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WO2025069421A1 - 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
WO2025069421A1
WO2025069421A1 PCT/JP2023/035756 JP2023035756W WO2025069421A1 WO 2025069421 A1 WO2025069421 A1 WO 2025069421A1 JP 2023035756 W JP2023035756 W JP 2023035756W WO 2025069421 A1 WO2025069421 A1 WO 2025069421A1
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Prior art keywords
csi
information
model
disclosure
resource
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English (en)
Japanese (ja)
Inventor
春陽 越後
浩樹 原田
聡 永田
シン ワン
リュー リュー
チーピン ピ
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NTT Docomo Inc
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NTT Docomo Inc
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Priority to PCT/JP2023/035756 priority Critical patent/WO2025069421A1/fr
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/20—Control channels or signalling for resource management
    • H04W72/21—Control channels or signalling for resource management in the uplink direction of a wireless link, i.e. towards the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/20—Control channels or signalling for resource management
    • H04W72/23—Control channels or signalling for resource management in the downlink direction of a wireless link, i.e. towards a terminal

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
  • AI artificial intelligence
  • ML machine learning
  • DL beam prediction Spatial domain downlink (DL) beam prediction, temporal DL beam prediction, positioning, etc. are being considered as use cases for utilizing AI models.
  • beam prediction methods may be called AI-based beam prediction (beam reporting), AI-based positioning, AI-based beam management (BM), etc.
  • Temporal DL beam prediction may be called, for example, time domain Channel State Information (CSI) prediction.
  • CSI Channel State Information
  • CSI Channel State Information
  • the performance monitoring of the AI model may be performed on the terminal side (terminal, user terminal, User Equipment (UE)) or on the network (NW, for example, a base station (Base Station (BS))).
  • UE User Equipment
  • NW for example, a base station (Base Station (BS))
  • one of the objectives of this disclosure is to provide a terminal, a wireless communication method, and a base station that can achieve optimal overhead reduction/channel estimation/resource utilization.
  • a terminal has a control unit that controls the transmission of requests for network-side performance monitoring regarding artificial intelligence (AI)-based channel state information (CSI) reporting, and a receiving unit that receives instructions regarding the CSI reporting transmitted based on the settings.
  • AI artificial intelligence
  • CSI channel state information
  • FIG. 1 is a diagram illustrating an example of a framework for managing AI models.
  • FIG. 2 is a diagram showing an example of specifying an AI model.
  • FIG. 3 is a diagram showing an example of CSI feedback using an encoder/decoder.
  • FIG. 4 illustrates an example life cycle management framework for performance monitoring in a UE according to an embodiment.
  • FIG. 5 illustrates an example life cycle management framework for performance monitoring in a BS according to one embodiment.
  • 6A and 6B are diagrams showing an example of an AI-based beam report.
  • FIG. 7 illustrates an example of performance monitoring of CSI compression at the UE side.
  • FIG. 8 is a diagram showing an example of CSI reconstruction using a proxy model.
  • FIG. 9A and 9B are diagrams illustrating an example of NW-side monitoring and UE-side monitoring, respectively.
  • FIG. 10 is a diagram illustrating an example of a monitoring method relating to a combination of UE side monitoring and NW side monitoring.
  • FIG. 11 is a diagram showing an example of generation of CSI elements related to option 4-4.
  • FIG. 12 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment.
  • FIG. 13 is a diagram illustrating an example of the configuration of a base station according to an embodiment.
  • FIG. 14 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment.
  • FIG. 15 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment.
  • FIG. 16 is a diagram illustrating an example of a vehicle according to an embodiment.
  • the UE generates (also called determining, calculating, estimating, measuring, etc.) CSI based on a reference signal (RS) (or a resource for the RS) and transmits (also called reporting, feedback, etc.) the generated CSI to a network (e.g., a base station).
  • RS reference signal
  • the CSI may be transmitted to the base station using, for example, an uplink control channel (e.g., a Physical Uplink Control Channel (PUCCH)) or an uplink shared channel (e.g., a Physical Uplink Shared Channel (PUSCH)).
