix (or precoding matrix), information on the Angle of Arrival (AoA), information on the Angle of Departure (AoD), location information); - auxiliary information of the data (which may be called meta-information); - Input/output data types (e.g. immutable values, floating point numbers), - Bit width of input/output data (e.g. 64 bits for each input value), Quantization interval (quantization step size) of input/output data (e.g., 1 dBm for L1-RSRP); The range that the input/output data can take (e.g., [0, 1]).
  • Content of input/output data e.g. RSRP, SINR, amplitude/phase information in the channel matrix (or precoding matrix), information on the Angle of Arrival (AoA), information on the
  • the information regarding AoA may include information regarding at least one of the azimuth angle of arrival and the zenith angle of arrival (ZoA). Furthermore, the information regarding AoD may include information regarding at least one of the azimuth angle of departure and the zenith angle of departure (ZoD).
  • the location information may be location information regarding the UE/NW.
  • the location information may include at least one of information (e.g., latitude, longitude, altitude) obtained using a positioning system (e.g., a satellite positioning system (Global Navigation Satellite System (GNSS), Global Positioning System (GPS), etc.)), information on the BS adjacent to (or serving) the UE (e.g., a BS/cell identifier (ID), a BS-UE distance, a direction/angle of the BS (UE) as seen from the UE (BS), coordinates of the BS (UE) as seen from the UE (BS) (e.g., coordinates on the X/Y/Z axes), etc.), a specific address of the UE (e.g., an Internet Protocol (IP) address), etc.
  • IP Internet Protocol
  • the location information of the UE is not limited to information based on the position of the BS, and may be information based on a specific point.
  • the location information may include information about its implementation (e.g., 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 following: a mobility type, a moving speed of the UE, an acceleration of the UE, and a moving direction of the UE.
  • the mobility type may correspond to at least one of fixed location UE, movable/moving UE, no mobility UE, low mobility UE, middle mobility UE, high mobility UE, cell-edge UE, not-cell-edge UE, etc.
  • environmental information may be information regarding the environment in which the data is acquired/used, and may correspond to, for example, frequency information (such as a band ID), environmental type information (information indicating at least one of indoor, outdoor, Urban Macro (UMa), Urban Micro (Umi), etc.), information indicating Line Of Site (LOS)/Non-Line Of Site (NLOS), etc.
  • frequency information such as a band ID
  • environmental type information information indicating at least one of indoor, outdoor, Urban Macro (UMa), Urban Micro (Umi), etc.
  • LOS Line Of Site
  • NLOS Non-Line Of Site
  • LOS may mean that the UE and BS are in an environment where they can see each other (or there is no obstruction)
  • NLOS may mean that the UE and BS are not in an environment where they can see each other (or there is an obstruction).
  • Information indicating LOS/NLOS may indicate a soft value (e.g., the probability of LOS/NLOS) or a hard value (e.g., either LOS or NLOS).
  • meta-information may mean, for example, information regarding input/output information suitable for an AI model, information regarding data that has been acquired/can be acquired, etc.
  • meta-information may include information regarding beams of RS (e.g., CSI-RS/SRS/SSB, etc.) (e.g., the pointing angle of each beam, 3 dB beam width, the shape of the pointed beam, the number of beams), layout information of gNB/UE antennas, frequency information, environmental information, meta-information ID, etc.
  • RS e.g., CSI-RS/SRS/SSB, etc.
  • meta-information may be used as input/output of an AI model.
  • the pre-processing/post-processing information for the input/output of the AI model may include information regarding at least one of the following: Whether to apply normalization (e.g., Z-score normalization, min-max normalization), Parameters for normalization (e.g. mean/variance for Z-score normalization, min/max for min-max normalization); Whether to apply a specific numeric transformation method (e.g., one hot encoding, label encoding, etc.); Selection rule for whether or not to use as training data.
  • normalization e.g., Z-score normalization, min-max normalization
  • Parameters for normalization e.g. mean/variance for Z-score normalization, min/max for min-max normalization
  • a specific numeric transformation method e.g., one hot encoding, label encoding, etc.
  • the information of the parameters of the AI model may include information regarding at least one of the following: - Weight information in an AI model (e.g., neuron coefficients (connection coefficients)), ⁇ Structure of the AI model, -
  • the type of AI model as a model component e.g., Residual Network (ResNet), DenseNet, RefineNet, Transformer model, CRBlock, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)
  • - Functions of the AI model as model components e.g., decoder, encoder.
  • the weight information in the AI model may include information regarding at least one of the following: - Bit width (size) of weight information Quantization interval of weight information, - Granularity of weight information, - The range of possible weight information - Weight parameters in the AI model, - Information on the difference from the AI model before the update (if updating), - Method of weight initialization (e.g., zero initialization, random initialization (based on normal/uniform/truncated normal distribution), Xavier initialization (for sigmoid function), He initialization (for Rectified Linear Units (ReLU))).
  • the structure of the AI model may also include information regarding at least one of the following: Number of layers, - Type of layer (e.g., convolutional 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 feature (L2 regularization, dropout feature, etc.), where to put this feature (e.g., after which layer)).
  • - Type of layer e.g., convolutional layer, activation layer, dense layer, normalization layer, pooling layer, attention layer
  • - Layer information e.g., Time series specific parameters (e.g. bidirectionality, time step)
  • Parameters for training e.g., type of feature (L2 regularization, dropout feature, etc.), where to put this feature (e.g., after which layer)).
  • the layer information may include information regarding at least one of the following: - The number of neurons in each layer, - kernel size, strides for pooling/convolutional layers, Pooling method (MaxPooling, AveragePooling, etc.), - Information on the residual block, Number of heads, - Normalization method (batch normalization, instance normalization, layer normalization, etc.), Activation functions (sigmoid, tanh function, ReLU, leaky ReLU information, Maxout, Softmax).
  • An AI model may be included as a component of another AI model.
  • an AI model may be an AI model in which processing proceeds in the order of model component #1 (ResNet), model component #2 (a transformer model), a dense layer, and a normalization layer.
  • ResNet model component #1
  • model component #2 a transformer model
  • dense layer a dense layer
  • normalization layer a normalization layer
  • Training information for the AI model may include information regarding at least one of the following: Information for the optimization algorithm (e.g., type of optimization (Stochastic Gradient Descent (SGD)), AdaGrad, Adam, etc.), parameters of the optimization (learning rate, momentum information, etc.), Loss function information (e.g., information on metrics of the loss function (Mean Absolute Error (MAE)), Mean Square Error (MSE), Cross Entropy Loss, NLL Loss, Kullback-Leibler (KL) Divergence, etc.)); - parameters to be frozen for training (e.g. layers, weights), - parameters to be updated (e.g. layers, weights), - parameters (e.g. layers, weights) that should be (used as) initial parameters for training, How to train/update the AI model (e.g., (recommended) number of epochs, batch size, number of data used for training).
  • Information for the optimization algorithm e.g., type of optimization (S
  • the inference information for the AI model may include information regarding decision tree branch pruning, parameter quantization, and the function of the AI model.
  • the function of the AI model may correspond to at least one of, for example, time domain beam prediction, spatial domain beam prediction, autoencoder for CSI feedback, and autoencoder for beam management.
  • An autoencoder for CSI feedback may be used as follows: The UE inputs the CSI/channel matrix/precoding matrix into the AI model of the encoder and transmits the encoded bits as CSI feedback (CSI report); - The BS reconstructs the CSI/channel matrix/precoding matrix, which is output as input to the AI model of the decoder using the received encoded bits.