  • PUCCH Physical Uplink Control Channel
  • PUSCH Physical Uplink Shared Channel
  • CSI includes a Channel Quality Indicator (CQI), a Precoding Matrix Indicator (PMI), a CSI-RS Resource Indicator (CRI), a SS/PBCH Block Resource Indicator (SSBRI), a Layer Indicator (LI), a Rank Indicator (RI), and a Layer 1 Reference Signal Received Power (L1-RSRP).
  • CQI Channel Quality Indicator
  • PMI Precoding Matrix Indicator
  • CRI CSI-RS Resource Indicator
  • SSBRI SS/PBCH Block Resource Indicator
  • LI Layer Indicator
  • RI Rank Indicator
  • L1-RSRP Layer 1 Reference Signal Received Power
  • L1-Reference Signal Received Power L1-RSRQ
  • L1-SINR Signal to Interference plus Noise Ratio
  • L1-SNR Signal to Noise Ratio
  • information on the channel matrix or channel coefficients
  • information on the precoding matrix or precoding coefficients
  • information on the beam/Transmission Configuration Indication state TCI state/spatial relation, etc.
  • the RS used to generate the CSI may be, for example, at least one of a Channel State Information Reference Signal (CSI-RS), a Synchronization Signal/Physical Broadcast Channel (SS/PBCH) block, a Synchronization Signal (SS), and a DeModulation Reference Signal (DMRS).
  • CSI-RS Channel State Information Reference Signal
  • SS/PBCH Synchronization Signal/Physical Broadcast Channel
  • SS Synchronization Signal
  • DMRS DeModulation Reference Signal
  • RS Non Zero Power (NZP) CSI-RS, Zero Power (ZP) CSI-RS, CSI Interference Measurement (CSI-IM), CSI-SSB, and SSB
  • NZP Non Zero Power
  • ZP Zero Power
  • CSI-IM CSI Interference Measurement
  • CSI-SSB CSI Interference Measurement
  • SSB SSB
  • CSI-RS may include other reference signals.
  • the UE may receive configuration information regarding CSI reporting (which may be referred to as CSI report configuration, report setting, etc.) and control CSI reporting based on the configuration information.
  • the report configuration information may be, for example, a Radio Resource Control (RRC) Information Element (IE) "CSI-ReportConfig.”
  • RRC Radio Resource Control
  • IE Radio Resource Control Information Element
  • the CSI reporting configuration may include at least one of the following information: Information regarding the CSI resources used for CSI measurements (resource configuration ID, for example, "CSI-ResourceConfigId”); Information regarding one or more quantities (CSI parameters) of CSI to be reported (report quantity information, e.g., "reportQuantity”); Report type information (eg, "reportConfigType”) indicating the time domain behavior of the reporting configuration.
  • resource configuration ID for example, "CSI-ResourceConfigId”
  • Information regarding one or more quantities (CSI parameters) of CSI to be reported (report quantity information, e.g., "reportQuantity”
  • Report type information eg, "reportConfigType" indicating the time domain behavior of the reporting configuration.
  • a CSI resource may be interchangeably referred to as a time instance, a CSI-RS opportunity/CSI-IM opportunity/SSB opportunity, a CSI-RS resource (one/multiple) opportunity, a CSI opportunity, an opportunity, a CSI-RS resource/CSI-IM resource/SSB resource, a time resource, a frequency resource, an antenna port (e.g., a CSI-RS port), etc.
  • the time unit of a CSI resource may be a slot, a symbol, etc.
  • the information on the CSI resources may include information on CSI resources for channel measurement, information on CSI resources for interference measurement (NZP-CSI-RS resources), information on CSI-IM resources for interference measurement, etc.
  • the reporting amount information may specify any one of the above CSI parameters (e.g., CRI, RI, PMI, CQI, LI, L1-RSRP, etc.) or a combination of these.
  • CSI parameters e.g., CRI, RI, PMI, CQI, LI, L1-RSRP, etc.
  • the report type information may indicate a periodic CSI (Periodic CSI (P-CSI)) report, an aperiodic CSI (A-CSI) report, or a semi-persistent CSI (Semi-Persistent CSI (SP-CSI)) report.
  • P-CSI Period CSI
  • A-CSI aperiodic CSI
  • SP-CSI semi-persistent CSI
  • the UE performs CSI-RS/SSB/CSI-IM measurements based on the CSI resource configuration corresponding to the CSI reporting configuration (the CSI resource configuration associated with CSI-ResourceConfigId) and derives the CSI to report based on the measurement results.