  • the UE/BS may input measurement results (beam quality, e.g., RSRP) based on sparse (or thick) beams into an AI model, which may output dense (or thin) beam quality.
  • beam quality e.g., RSRP
  • the UE/BS may input time series (past, present, etc.) measurement results (beam quality, e.g., RSRP) into an AI model and output future beam quality.
  • time series past, present, etc.
  • beam quality e.g., RSRP
  • the performance information regarding the AI model may include information regarding the expected value of a loss function defined for the AI model.
  • the AI model information in this disclosure may include information regarding the scope of application (scope of applicability) of the AI model.
  • the scope of application may be indicated by a physical cell ID, a serving cell index, etc.
  • Information regarding the scope of application may be included in the above-mentioned environmental information.
  • AI model information regarding a specific AI model may be predetermined in a standard, or may be notified to the UE from the network (NW).
  • An AI model defined in a standard may be referred to as a reference AI model.
  • AI model information regarding a 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 identifying the AI model (e.g., may be called an AI model index, an AI model ID, a model ID, etc.).
  • 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 (e.g., input/output information of the AI model) may be predetermined in a standard, or may be notified to the UE from the NW.
  • the AI model information in this disclosure may be associated with an AI model and may be referred to as AI model relevant information, simply relevant information, etc.
  • the AI model relevant information does not need to explicitly include information for identifying the AI model.
  • the AI model relevant information may be information that includes only meta information, for example.
  • the model ID may be interchangeably read as an ID (model set ID) corresponding to a set of AI models.
  • the model ID may be interchangeably read as a meta information ID.
  • the meta information (or meta information ID) may be associated with information regarding the beam (beam setting) as described above.
  • the meta information (or meta information ID) may be used by the UE to select an AI model taking into account which beam the BS is using, or may be used to notify the BS of which beam to use to apply the AI model deployed by the UE.
  • the meta information ID may be interchangeably read as an ID (meta information set ID) corresponding to a set of meta information.
  • any information may be notified to the UE (from the NW) (in other words, any information received from the BS in the UE) using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal/channel (e.g., PDCCH, PDSCH, reference signal), or a combination thereof.
  • physical layer signaling e.g., DCI
  • higher layer signaling e.g., RRC signaling, MAC CE
  • a specific signal/channel e.g., PDCCH, PDSCH, reference signal
  • the MAC CE may be identified by including in the MAC subheader a new Logical Channel ID (LCID) that is not specified in existing standards.
  • LCID Logical Channel ID
  • the notification When the notification is made by a DCI, the notification may be made by a specific field of the DCI, a Radio Network Temporary Identifier (RNTI) used to scramble Cyclic Redundancy Check (CRC) bits assigned to the DCI, the format of the DCI, etc.
  • RNTI Radio Network Temporary Identifier
  • CRC Cyclic Redundancy Check
  • notification of any information to the UE in the above-mentioned embodiments may be performed periodically, semi-persistently, or aperiodically.
  • notification of any information from the UE may be performed using physical layer signaling (e.g., UCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal/channel (e.g., PUCCH, PUSCH, reference signal), or a combination thereof.
  • physical layer signaling e.g., UCI
  • higher layer signaling e.g., RRC signaling, MAC CE
  • a specific signal/channel e.g., PUCCH, PUSCH, reference signal
  • the MAC CE may be identified by including a new LCID in the MAC subheader that is not specified in existing standards.
  • the notification may be transmitted using PUCCH or PUSCH.
  • notification of any information from the UE may be performed periodically, semi-persistently, or aperiodically.
  • At least one of the above-mentioned embodiments may be applied when a specific condition is satisfied, which may be specified in a standard or may be notified to a UE/BS using higher layer signaling/physical layer signaling.
  • At least one of the above-described embodiments may be applied only to UEs that have reported or support a particular UE capability.
  • the particular UE capability may indicate support for particular processing/operations/control/information for at least one of the above embodiments/options/options.
  • the above-mentioned specific UE capabilities may be capabilities that are applied across all frequencies (commonly regardless of frequency), capabilities per frequency (e.g., one or a combination of cell, band, band combination, BWP, component carrier, etc.), capabilities per frequency range (e.g., Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), capabilities per subcarrier spacing (SubCarrier Spacing (SCS)), or capabilities per Feature Set (FS) or Feature Set Per Component-carrier (FSPC).
  • FR1 Frequency Range 1
  • FR2 FR2, FR3, FR4, FR5, FR2-1, FR2-2
  • SCS subcarrier Spacing
  • FS Feature Set
  • FSPC Feature Set Per Component-carrier
  • the specific UE capabilities may be capabilities that are applied across all duplexing methods (commonly regardless of the duplexing method), or may be capabilities for each duplexing method (e.g., Time Division Duplex (TDD) and Frequency Division Duplex (FDD)).
  • TDD Time Division Duplex
  • FDD Frequency Division Duplex
  • the above-mentioned embodiments may be applied when the UE configures/activates/triggers specific information related to the above-mentioned embodiments (or performs the operations of the above-mentioned embodiments) by higher layer signaling/physical layer signaling.
  • the specific information may be information indicating the enablement of the use of an AI model, information indicating the enablement of CSI prediction, information indicating the enablement of data collection/model monitoring based on a data set, any RRC parameters for a particular release (e.g., Rel. 18/19), etc.
  • the UE may, for example, apply Rel. 15/16 operations.
  • Appendix A With respect to one embodiment of the present disclosure, the following invention is noted.
  • Appendix A-1 a transmitter for transmitting information regarding a transmittable data set to a network; a receiving unit for receiving a data set request transmitted based on information about the data set; a control unit that controls transfer of the data set based on the data set request.
  • Appendix A-2 The terminal according to Appendix A-1, wherein the information relating to the data set is data set specific support information.
  • Appendix A-3 The terminal according to claim 1 or 2, wherein the transmitting unit transmits a response signal to the data set request.
  • Appendix A-4 The terminal according to any one of Appendix A-1 to Appendix A-3, wherein the transmitting unit transmits a signal indicating the end of the transfer of the data set, or the receiving unit receives a signal indicating the end of the transfer of the data set.
  • Appendix B With respect to one embodiment of the present disclosure, the following invention is noted.
  • Appendix B-1 a receiver for receiving information about a set of data available for transmission by the network; a transmitter for transmitting a data set request based on information about the data set; A control unit that controls reception of the data set transferred based on the data set request.
  • Appendix B-2 The terminal according to Appendix B-1, wherein the information about the dataset is dataset-specific support information or a preset of the dataset request.
  • Appendix B-3 The terminal according to claim 1 or 2, wherein the receiving unit receives a response signal to the data set request.
  • Appendix B-4 The terminal according to any one of Appendix B-1 to Appendix B-3, wherein the transmitting unit transmits a signal indicating the end of the transfer of the data set, or the receiving unit receives a signal indicating the end of the transfer of the data set.
  • Appendix C With respect to one embodiment of the present disclosure, the following invention is noted.
  • Appendix C-1 a receiver for receiving a data set information setting including information indicative of a characteristic of the data sample and a setting for detecting a characteristic of the particular information;
  • a terminal having a control unit that detects the specific information based on the setting, and performs a specific model operation based on the detection of the specific information.
  • Appendix C-2 The terminal of claim 1, wherein the information indicating the characteristics of the data sample is associated with at least one of a specific feature and a specific label.
  • Appendix C-3 The terminal according to claim 1 or 2, wherein the specific information is a monitored feature.
  • the control unit is a terminal according to any one of Appendix C-1 to Appendix C-3, which reports characteristics of the specific information.