  • the CSI resource configuration (e.g., the CSI-ResourceConfig information element) may include a csi-RS-ResourceSetList field indicating more specific CSI-RS/SSB resources, resource type information (e.g., "resourceType") indicating the time domain behavior of the resource configuration, etc.
  • the resource type information may indicate a P-CSI resource, an A-CSI resource, or an SP-CSI resource.
  • 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 (of an AI model) 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, production use, actual use, etc. may be interchangeable.
  • terms such as signal and signal/channel may be interchangeable.
  • FIG. 1 shows an example of a framework for managing AI models.
  • each stage related to an AI model is shown as a block.
  • This example is also referred to as Life Cycle Management (LCM) of an AI model.
  • LCM Life Cycle Management
  • 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 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), output (providing model output to the actor), etc.
  • 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 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) by 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 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 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.
  • Figure 2 shows an example of specifying an AI model.
  • the UE and NW e.g., a base station (BS)
  • NW e.g., a base station (BS)
  • the UE may report, for example, the capabilities of model #1 and model #2 to the NW, and the NW may instruct the UE on the AI model to use.
  • AI-based CSI feedback As a use case of utilizing an AI model, CSI compression using a two-sided AI model is being considered. Such a CSI compression method may be called AI-based CSI feedback, and may be realized, for example, by using an autoencoder.
  • Figure 3 shows an example of CSI feedback using an encoder/decoder.
  • the UE transmits information (CSI feedback information) including encoded bits that are output by inputting CSI to an encoder from an antenna.
  • the BS inputs the received CSI feedback information bits to a corresponding decoder to obtain the CSI to be output.
  • the input CSI may include, for example, information on channel coefficients (elements of a channel matrix) or information on precoding coefficients (elements of a precoding matrix).
  • the CSI may correspond to information on the channel state in the space-frequency domain.
  • the input may include information other than CSI.
  • the CSI output from the decoder may be reconstructed CSI that corresponds to the input to the encoder, or it may be CSI different from the input to the encoder (e.g., if the input information is information on channel coefficients, it may be information on precoding coefficients, etc.).
  • the encoder/decoder may also include pre-processing of the input and post-processing of the output.
  • the encoded bits are more compressed than the input information before encoding, which is expected to reduce the communication overhead required for CSI feedback.
  • FIG. 4 illustrates an example of a lifecycle management framework for performance monitoring in a UE according to one embodiment.
  • the UE monitors the performance of the model and fallback scheme (non-AI based CSI feedback).
  • the UE evaluates the performance of the monitored/reported models and fallback schemes (non-AI based CSI feedback).
  • the UE reports the above monitored performance to the NW.
  • the NW evaluates the performance of the reported model and fallback scheme.
  • the UE sends a request to the NW regarding which model should be applied or whether a fallback scheme should be applied.
  • the UE may be instructed which scheme (model) is to be activated.
  • the UE may activate a model or a fallback scheme.
  • FIG. 5 illustrates an example of a life cycle management framework for performance monitoring in a BS according to one embodiment.
  • the UE reports information for performance monitoring in the NW (BS).
  • the network monitors the performance of the model and the fallback scheme (non-AI-based CSI feedback).
  • the NW evaluates the performance of the model and the fallback scheme.
  • the UE may be instructed which scheme (model) is to be activated.
  • the UE may activate a model or a fallback scheme.
  • AI-based beam report 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
  • FIGS. 6A and 6B are diagrams showing an example of an AI-based beam report.
  • FIG. 6A shows spatial domain DL beam prediction.
  • the UE may measure a spatially sparse (or thick) beam, input the measurement results, etc., into an AI model, and output a predicted result of the beam quality of a spatially dense (or thin) beam.
  • Figure 6B shows temporal DL beam prediction.
  • the UE may measure the beam over time, input the measurement results, etc., to 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/RS related to the output (prediction result) of the AI model may be referred to as set A.
  • the beams/RS related to the input of the AI model may be referred to as set B.
  • 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.
  • (Performance monitoring of CSI compression at the UE side) 7 is a diagram showing an example of performance monitoring of CSI compression at the UE side, in which the UE may monitor expected performance if an encoder is available at the UE.