  • Wired communication system A 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 embodiments of the present disclosure or a combination of these.
  • FIG. 13 is a diagram showing an example of a schematic configuration of a wireless communication system according to an embodiment.
  • the wireless communication system 1 (which may simply be referred to as system 1) may be a system that realizes communication using Long Term Evolution (LTE) specified by the Third Generation Partnership Project (3GPP), 5th generation mobile communication system New Radio (5G NR), or the like.
  • LTE Long Term Evolution
  • 3GPP Third Generation Partnership Project
  • 5G NR 5th generation mobile communication system New Radio
  • the wireless communication system 1 may also support dual connectivity between multiple Radio Access Technologies (RATs) (Multi-RAT Dual Connectivity (MR-DC)).
  • MR-DC may include dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (EN-DC)), dual connectivity between NR and LTE (NR-E-UTRA Dual Connectivity (NE-DC)), etc.
  • RATs Radio Access Technologies
  • MR-DC may include dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (EN-DC)), dual connectivity between NR and LTE (NR-E-UTRA Dual Connectivity (NE-DC)), etc.
  • 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 (MN), and the NR base station (gNB) is the secondary node (SN).
  • the NR base station (gNB) is the MN, and the LTE (E-UTRA) base station (eNB) is the SN.
  • the wireless communication system 1 may support dual connectivity between multiple base stations within the same RAT (e.g., dual connectivity in which both the MN and SN are NR base stations (gNBs) (NR-NR Dual Connectivity (NN-DC))).
  • dual connectivity in which both the MN and SN are NR base stations (gNBs) (NR-NR Dual Connectivity (NN-DC))).
  • gNBs NR base stations
  • N-DC Dual Connectivity
  • the wireless communication system 1 may include a base station 11 that forms a macrocell C1 with a relatively wide coverage, and base stations 12 (12a-12c) that are arranged within the macrocell C1 and form a small cell C2 that is narrower than the macrocell C1.
  • a user terminal 20 may be located within at least one of the cells. The arrangement and number of each cell and user terminal 20 are not limited to the embodiment shown in the figure. Hereinafter, when there is no need to distinguish between the base stations 11 and 12, they will be collectively referred to as base station 10.
  • the user terminal 20 may be connected to at least one of the multiple base stations 10.
  • the user terminal 20 may utilize at least one of carrier aggregation (CA) using multiple 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 for example, FR1 may correspond to a higher frequency band than FR2.
  • 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 multiple base stations 10 may be connected by wire (e.g., optical fiber conforming to the Common Public Radio Interface (CPRI), X2 interface, etc.) or wirelessly (e.g., NR communication).
  • wire e.g., optical fiber conforming to the Common Public Radio Interface (CPRI), X2 interface, etc.
  • NR communication e.g., NR communication
  • base station 11 which corresponds to the upper station
  • IAB Integrated Access Backhaul
  • base station 12 which corresponds to a relay station
  • the base station 10 may be connected to the core network 30 directly or via another base station 10.
  • the core network 30 may include at least one of, for example, an Evolved Packet Core (EPC), a 5G Core Network (5GCN), a Next Generation Core (NGC), etc.
  • EPC Evolved Packet Core
  • 5GCN 5G Core Network
  • NGC Next Generation Core
  • the core network 30 may include network functions (Network Functions (NF)) such as, for example, a User Plane Function (UPF), an Access and Mobility management Function (AMF), a Session Management Function (SMF), a Unified Data Management (UDM), an Application Function (AF), a Data Network (DN), a Location Management Function (LMF), and Operation, Administration and Maintenance (Management) (OAM).
  • NF Network Functions
  • UPF User Plane Function
  • AMF Access and Mobility management Function
  • SMF Session Management Function
  • UDM Unified Data Management
  • AF Application Function
  • DN Data Network
  • LMF Location Management Function
  • OAM Operation, Administration and Maintenance
  • the user terminal 20 may be a terminal that supports at least one of the communication methods such as LTE, LTE-A, and 5G.
  • a wireless access method based on Orthogonal Frequency Division Multiplexing 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
  • the radio access method may also be called a waveform.
  • other radio access methods e.g., other single-carrier transmission methods, other multi-carrier transmission methods
  • a downlink shared channel (Physical Downlink Shared Channel (PDSCH)) shared by each user terminal 20, a broadcast channel (Physical Broadcast Channel (PBCH)), a downlink control channel (Physical Downlink Control Channel (PDCCH)), etc. may be used as the downlink channel.
  • PDSCH Physical Downlink Shared Channel
  • PBCH Physical Broadcast Channel
  • PDCCH Physical Downlink Control Channel
  • an uplink shared channel (Physical Uplink Shared Channel (PUSCH)) shared by each user terminal 20, an uplink control channel (Physical Uplink Control Channel (PUCCH)), a random access channel (Physical Random Access Channel (PRACH)), etc. may be used as an uplink channel.
  • PUSCH Physical Uplink Shared Channel
  • PUCCH Physical Uplink Control Channel
  • PRACH Physical Random Access Channel
  • SIB System Information Block
  • PDSCH User data, upper layer control information, System Information Block (SIB), etc.
  • SIB System Information Block
  • PUSCH User data, upper layer control information, etc.
  • MIB Master Information Block
  • PBCH Physical Broadcast Channel
  • Lower layer control information may be transmitted by the PDCCH.
  • the lower layer control information may include, for example, downlink control information (Downlink Control Information (DCI)) including scheduling information for at least one of the PDSCH and the PUSCH.
  • DCI Downlink Control Information
  • the DCI for scheduling the PDSCH may be called a DL assignment or DL DCI
  • the DCI for scheduling the PUSCH may be called a UL grant or UL DCI.
  • the PDSCH may be interpreted as DL data
  • the PUSCH may be interpreted as UL data.
  • a control resource set (COntrol REsource SET (CORESET)) and a search space may be used to detect the PDCCH.
  • the CORESET corresponds to the resources to search for DCI.
  • the search space corresponds to the search region and search method of PDCCH candidates.
  • One CORESET may be associated with one or multiple search spaces. The UE may monitor the CORESET associated with a search space based on the search space configuration.
  • a 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 the terms “search space,” “search space set,” “search space setting,” “search space set setting,” “CORESET,” “CORESET setting,” etc. in this disclosure may be read as interchangeable.
  • the PUCCH may transmit uplink control information (UCI) including at least one of channel state information (CSI), delivery confirmation information (which may be called, for example, Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), ACK/NACK, etc.), and a scheduling request (SR).
  • UCI uplink control information
  • CSI channel state information
  • HARQ-ACK Hybrid Automatic Repeat reQuest ACKnowledgement
  • ACK/NACK ACK/NACK
  • SR scheduling request
  • the PRACH may transmit a random access preamble for establishing a connection with a cell.
  • downlink, uplink, etc. may be expressed without adding "link.”
  • various channels may be expressed without adding "Physical” to the beginning.
  • a synchronization signal (SS), a downlink reference signal (DL-RS), etc. may be transmitted.
  • a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS), a demodulation reference signal (DMRS), a positioning reference signal (PRS), a 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 an SS (PSS, SSS) and a PBCH (and a DMRS for PBCH) may be called an SS/PBCH block, an SS Block (SSB), etc.
  • the SS, SSB, etc. may also be called a reference signal.
  • a measurement reference signal Sounding Reference Signal (SRS)
  • a demodulation reference signal DMRS
  • UL-RS uplink reference signal
  • DMRS may also be called a user equipment-specific reference signal (UE-specific Reference Signal).
  • the base station 14 is a diagram showing an example of a configuration of a base station according to an embodiment.
  • the base station 10 includes a control unit 110, a transceiver unit 120, a transceiver antenna 130, and a transmission line interface 140. Note that one or more of each of the control unit 110, the transceiver unit 120, the transceiver antenna 130, and the transmission line interface 140 may be provided.
  • this example mainly shows the functional blocks of the characteristic parts of this embodiment, and the base station 10 may also be assumed to have other functional blocks necessary for wireless communication. Some of the processing of each part 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 are described based on a common understanding in the technical field to which this disclosure pertains.
  • the control unit 110 may control signal generation, scheduling (e.g., resource allocation, mapping), etc.
  • the control unit 110 may control transmission and reception using the transceiver unit 120, the transceiver antenna 130, and the transmission path interface 140, measurement, etc.
  • the control unit 110 may generate data, control information, sequences, etc. to be transmitted as signals, and transfer them to the transceiver unit 120.
  • the control unit 110 may perform call processing of communication channels (setting, release, etc.), status management of the base station 10, management of radio resources, etc.
  • the transceiver unit 120 may include a baseband unit 121, a radio frequency (RF) unit 122, and a measurement unit 123.
  • the baseband unit 121 may include a transmission processing unit 1211 and a reception processing unit 1212.
  • the transceiver unit 120 may be composed of a transmitter/receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transceiver circuit, etc., which are described based on a common understanding in the technical field to which the present disclosure relates.
  • the transceiver unit 120 may be configured as an integrated transceiver unit, or may be composed of a transmission unit and a reception unit.
  • the transmission unit may be composed of a transmission processing unit 1211 and an RF unit 122.
  • the reception unit may be composed of a reception processing unit 1212, an RF unit 122, and a measurement unit 123.
  • the transmitting/receiving antenna 130 can be configured as an antenna described based on common understanding in the technical field to which this disclosure pertains, such as an array antenna.
  • the transceiver 120 may transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc.
  • the transceiver 120 may receive the above-mentioned uplink channel, uplink reference signal, etc.
  • the transceiver 120 may form at least one of the transmit beam and the receive beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), etc.
  • digital beamforming e.g., precoding
  • analog beamforming e.g., phase rotation
  • the transceiver 120 may perform Packet Data Convergence Protocol (PDCP) layer processing, Radio Link Control (RLC) layer processing (e.g., RLC retransmission control), Medium Access Control (MAC) layer processing (e.g., HARQ retransmission control), etc., on data and control information obtained from the control unit 110, and generate a bit string to be transmitted.
  • PDCP Packet Data Convergence Protocol
  • RLC Radio Link Control
  • MAC Medium Access Control
  • HARQ retransmission control HARQ retransmission control
  • the transceiver 120 may perform transmission processing such as channel coding (which may include error correction coding), modulation, mapping, filtering, Discrete Fourier Transform (DFT) processing (if necessary), Inverse Fast Fourier Transform (IFFT) processing, precoding, and digital-to-analog conversion on the bit string to be transmitted, and output a baseband signal.
  • transmission processing such as channel coding (which may include error correction coding), modulation, mapping, filtering, Discrete Fourier Transform (DFT) processing (if necessary), Inverse Fast Fourier Transform (IFFT) processing, precoding, and digital-to-analog conversion on the bit string to be transmitted, and output a baseband signal.
  • channel coding which may include error correction coding
  • DFT Discrete Fourier Transform
  • IFFT Inverse Fast Fourier Transform
  • the transceiver unit 120 may perform modulation, filtering, amplification, etc., on the baseband signal to a radio frequency band, and transmit the radio frequency band signal via the transceiver antenna 130.
  • the transceiver unit 120 may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency band signal received by the transceiver antenna 130.
  • the transceiver 120 may apply reception processing such as analog-to-digital conversion, Fast Fourier Transform (FFT) processing, Inverse Discrete Fourier Transform (IDFT) processing (if necessary), filtering, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal, and acquire user data, etc.
  • reception processing such as analog-to-digital conversion, Fast Fourier Transform (FFT) processing, Inverse Discrete Fourier Transform (IDFT) processing (if necessary), filtering, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal, and acquire user data, etc.
  • FFT Fast Fourier Transform
  • IDFT Inverse Discrete Fourier Transform
  • the transceiver 120 may perform measurements on the received signal.
  • the measurement unit 123 may perform Radio Resource Management (RRM) measurements, Channel State Information (CSI) measurements, etc. based on the received signal.
  • the measurement unit 123 may measure received power (e.g., Reference Signal Received Power (RSRP)), received quality (e.g., Reference Signal Received Quality (RSRQ), Signal to Interference plus Noise Ratio (SINR), Signal to Noise Ratio (SNR)), signal strength (e.g., Received Signal Strength Indicator (RSSI)), propagation path information (e.g., CSI), etc.
  • RSRP Reference Signal Received Power
  • RSSI Received Signal Strength Indicator
  • the measurement results may be output to the control unit 110.
  • the transmission path interface 140 may transmit and receive signals (backhaul signaling) between devices included in the core network 30 (e.g., network nodes providing NF), other base stations 10, etc., and may acquire and transmit user data (user plane data), control plane data, etc. for the user terminal 20.
  • devices included in the core network 30 e.g., network nodes providing NF
  • other base stations 10, etc. may acquire and transmit user data (user plane data), control plane data, etc. for the user terminal 20.
  • the transmitter and receiver of the base station 10 in this disclosure may be configured with at least one of the transmitter/receiver 120, the transmitter/receiver antenna 130, and the transmission path interface 140.
  • the transceiver 120 may receive information about a dataset that can be transmitted to the network.
  • the transceiver 120 may transmit a dataset request based on the information about the dataset.
  • the control unit 110 may control the reception of the dataset that is transferred based on the dataset request (first, second, and third embodiments).
  • the transceiver 120 may transmit information about a dataset that can be transmitted by the network.
  • the transceiver 120 may receive a dataset request that is transmitted based on the information about the dataset.
  • the control unit 110 may control the transfer of the dataset based on the dataset request (first, second, and third embodiments).
  • the transceiver unit 120 may transmit a dataset information setting including information indicating characteristics of the data sample, and a setting for detection of characteristics of specific information.
  • the control unit 110 may use the setting to instruct detection of the specific information, and may use the detection of the specific information to instruct a specific model operation (fourth embodiment).
  • the (User terminal) 15 is a diagram showing an example of the configuration of a user terminal according to an embodiment.
  • the user terminal 20 includes a control unit 210, a transceiver unit 220, and a transceiver antenna 230. Note that the control unit 210, the transceiver unit 220, and the transceiver antenna 230 may each include one or more.
  • this example mainly shows the functional blocks of the characteristic parts of this embodiment, and the user terminal 20 may also be assumed to have other functional blocks necessary for wireless communication. Some of the processing of each part 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 are described based on a common understanding in the technical field to which this disclosure pertains.
  • the control unit 210 may control signal generation, mapping, etc.
  • the control unit 210 may control transmission and reception using the transceiver unit 220 and the transceiver antenna 230, measurement, etc.
  • the control unit 210 may generate data, control information, sequences, etc. to be transmitted as signals, and transfer them to the transceiver unit 220.
  • the transceiver unit 220 may include a baseband unit 221, an RF unit 222, and a measurement unit 223.
  • the baseband unit 221 may include a transmission processing unit 2211 and a reception processing unit 2212.