  • the performance (expected performance) monitored in FIG. 7 may be at least one of the following: (1) Expected communication quality calculated based on the output of an AI model. For example, expected CQI that satisfies a certain block error probability under a specific resource allocation assumption. (2) The expected performance of the reconstructed CSI compared to the target CSI (e.g., expected noise variance).
  • the CQI in (1) may be, for example, at least one of a wideband CQI, an average of subband CQIs, a weighted average of subband CQIs, a maximum/minimum of subband CQI, etc.
  • the specific resource allocation may correspond to a frequency/time resource allocation for receiving a certain channel/signal (e.g., PDSCH, PDCCH, corresponding DMRS), and the type of resource allocation may be specified in the standard (e.g., the expected number of symbols, the number of resource blocks, etc.).
  • the certain block error probability may be, for example, at least one of 0.1, 0.00001, etc.
  • the CSI output from the decoder is the reconstructed CSI that corresponds to the input to the encoder.
  • the decoder in the UE is only provided for performance monitoring, and the CSI feedback sent by the UE is the output of the encoder.
  • the UE does not have a decoder that corresponds to the encoder.
  • the UE performs channel measurements based on the CSI-RS transmitted from the BS and obtains the channel matrix H.
  • the UE estimates its performance based on H.
  • the UE may perform a specific process on H (e.g., Singular Value Decomposition (SVD)) to obtain W.
  • H e.g., Singular Value Decomposition (SVD)
  • the UE estimates performance based on W.
  • the UE may perform the above-mentioned preprocessing on the above-mentioned W to obtain p-W.
  • the UE may estimate performance based on p-W, or may estimate performance based on W.
  • the UE may also transmit a performance report to the BS as necessary.
  • the UE may receive information on the expected performance of the AI model corresponding to the encoder's AI model from the vendor's data server or NW.
  • the information may be included in the AI model information.
  • the data server may be interchangeably referred to as a repository, an uploader, a library, a cloud server, or simply a server.
  • the data server in this disclosure may be provided by any platform such as GitHub (registered trademark), and may be operated by any company/organization.
  • the UE performs channel measurement based on the CSI-RS transmitted from the BS, and obtains the H/W/p-W corresponding to the target CSI.
  • the UE also calculates (estimates) the expected performance based on the target CSI and the above-mentioned expected performance information. If performance monitoring is the only task, the UE does not need to operate the encoder.
  • the UE can use a proxy model to calculate the expected reconstructed CSI instead of the reconstruction model actually used by the base station.
  • the proxy model is a model that mimics the reconstruction model used by the base station.
  • the proxy model can be a simple model. This can reduce the processing and storage problems of the UE.
  • the proxy model can be different from the actual reconstruction model in the base station. This can avoid the uniqueness problem.
  • Figure 8 shows an example of CSI reconstruction (pseudo reconstruction) using a proxy model.
  • the UE receives a proxy model for decoding from the NW (base station).
  • the UE uses the proxy model to reconstruct the encoded CSI and outputs it as an estimated CSI.
  • the UE maps the estimated result to the actual CSI and calculates a KPI (Key Performance Indicator) (e.g., SGCS (squared generalized cosine similarity)).
  • KPI Key Performance Indicator
  • SGCS squared generalized cosine similarity
  • Performance monitoring of AI/ML CSI feedback includes NW side monitoring and UE side monitoring.
  • the network side monitoring may be based on ground-truth feedback and channel estimation using a UL reference signal (e.g., SRS).
  • a UL reference signal e.g., SRS
  • FIG. 9A is a diagram showing an example of network side monitoring.
  • the UE measures the RS resource for input, and then generates AI/ML CSI depending on whether the matrix to be acquired is H or W. Also, the UE measures the RS resource for input/reference, and then transmits ground-truth feedback/SRS depending on whether the matrix to be acquired is H or W.
  • AI/ML CSI reconstruction is performed in the network, and based on (comparing) the CSI reconstruction and the ground-truth feedback/SRS, the Normalized Mean Square Error (NMSE)/Squared Generalized Cosine Similarity (SGCS) are calculated as KPIs.
  • NMSE Normalized Mean Square Error
  • SGCS Generalized Cosine Similarity
  • UE side monitoring may be monitoring based on a proxy CSI reconstruction model.
  • Figure 9B is a diagram showing an example of UE-side monitoring.
  • the UE measures the RS resource for input, then generates AI/ML CSI depending on whether the matrix to be acquired is H or W, and performs proxy AI/ML CSI reconfiguration based on the obtained bit stream.
  • the UE also measures the RS resource for input/reference.
  • the UE reports CSI based on the CSI generation to the NW, and NMSE/SGCS is calculated based on (comparing) the CSI reconfiguration and the input/reference RS resource measurement.
  • the UE reports monitoring based on the calculated NMSE/SGCS.
  • the CSI report and the monitoring report are associated.
  • the UE evaluates the calculated NMSE/SGCS.
  • the NW performs AI/ML CSI reconfiguration based on the CSI report.
  • intermediate KPIs such as SGCS, NMSE, and Recall at Rank (RAR) may be reused.
  • the network may have sufficient capability (computational power) to accurately monitor multiple models, but may lack target CSI data for monitoring.
  • the UE may have target CSI data, but may not have sufficient capability to accurately monitor multiple models.
  • This method may, for example, monitor only the active model in the UE (first monitoring, which may be called coarse monitoring), and monitor multiple models in the network when a specific event is triggered (second monitoring, which may be called fine monitoring).
  • This method can reduce the overhead for ground-truth feedback by the UE and utilize the computational power of the network for accurate model monitoring.
  • the inventors therefore came up with a way to solve these problems.
  • 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
  • index identifier
  • indicator indicator
  • resource ID etc.
  • sequence list, set, group, cluster, subset, etc.
  • TRP
  • CSI-RS Non-Zero Power (NZP) CSI-RS, Zero Power (ZP) CSI-RS, and CSI Interference Measurement (CSI-IM) may be interchangeable.
  • CSI-RS may include other reference signals.
  • the measured/reported RS may refer to the RS measured/reported for CSI reporting.
  • timing, time, duration, slot, subslot, symbol, subframe, etc. may be interpreted as interchangeable.
  • direction, axis, dimension, domain, polarization, polarization component, etc. may be interpreted as interchangeable.
  • estimation, prediction, and inference may be interpreted as interchangeable. Also, in this disclosure, estimate, predict, and infer may be interpreted as interchangeable.
  • the autoencoder, encoder, decoder, etc. may be interpreted as at least one of a model, an ML model, a neural network model, an AI model, an AI algorithm, etc.
  • the autoencoder may be interpreted as any autoencoder, such as a stacked autoencoder or a convolutional autoencoder.
  • the encoder/decoder of the present disclosure may employ models such as Residual Network (ResNet), DenseNet, and RefineNet.
  • bits, bit strings, bit series, series, values, information, values obtained from bits, information obtained from bits, etc. may be interpreted as interchangeable.
  • a layer for an encoder
  • a layer may be interchangeably read 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.
  • RSRP may be interchangeably read as any parameter related to reception power/reception quality, etc. (e.g., RSRQ, SINR, CSI, etc.).
  • the RS may be, for example, a CSI-RS, an SS/PBCH block (SS block (SSB)), etc.
  • the RS index may be a CSI-RS resource indicator (CSI-RS Resource Indicator (CRI)), an SS/PBCH block resource indicator (SS/PBCH Block Indicator (SSBRI)), etc.
  • CSI-RS Resource Indicator CRI
  • SSBRI SS/PBCH Block Indicator
  • channel measurement/estimation may be performed using at least one of, for example, a Channel State Information Reference Signal (CSI-RS), a Synchronization Signal (SS), a Synchronization Signal/Physical Broadcast Channel (SS/PBCH) block, a DeModulation Reference Signal (DMRS), a Sounding Reference Signal (SRS), etc.
  • CSI-RS Channel State Information Reference Signal
  • SS Synchronization Signal
  • SS/PBCH Synchronization Signal/Physical Broadcast Channel
  • DMRS DeModulation Reference Signal
  • SRS Sounding Reference Signal
  • the terms receive beam assumption, number of receive beams, receive beam index, receive beam selection, receive beam setting, and receive beam instruction may be interchangeable.