  • the transceiver unit 220 may be composed of a transmitter/receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transceiver circuit, etc., which are described based on a common understanding in the technical field to which the present disclosure relates.
  • the transceiver unit 220 may be configured as an integrated transceiver unit, or may be composed of a transmission unit and a reception unit.
  • the transmission unit may be composed of a transmission processing unit 2211 and an RF unit 222.
  • the reception unit may be composed of a reception processing unit 2212, an RF unit 222, and a measurement unit 223.
  • the transmitting/receiving antenna 230 can be configured as an antenna described based on common understanding in the technical field to which this disclosure pertains, such as an array antenna.
  • the transceiver 220 may receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc.
  • the transceiver 220 may transmit the above-mentioned uplink channel, uplink reference signal, etc.
  • the transceiver 220 may form at least one of the transmit beam and receive beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), etc.
  • digital beamforming e.g., precoding
  • analog beamforming e.g., phase rotation
  • the transceiver 220 may perform PDCP layer processing, RLC layer processing (e.g., RLC retransmission control), MAC layer processing (e.g., HARQ retransmission control), etc. on the data and control information acquired from the controller 210, and generate a bit string to be transmitted.
  • RLC layer processing e.g., RLC retransmission control
  • MAC layer processing e.g., HARQ retransmission control
  • the transceiver 220 may perform transmission processing such as channel coding (which may include error correction coding), modulation, mapping, filtering, DFT processing (if necessary), IFFT processing, precoding, and digital-to-analog conversion on the bit string to be transmitted, and output a baseband signal.
  • transmission processing such as channel coding (which may include error correction coding), modulation, mapping, filtering, DFT processing (if necessary), IFFT processing, precoding, and digital-to-analog conversion on the bit string to be transmitted, and output a baseband signal.
  • Whether or not to apply DFT processing may be based on the settings of transform precoding.
  • the transceiver unit 220 transmission processing unit 2211
  • the transceiver unit 220 may perform DFT processing as the above-mentioned transmission processing in order to transmit the channel using a DFT-s-OFDM waveform, and when transform precoding is not enabled, it is not necessary to perform DFT processing as the above-mentioned transmission processing.
  • the transceiver unit 220 may perform modulation, filtering, amplification, etc., on the baseband signal to a radio frequency band, and transmit the radio frequency band signal via the transceiver antenna 230.
  • the transceiver unit 220 may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency band signal received by the transceiver antenna 230.
  • the transceiver 220 may apply reception processing such as analog-to-digital conversion, FFT processing, IDFT processing (if necessary), filtering, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal to acquire user data, etc.
  • reception processing such as analog-to-digital conversion, FFT processing, IDFT processing (if necessary), filtering, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal to acquire user data, etc.
  • the transceiver 220 may perform measurements on the received signal. For example, the measurement unit 223 may perform RRM measurements, CSI measurements, etc. based on the received signal.
  • the measurement unit 223 may measure received power (e.g., RSRP), received quality (e.g., RSRQ, SINR, SNR), signal strength (e.g., RSSI), propagation path information (e.g., CSI), etc.
  • the measurement results may be output to the control unit 210.
  • the measurement unit 223 may derive channel measurements for CSI calculation based on channel measurement resources.
  • the channel measurement resources may be, for example, non-zero power (NZP) CSI-RS resources.
  • the measurement unit 223 may derive interference measurements for CSI calculation based on interference measurement resources.
  • the interference measurement resources may be at least one of NZP CSI-RS resources for interference measurement, CSI-Interference Measurement (IM) resources, etc.
  • CSI-IM may be called CSI-Interference Management (IM) or may be interchangeably read as Zero Power (ZP) CSI-RS.
  • the transmitting unit and receiving unit of the user terminal 20 in this disclosure may be configured by at least one of the transmitting/receiving unit 220 and the transmitting/receiving antenna 230.
  • the transceiver 220 may transmit information about a data set that can be transmitted to the network.
  • the transceiver 220 may receive a data set request that is transmitted based on the information about the data set.
  • the control unit 210 may control the transfer of the data set based on the data set request (first, second, and third embodiments).
  • the information about the dataset may be dataset-specific support information (first embodiment).
  • the transceiver unit 220 may transmit a response signal to the data set request (second embodiment).
  • the transceiver 220 may transmit a signal instructing the end of the transfer of the data set, or may receive a signal instructing the end of the transfer of the data set (third embodiment).
  • the transceiver 220 may receive information about a dataset that can be transmitted by the network.
  • the transceiver 220 may transmit a dataset request based on the information about the dataset.
  • the control unit 210 may control the reception of the dataset transferred based on the dataset request (first/second/third embodiment).
  • the information about the dataset may be dataset-specific support information or pre-configured dataset requirements (first embodiment).
  • the transceiver unit 220 may receive a response signal to the data set request (second embodiment).
  • the transceiver 220 may transmit a signal instructing the end of the transfer of the data set, or may receive a signal instructing the end of the transfer of the data set (third embodiment).
  • the transceiver unit 220 may receive a data set information setting including information indicating characteristics of the data sample, and a setting for detecting characteristics of specific information.
  • the control unit 210 may detect the specific information based on the setting, or may perform a specific model operation based on the detection of the specific information (fourth embodiment).
  • the information indicating the characteristics of the data sample may be associated with at least one of a specific feature and a specific label (fourth embodiment).
  • the specific information may be a monitored feature (fourth embodiment).
  • the control unit 210 may report the characteristics of the specific information (fourth embodiment).
  • each functional block may be realized using one device that is physically or logically coupled, or may be realized using two or more devices that are physically or logically separated and directly or indirectly connected (for example, using wires, wirelessly, etc.).
  • the functional blocks may be realized by combining the one device or the multiple devices with software.
  • the functions include, but are not limited to, judgement, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, deeming, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment.
  • a functional block (component) that performs the transmission function may be called a transmitting unit, a transmitter, and the like. In either case, as mentioned above, there are no particular limitations on the method of realization.
  • a base station, a user terminal, etc. in one embodiment of the present disclosure may function as a computer that performs processing of the wireless communication method of the present disclosure.
  • FIG. 16 is a diagram showing an example of the hardware configuration of a base station and a user terminal according to one embodiment.
  • the above-mentioned base station 10 and user terminal 20 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 terms apparatus, circuit, device, section, unit, etc. may be interpreted as interchangeable.
  • the hardware configuration of the base station 10 and the user terminal 20 may be configured to include one or more of the devices shown in the figures, or may be configured to exclude some of the devices.
  • processor 1001 may be implemented by one or more chips.
  • the functions of the base station 10 and the user terminal 20 are realized, for example, by loading specific software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communications via the communication device 1004, and control at least one of the reading and writing of data in the memory 1002 and storage 1003.
  • the processor 1001 for example, runs an operating system to control the entire computer.
  • the processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, registers, etc.
  • CPU central processing unit
  • control unit 110 210
  • transmission/reception unit 120 220
  • etc. may be realized by the processor 1001.
  • the processor 1001 also reads out programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to these.
  • the programs used are those that cause a computer to execute at least some of the operations described in the above embodiments.
  • the control unit 110 (210) may be realized by a control program stored in the memory 1002 and running on the processor 1001, and similar implementations may be made for other functional blocks.
  • Memory 1002 is a computer-readable recording medium and may be composed of at least one of, for example, Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically EPROM (EEPROM), Random Access Memory (RAM), and other suitable storage media. Memory 1002 may also be called a register, cache, main memory, etc. Memory 1002 can store executable programs (program codes), software modules, etc. for implementing a wireless communication method according to one embodiment of the present disclosure.