  • the terms receive beam, transmit beam, DL receive beam, DL transmit beam, and transmit and receive beam pairs may be interchangeable.
  • the terms transmit/receive beam may be interchangeable with the terms transmit/receive beam for beam prediction and transmit/receive beam for CSI measurement/reporting for beam prediction.
  • functionality may refer to the use of a model or the physical meaning of the model's input/output. Multiple models may have the same functionality. Monitoring (checking performance)/activation/deactivation/switching/fallback/update may be instructed (controlled) based on the functionality (e.g., for each function).
  • a model ID may also refer to an identifier for a model (or a set of models). Multiple models may be assigned the same model ID in an actual deployment. In this case, these models may actually be different models (e.g., have different number of layers, etc.), but may be treated as the same model.
  • the use cases may include AI/ML for at least one of enhanced CSI feedback/beam management/enhanced positioning.
  • the use cases may also include other new use cases for AI/ML.
  • the model ID may be interchangeably read as a meta information (or a set of meta information) ID.
  • the meta information (or meta information ID) may be associated with information regarding the applicability of the model/functionality, the environment, the UE/gNB settings, etc.
  • functionality, function, functionality ID, model, and model ID may be interpreted interchangeably.
  • update, report, and send may be read interchangeably.
  • meta information may be interpreted as interchangeable.
  • monitor and evaluation may be interpreted interchangeably.
  • entity specific entity, UE, NW, gNB, and LMF may be read as interchangeable.
  • NW, LMF, gNB, and BS may be read as interchangeable.
  • the UE side model and UE may be interpreted as interchangeable.
  • model UE side model
  • logical model logical model
  • physical model may be interchangeable.
  • model/functionality may refer to a data-driven algorithm that applies AI/ML techniques to generate a set of outputs based on a set of inputs.
  • performance indicators and monitoring indicators may be interpreted as interchangeable.
  • association, correspondence, and mapping may be interpreted as interchangeable.
  • monitor result monitored result
  • post-monitoring result post-monitoring result
  • monitoring result may be read interchangeably.
  • the monitoring results may include information regarding at least one of the inference results, a performance index, and the content of an event occurrence based on the performance index/whether or not an event has occurred.
  • the UE may report the following information as monitoring results: Performance metrics corresponding to the monitored model/functionality.
  • Performance metrics corresponding to the monitored model/functionality.
  • An event occurs in the calculation of a performance index corresponding to a monitored model/functionality (eg, the value of a positive index is greater/less than a threshold for a certain duration).
  • an AI/ML-based CSI report may refer to a CSI report associated with at least one of a model ID and a particular functionality.
  • an AI/ML-based CSI report may be, for example, at least one of predicted CSI, compressed CSI, advanced CSI, and CSI of any type (e.g., type [x]).
  • AI/ML-based CSI reporting and CSI reporting may be read interchangeably.
  • a proxy model may refer to a model that is used only for performance monitoring and has no other uses other than performance monitoring.
  • a proxy model may refer to a model that estimates secondary information (CQI/RI, etc.) of the restored CSI.
  • AI/ML functionality may refer to functionality that is commanded by the NW or reported by the UE.
  • the functionality may be, for example, at least one of predicted CSI, compressed CSI, advanced CSI, and CSI of any type (e.g., type [x]).
  • AI/ML functionality may be interpreted interchangeably.
  • an AI/ML model an AI/ML CSI model may refer to a model/entity that is identified by a specific ID and performs a specific function (functionality).
  • report quantity information regarding report quantity, and report quantity information may be read interchangeably.
  • reporting settings may be read interchangeably.
  • report CSI report, measurement result report, monitoring report, and monitor result report may be read interchangeably.
  • historical CSI historical ground-truth (GT) CSI
  • GT ground-truth
  • GT CSI historical GT CSI
  • GT CSI historical ground-truth
  • CSI reporting CSI compression
  • CSI-RS and PDSCH/DMRS may be interpreted as interchangeable.
  • type X monitoring may refer to monitoring (results) based on precoded RS resources.
  • type Y monitoring may refer to monitoring (results) based on PDSCH/DMRS.
  • RS Type B may refer to an RS (signal/channel) associated with a measurement report/monitoring result report
  • RS Type A may refer to an RS (signal/channel) associated with a CSI report associated with a model ID or specific functionality/feature.
  • performance metrics metrics for monitoring reports, and KPIs may be interpreted interchangeably.
  • Fig. 10 is a sequence diagram between a terminal (UE) and a base station (NW) showing an overall picture of each embodiment of the present disclosure.
  • the procedure shown in Fig. 10 is merely an example, and the order of each step can be changed as appropriate as long as no contradiction occurs.
  • the network may first transmit various settings (e.g., reporting settings for CSI reporting) to the UE.
  • various settings e.g., reporting settings for CSI reporting
  • the UE may then receive the CSI-RS and perform AI/ML CSI reporting.
  • the UE may then receive each channel (e.g., PDSCH) that is transmitted based on the CSI report, etc.
  • the UE may start a first monitoring (coarse monitoring) from a specific timing.
  • the UE may store/acquire historical ground-truth CSI after being triggered to store.
  • the UE may determine whether to trigger monitoring of an event report based on at least one of the first monitoring and the storage/acquisition of historical correct CSI.
  • the UE may send an event report (e.g., a request for second monitoring) to the NE.
  • the NW may at least one of transmit configuration for historical CSI feedback and schedule historical CSI feedback based on the event report.
  • the UE may perform CSI compression on multiple historical CSIs and provide historical correct answer feedback based on the configuration/schedule from the NW.
  • the NW may perform a second monitoring based on feedback from the UE.
  • the NW may perform an operation such as model switching/fallback based on the second monitoring.
  • coherent joint transmission (CJT) codebook type 2 codebook for CJT, extended type 2 codebook for CJT, type 2 codebook for Rel. 18 CJT, typeII-CJT-r18, additional extended type 2 PS codebook for CJT, type 2 PS codebook for Rel. 18 CJT, typeII-CJT-PortSelection-r18' may be read as interchangeable.
  • each embodiment/option may be applied alone or in combination with multiple options.
  • the first embodiment relates to UE operation and configuration related to preparation of historical CSI reporting in the UE.
  • the UE may receive configuration for storing/retrieving historical GT CSI from the NW.
  • the configuration may be transmitted according to at least one of the methods described in Supplementary Note 2 below.
  • the setting may include, for example, at least one of information regarding the time/time slot for the start of historical GT CSI storage (e.g., the time window until the latest AI/ML CSI feedback), information regarding an event that triggers (starts) the storage of historical GT CSI (e.g., when the UE's monitoring result is below a certain threshold), information regarding the window length for storing historical GT CSI, and information regarding the amount of historical GT CSI to be stored.
  • information regarding the time/time slot for the start of historical GT CSI storage e.g., the time window until the latest AI/ML CSI feedback
  • information regarding an event that triggers (starts) the storage of historical GT CSI e.g., when the UE's monitoring result is below a certain threshold
  • information regarding the window length for storing historical GT CSI e.g., when the UE's monitoring result is below a certain threshold
  • information regarding the window length for storing historical GT CSI e.g., when the UE's monitoring result is below
  • the UE may store GT CSI for a configured time/amount of storage based on the configuration.
  • the UE may drop the oldest CSI it stores based on this configuration.
  • the UE may assume that CSI measured using input RS resources for AI/ML functions (e.g., reporting predicted/compressed CSI) is reported to the NW using a specific format.
  • AI/ML functions e.g., reporting predicted/compressed CSI
  • the particular format may be, for example, a format other than the AI/ML-based CSI reporting format.
  • CSI-H the CSI relating to this particular format
  • CSI-H the CSI-H
  • the UE/NW may follow options 1-1/1-2 below.
  • the UE may measure/store CSI using an input RS resource if the input RS resource is located within a particular time resource (eg, a time window).
  • the start/end timing (e.g., slot/symbol) of the time resource may be set/instructed to the UE.
  • the setting/instruction may be performed according to at least one of the methods described in Supplementary Note 2 below.
  • start/end timing e.g., slot/symbol
  • time resource e.g., time window
  • start/end timing e.g., slot/symbol
  • end timing of the time window may be the current slot.
  • Option 1-1 allows the input RS resource to be appropriately determined based on the time resource.
  • the UE may be configured/instructed as to an event for triggering CSI measurement/storage, and the UE may determine to measure/storage CSI based on the configured/instructed event.