  • ROM Read Only Memory
  • EPROM Erasable Programmable ROM
  • EEPROM Electrically EPROM
  • RAM Random Access Memory
  • Memory 1002 may also be called a register, cache, main memory, etc.
  • Memory 1002 can store executable programs (program codes), software modules, etc. for implementing a wireless communication method according to one embodiment of the present disclosure.
  • Storage 1003 is a computer-readable recording medium and may be composed of at least one of a flexible disk, a floppy disk, a magneto-optical disk (e.g., a compact disk (Compact Disc ROM (CD-ROM)), a digital versatile disk, a Blu-ray disk), a removable disk, a hard disk drive, a smart card, a flash memory device (e.g., a card, a stick, a key drive), a magnetic stripe, a database, a server, or other suitable storage medium.
  • Storage 1003 may also be referred to as an auxiliary storage device.
  • the communication device 1004 is hardware (transmitting/receiving device) for communicating between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module.
  • the communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., to realize at least one of Frequency Division Duplex (FDD) and Time Division Duplex (TDD).
  • FDD Frequency Division Duplex
  • TDD Time Division Duplex
  • the above-mentioned transmitting/receiving unit 120 (220), transmitting/receiving antenna 130 (230), etc. may be realized by the communication device 1004.
  • the transmitting/receiving unit 120 (220) may be implemented as a transmitting unit 120a (220a) and a receiving unit 120b (220b) that are physically or logically separated.
  • the input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside.
  • the output device 1006 is an output device (e.g., a display, a speaker, a Light Emitting Diode (LED) lamp, etc.) that outputs to the outside.
  • the input device 1005 and the output device 1006 may be integrated into one structure (e.g., a touch panel).
  • each device such as the processor 1001 and 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 between each device.
  • the base station 10 and the user terminal 20 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized using the hardware.
  • the processor 1001 may be implemented using at least one of these pieces of hardware.
  • a channel, a symbol, and a signal may be read as mutually interchangeable.
  • a signal may also be a message.
  • a reference signal may be abbreviated as RS, and may be called a pilot, a pilot signal, or the like depending on the applied standard.
  • a component carrier may also 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 (e.g., 1 ms) that is independent of numerology.
  • the numerology may be a communication parameter that is applied to at least one of the transmission and reception of a signal or channel.
  • the numerology may indicate, for example, at least one of the following: SubCarrier Spacing (SCS), bandwidth, symbol length, cyclic prefix length, Transmission Time Interval (TTI), number of symbols per TTI, radio frame configuration, 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.
  • SCS SubCarrier Spacing
  • TTI Transmission Time Interval
  • radio frame configuration 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 consist of one or more symbols in the time domain (such as Orthogonal Frequency Division Multiplexing (OFDM) symbols, Single Carrier Frequency Division Multiple Access (SC-FDMA) symbols, etc.).
  • OFDM Orthogonal Frequency Division Multiplexing
  • SC-FDMA Single Carrier Frequency Division Multiple Access
  • a slot may also be a time unit based on numerology.
  • a slot may include multiple minislots. Each minislot may consist of one or multiple symbols in the time domain. A minislot may also be called a subslot. A minislot may consist of fewer symbols than a slot.
  • a PDSCH (or PUSCH) transmitted in a time unit larger than a minislot may be called PDSCH (PUSCH) mapping type A.
  • a PDSCH (or PUSCH) transmitted using a minislot may be called PDSCH (PUSCH) mapping type B.
  • a radio frame, a subframe, a slot, a minislot, and a symbol all represent time units when transmitting a signal.
  • a different name may be used for a radio frame, a subframe, a slot, a minislot, and a symbol, respectively.
  • the time units such as a frame, a subframe, a slot, a minislot, and a symbol in this disclosure may be read as interchangeable.
  • one subframe may be called a TTI
  • multiple consecutive subframes may be called a TTI
  • one slot or one minislot may be called a TTI.
  • at least one of the subframe and the TTI may be a subframe (1 ms) in existing LTE, a period shorter than 1 ms (e.g., 1-13 symbols), or a period longer than 1 ms.
  • the unit representing the TTI may be called a slot, minislot, etc., instead of a subframe.
  • TTI refers to, for example, the smallest time unit for scheduling in wireless communication.
  • a base station schedules each user terminal by allocating radio resources (such as frequency bandwidth and transmission power that can be used by each user terminal) in TTI units.
  • radio resources such as frequency bandwidth and transmission power that can be used by each user terminal
  • the TTI may be a transmission time unit for a channel-coded data packet (transport block), a code block, a code word, etc., or may be a processing unit for scheduling, link adaptation, etc.
  • the time interval e.g., the number of symbols
  • the time interval in which a transport block, a code block, a code word, etc. is actually mapped may be shorter than the TTI.
  • one or more TTIs may be the minimum time unit of scheduling.
  • the number of slots (minislots) that constitute the minimum time unit of 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.
  • a TTI shorter than a normal TTI may be called a shortened TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, minislot, subslot, slot, etc.
  • a long TTI (e.g., a normal TTI, a subframe, etc.) may be interpreted as a TTI having a time length of more than 1 ms
  • a short TTI e.g., a shortened TTI, etc.
  • TTI length shorter than the TTI length of a long TTI and equal to or greater than 1 ms.
  • a resource block is a resource allocation unit in the time domain and frequency domain, and may include one or more consecutive subcarriers in the frequency domain.
  • the number of subcarriers included in an RB may be the same regardless of numerology, and may be, for example, 12.
  • 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 be one slot, one minislot, one subframe, or one TTI in length.
  • One TTI, one subframe, etc. may each be composed of one or more resource blocks.
  • one or more RBs may be referred to as a physical resource block (Physical RB (PRB)), a sub-carrier group (Sub-Carrier Group (SCG)), a resource element group (Resource Element Group (REG)), a PRB pair, an RB pair, etc.
  • PRB Physical RB
  • SCG sub-carrier Group
  • REG resource element group
  • PRB pair an RB pair, etc.
  • a resource block may be composed of one or more resource elements (REs).
  • REs resource elements
  • one RE may be a radio resource area of one subcarrier and one symbol.
  • a Bandwidth Part which may also be referred to as a partial bandwidth, may represent a subset of contiguous common resource blocks (RBs) for a given numerology on a given carrier, where the common RBs may be identified by an index of the RB relative to a common reference point of the carrier.
  • PRBs may be defined in a BWP and numbered within the BWP.
  • the BWP may include a UL BWP (BWP for UL) and a DL BWP (BWP for DL).
  • BWP UL BWP
  • BWP for DL DL BWP
  • One or more BWPs may be configured for a UE within one carrier.
  • 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 the active BWP.
  • BWP bitmap
  • radio frames, subframes, slots, minislots, and symbols 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 subcarriers included in an RB, as well as the number of symbols in a TTI, the symbol length, and the cyclic prefix (CP) length can be changed in various ways.
  • the information, parameters, etc. described in this disclosure may be represented using absolute values, may be represented using relative values from a predetermined value, or may be represented using other corresponding information.
  • a radio resource may be indicated by a predetermined index.
  • the names used for parameters and the like in this disclosure are not limiting in any respect. Furthermore, the formulas and the like using these parameters may differ from those explicitly disclosed in this disclosure.
  • the various channels (PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not limiting in any respect.
  • the information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies.
  • the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.
  • information, signals, etc. may be output from a higher layer to a lower layer and/or from a lower layer to a higher layer.
  • Information, signals, etc. may be input/output via multiple network nodes.
  • Input/output information, signals, etc. may be stored in a specific location (e.g., memory) or may be managed using a management table. Input/output information, signals, etc. may be overwritten, updated, or added to. Output information, signals, etc. may be deleted. Input information, signals, etc. may be transmitted to another device.
  • a specific location e.g., memory
  • Input/output information, signals, etc. may be overwritten, updated, or added to.
  • Output information, signals, etc. may be deleted.
  • Input information, signals, etc. may be transmitted to another device.
  • the 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 performed by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), higher 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 of these.
  • DCI Downlink Control Information
  • UCI Uplink Control Information
  • RRC Radio Resource Control
  • MIB Master Information Block
  • SIB System Information Block
  • MAC Medium Access Control
  • the physical layer signaling may be called Layer 1/Layer 2 (L1/L2) control information (L1/L2 control signal), L1 control information (L1 control signal), etc.
  • the RRC signaling may be called an RRC message, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, etc.
  • the MAC signaling may be notified, for example, using a MAC Control Element (CE).
  • CE MAC Control Element
  • notification of specified information is not limited to explicit notification, but may be implicit (e.g., by not notifying the specified information or by notifying other information).
  • the determination may be based on a value represented by a single bit (0 or 1), a Boolean value represented by true or false, or a comparison of numerical values (e.g., with a predetermined value).
  • Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.
  • Software, instructions, information, etc. may also be transmitted and received via a transmission medium.
  • a transmission medium For example, if the software is transmitted from a website, server, or other remote source using at least one of wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and/or wireless technologies (such as infrared, microwave, etc.), then at least one of these wired and wireless technologies is included within the definition of a transmission medium.
  • wired technologies such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)
  • wireless technologies such as infrared, microwave, etc.
  • Network may refer to the devices included in the network (e.g., base stations).
  • the antenna port may be interchangeably read as an antenna port for any signal/channel (e.g., a demodulation reference signal (DMRS) port).
  • the resource may be interchangeably read as a resource for any signal/channel (e.g., a reference signal resource, an SRS resource, etc.).
  • the resource may include time/frequency/space/power resources.
  • the spatial domain transmission filter may include at least one of a spatial domain transmission filter and a spatial domain reception filter.
  • the above groups may include, for example, at least one of a spatial relationship group, a Code Division Multiplexing (CDM) group, a Reference Signal (RS) group, a Control Resource Set (CORESET) group, a PUCCH group, an antenna port group (e.g., a DMRS port group), a layer group, a resource group, a beam group, an antenna group, a panel group, etc.
  • CDM Code Division Multiplexing
  • RS Reference Signal
  • CORESET Control Resource Set
  • beam SRS Resource Indicator (SRI), CORESET, CORESET pool, PDSCH, PUSCH, codeword (CW), transport block (TB), RS, etc. may be read as interchangeable.
  • SRI SRS Resource Indicator
  • CORESET CORESET pool
  • PDSCH PUSCH
  • codeword CW
  • TB transport block
  • RS etc.
  • TCI state downlink TCI state
  • DL TCI state downlink TCI state
  • UL TCI state uplink TCI state
  • unified TCI state common TCI state
  • joint TCI state etc.
  • QCL QCL
  • QCL assumptions QCL relationship
  • QCL type information QCL property/properties
  • specific QCL type e.g., Type A, Type D
  • specific QCL type e.g., Type A, Type D
  • index identifier
  • indicator indication, resource ID, etc.
  • sequence list, set, group, cluster, subset, etc.
  • TCI state ID the spatial relationship information identifier
  • TCI state ID the spatial relationship information
  • TCI state the spatial relationship information
  • TCI state the spatial relationship information
  • TCI state the spatial relationship information
  • Base Station may also be referred to by terms such as macrocell, small cell, femtocell, picocell, etc.
  • a base station can accommodate one or more (e.g., three) cells.
  • a base station accommodates multiple cells, the entire coverage area of the base station can be divided into multiple smaller areas, and each smaller area can also provide communication services by a base station subsystem (e.g., a small base station for indoor use (Remote Radio Head (RRH))).
  • RRH Remote Radio Head
  • the term "cell” or “sector” refers to a part or the entire coverage area of at least one of the base station and base station subsystems that provide communication services in this coverage.
  • a base station transmitting information to a terminal may be interpreted as the base station instructing the terminal to control/operate based on the information.
  • MS Mobile Station
  • UE User Equipment
  • a mobile station may also be referred to as 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 the base station and the mobile station may be called a transmitting device, a receiving device, a wireless communication device, etc.
  • at least one of the base station and the mobile station may be a device mounted on a moving object, the moving object itself, etc.
  • the moving body in question refers to an object that can move, and the moving speed is arbitrary, and of course includes the case where the moving body is stationary.
  • the moving body in question includes, but is not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcarts, rickshaws, ships and other watercraft, airplanes, rockets, artificial satellites, drones, multicopters, quadcopters, balloons, and objects mounted on these.
  • the moving body in question may also be a moving body that moves autonomously based on an operating command.
  • the moving object may be a vehicle (e.g., a car, an airplane, etc.), an unmanned moving object (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned).
  • a vehicle e.g., a car, an airplane, etc.
  • an unmanned moving object e.g., a drone, an autonomous vehicle, etc.
  • a robot manned or unmanned
  • at least one of the base station and the mobile station may also include 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. 17 is a diagram showing 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 (including a current sensor 50, 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 unit 59, and a communication module 60.
  • various sensors including a current sensor 50, 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 unit 59 including a communication module 60.
  • the drive unit 41 is composed of at least one of an engine, a motor, and a hybrid of an engine and a motor, for example.
  • the steering unit 42 includes at least a steering wheel (also called a handlebar), 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 is composed of a microprocessor 61, memory (ROM, RAM) 62, and a communication port (e.g., an Input/Output (IO) port) 63. Signals are input to the electronic control unit 49 from various sensors 50-58 provided in the vehicle.
  • the electronic control unit 49 may also be called an Electronic Control Unit (ECU).
  • ECU Electronic Control Unit
  • Signals from the various sensors 50-58 include a current signal from a current sensor 50 that senses the motor current, a rotation speed signal of the front wheels 46/rear wheels 47 acquired by a rotation speed sensor 51, an air pressure signal of the front wheels 46/rear wheels 47 acquired by an air pressure sensor 52, a vehicle speed signal acquired by a vehicle speed sensor 53, an acceleration signal acquired by an acceleration sensor 54, a depression amount signal of the accelerator pedal 43 acquired by an accelerator pedal sensor 55, a depression amount signal of the brake pedal 44 acquired by a brake pedal sensor 56, an operation signal of the shift lever 45 acquired by a shift lever sensor 57, and a detection signal for detecting obstacles, vehicles, pedestrians, etc. acquired by an object detection sensor 58.
  • the information service unit 59 is composed of various devices, such as a car navigation system, audio system, speakers, displays, televisions, and radios, for providing (outputting) various information such as driving information, traffic information, and entertainment information, and one or more ECUs that control these devices.
  • the information service unit 59 uses information acquired from external devices via the communication module 60, etc., to provide various information/services (e.g., multimedia information/multimedia services) to the occupants of the vehicle 40.
  • various information/services e.g., multimedia information/multimedia services
  • the information service unit 59 may include input devices (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, a touch panel, etc.) that accept input from the outside, and may also include output devices (e.g., a display, a speaker, an LED lamp, a touch panel, etc.) that perform output to the outside.
  • input devices e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, a touch panel, etc.
  • output devices e.g., a display, a speaker, an LED lamp, a touch panel, etc.
  • the driving assistance system unit 64 is composed of various devices that provide functions for preventing accidents and reducing the driver's driving load, such as a millimeter wave radar, a Light Detection and Ranging (LiDAR), a camera, a positioning locator (e.g., a Global Navigation Satellite System (GNSS)), map information (e.g., a High Definition (HD) map, an Autonomous Vehicle (AV) map, etc.), a gyro system (e.g., an Inertial Measurement Unit (IMU), an Inertial Navigation System (INS), etc.), an Artificial Intelligence (AI) chip, and an AI processor, and one or more ECUs that control these devices.
  • the driving assistance system unit 64 also transmits and receives various information via the communication module 60 to realize a driving assistance function or an autonomous driving function.
  • 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 transmits and receives data (information) via the communication port 63 between the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, the microprocessor 61 and memory (ROM, RAM) 62 in the electronic control unit 49, and the various sensors 50-58 that are provided on the vehicle 40.
  • 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 an external device. For example, it transmits and receives various information to and from the 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 above-mentioned base station 10 or user terminal 20.
  • the communication module 60 may also be, for example, at least one of the above-mentioned base station 10 and user terminal 20 (it may function as at least one of the base station 10 and user terminal 20).
  • the communication module 60 may transmit at least one of the signals from the various sensors 50-58 described above input to the electronic control unit 49, information obtained based on the signals, and information based on input from the outside (user) obtained via the information service unit 59 to an external device via wireless communication.
  • the electronic control unit 49, the various sensors 50-58, the information service unit 59, etc. may be referred to as input units that accept 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, vehicle distance information, etc.) transmitted from an external device and displays it on an information service unit 59 provided in the vehicle.
  • the information service unit 59 may also be called an output unit that outputs information (for example, outputs information to a device such as a display or speaker based on the PDSCH (or data/information decoded from the PDSCH) received by the communication module 60).
  • the communication module 60 also stores various information received from external devices in memory 62 that can be used by the microprocessor 61. Based on the information stored in memory 62, the microprocessor 61 may control the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, various sensors 50-58, and the like provided on the vehicle 40.
  • the base station in the present disclosure may be read as a user terminal.
  • each aspect/embodiment of the present disclosure may be applied to a configuration in which communication between a base station and a user terminal is replaced with communication between multiple user terminals (which may be called, for example, Device-to-Device (D2D), Vehicle-to-Everything (V2X), etc.).
  • the user terminal 20 may be configured to have the functions of the base station 10 described above.
  • terms such as "uplink” and "downlink” may be read as terms corresponding to terminal-to-terminal communication (for example, "sidelink").
  • the uplink channel, downlink channel, etc. may be read as the sidelink channel.
  • the user terminal in this disclosure may be interpreted as a base station.
  • the base station 10 may be configured to have the functions of the user terminal 20 described above.
  • operations that are described as being performed by a base station may in some cases be performed by its upper node.
  • a network that includes one or more network nodes having base stations, it is clear that various operations performed for communication with terminals may be performed by the base station, one or more network nodes other than the base station (such as, but not limited to, a Mobility Management Entity (MME) or a Serving-Gateway (S-GW)), or a combination of these.
  • MME Mobility Management Entity
  • S-GW Serving-Gateway
  • each aspect/embodiment described in this disclosure may be used alone, in combination, or switched between depending on the implementation.
  • the processing procedures, sequences, flow charts, etc. of each aspect/embodiment described in this disclosure may be rearranged as long as there is no inconsistency.
  • the methods described in this disclosure present elements of various steps using an exemplary order, 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
  • 4th generation mobile communication system 4th generation mobile communication system
  • 5G 5th generation mobile communication system
  • 6G 6th generation mobile communication system
  • xG x is, for example, an integer or decimal
  • Future Radio Access FX
  • GSM Global System for Mobile communications
  • CDMA2000 Code Division Multiple Access
  • UMB Ultra Mobile Broadband
  • IEEE 802.11 Wi-Fi
  • IEEE 802.16 WiMAX (registered trademark)
  • IEEE 802.20 Ultra-WideBand (UWB), Bluetooth (registered trademark), and other appropriate wireless communication methods, as well as next-generation systems that are expanded, modified,
  • the phrase “based on” does not mean “based only on,” unless expressly stated otherwise. In other words, the phrase “based on” means both “based only on” and “based at least on.”
  • any reference to elements using designations such as “first,” “second,” etc., used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a 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 some way.
  • determining may encompass a wide variety of actions. For example, “determining” may be considered to be judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry (e.g., looking in a table, database, or other data structure), ascertaining, etc.
  • Determining may also be considered to mean “determining” receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in a memory), etc.
  • judgment (decision) may be considered to mean “judging (deciding)” resolving, selecting, choosing, establishing, comparing, etc.
  • judgment (decision) may be considered to mean “judging (deciding)” some kind of action.
  • judgment (decision) may be interpreted interchangeably with the actions described above.
  • expect may be read as “be expected”.
  • "expect(s)" ("" may be expressed, for example, as a that clause, a to infinitive, etc.) may be read as “be expected".
  • "does not expect" may be read as "be not expected".
  • "An apparatus A is not expected" may be read as "An apparatus B other than apparatus A does not expect" (for example, if apparatus A is a UE, apparatus B may be a base station).
  • the "maximum transmit power" referred to in this disclosure may mean the maximum value of transmit power, may mean the nominal UE maximum transmit power, or may mean the rated UE maximum transmit power.
  • connection and “coupled,” or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” to each other.
  • the coupling or connection between the elements may be physical, logical, or a combination thereof. For example, "connected” may be read as "accessed.”
  • a and B are different may mean “A and B are different from each other.”
  • the term may also mean “A and B are each different from C.”
  • Terms such as “separate” and “combined” may also be interpreted in the same way as “different.”
  • timing, time, duration, time instance, any time unit e.g., slot, subslot, symbol, subframe
  • period occasion, resource, etc.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Telephonic Communication Services (AREA)
  • Selective Calling Equipment (AREA)

Abstract

Selon un aspect de la présente divulgation, un terminal comprend : une unité de réception qui reçoit des informations relatives à un ensemble de données pouvant être transmis par un réseau ; une unité de transmission qui transmet une demande d'ensemble de données d'après les informations relatives à l'ensemble de données ; et une unité de commande qui commande la réception de l'ensemble de données transféré d'après la demande d'ensemble de données. Selon un aspect de la présente divulgation, la collecte des données et la surveillance du modèle peuvent être effectuées de manière appropriée.
PCT/JP2023/000878 2023-01-13 2023-01-13 Terminal, procédé de communication sans fil et station de base Ceased WO2024150435A1 (fr)

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019522912A (ja) * 2016-06-24 2019-08-15 グァンドン オッポ モバイル テレコミュニケーションズ コーポレーション リミテッドＧｕａｎｇｄｏｎｇ Ｏｐｐｏ Ｍｏｂｉｌｅ Ｔｅｌｅｃｏｍｍｕｎｉｃａｔｉｏｎｓ Ｃｏｒｐ．， Ｌｔｄ． 情報伝送方法及び装置

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019522912A (ja) * 2016-06-24 2019-08-15 グァンドン オッポ モバイル テレコミュニケーションズ コーポレーション リミテッドＧｕａｎｇｄｏｎｇ Ｏｐｐｏ Ｍｏｂｉｌｅ Ｔｅｌｅｃｏｍｍｕｎｉｃａｔｉｏｎｓ Ｃｏｒｐ．， Ｌｔｄ． 情報伝送方法及び装置

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