  • the setting/instruction may be performed according to at least one of the methods described in Supplementary Note 2 below.
  • the event may be, for example, when the performance of the AI/ML model monitored by the UE falls below a certain threshold (at a particular time period/timing/instance).
  • the specific period/timing/instance/threshold may be predefined in the specifications, or may be set/indicated based on at least one of the methods described in Supplementary Note 2 below.
  • the start timing/period of the first monitoring by the UE may be specified in advance in the specifications, may be set/instructed from the network using higher layer signaling (RRC/MAC CE)/DCI, may be determined based on a report of the UE capabilities, may depend on the UE implementation, or may be determined based on a combination of at least two of these.
  • RRC/MAC CE higher layer signaling
  • the particular number of times may be one or more (e.g., N times (consecutive/non-consecutive)).
  • N may be set/indicated, for example, according to at least one of the methods described in Supplementary Note 2 below.
  • the event may also be, for example, when the UE receives a trigger signal.
  • the trigger signal may be set/indicated, for example, according to at least one of the methods described in Supplementary Note 2 below.
  • the UE may assume/judge that more than a certain number (e.g., M) of CSI-H (or equal to or greater than a certain number) will not be reported to the NW.
  • the UE may acquire/store up to M (or M-1) pieces of historical CSI. According to this method, the number of CSIs stored by the UE can be appropriately controlled.
  • the M may be set/indicated, for example, according to at least one of the methods described in Supplementary Note 2 below, or may be reported by the UE according to at least one of the methods described in Supplementary Note 3 below.
  • the UE may assume that the CSI-H reported to the NW is the CSI measured in the latest slot (time slot).
  • the second embodiment relates to configuration and UE operation regarding a request for second monitoring in the NW.
  • the UE may receive at least one of the setting and a trigger signal for the request from the NW.
  • the configuration/trigger signal may be transmitted according to at least one of the methods described in Supplementary Note 2 below.
  • the configuration may include, for example, at least one of information regarding a particular condition (e.g., at least one of thresholds for the monitored KPIs, a set amount of collected GT CSI, and a type of CSI (e.g., periodic/non-periodic/semi-persistent)), a trigger for an event report regarding the second monitoring, and information regarding resources for reporting.
  • a particular condition e.g., at least one of thresholds for the monitored KPIs, a set amount of collected GT CSI, and a type of CSI (e.g., periodic/non-periodic/semi-persistent)
  • a trigger for an event report regarding the second monitoring e.g., periodic/non-periodic/semi-persistent
  • the UE may report an event regarding the second monitoring using a specific method.
  • the UE may take the action described in Options 2-1-1/2-1-2 below based on certain conditions.
  • the specific conditions may be set, for example, according to at least one of the methods described in Supplementary Note 2 below.
  • the UE may report a specific message (which may be referred to as message A, for example) if a set condition is met.
  • the particular message may be sent, for example, according to at least one of the methods described in Supplementary Note 3 below.
  • the particular message may be, for example, a message to notify the NW that the conditions for the second monitoring have been met.
  • Option 2-1-1 allows the trigger operation related to the second monitoring to be performed appropriately.
  • the UE may report a status regarding whether the set conditions are met or not.
  • the report may be sent, for example, according to at least one of the methods described in Supplementary Note 3 below.
  • the UE may transmit information (e.g., one bit of information) indicating a binary state indicating whether second monitoring is required or not.
  • information e.g., one bit of information
  • Option 2-1-2 allows the trigger operation related to the second monitoring to be performed appropriately.
  • At least one of the specific messages in option 2-1-1 and the reports in option 2-1-2 may be reported, for example, using a dedicated field in AI/ML-based CSI feedback.
  • At least one of the specific messages in option 2-1-1 and the reports in option 2-1-2 may be transmitted using, for example, an existing (defined by Rel. 18/19/20/21) method/content combination (e.g., a CQI/RI combination, or a special value of PMI (e.g., a PMI in which all or part of the information bits are set to a specific value (e.g., 0))).
  • an existing (defined by Rel. 18/19/20/21) method/content combination e.g., a CQI/RI combination, or a special value of PMI (e.g., a PMI in which all or part of the information bits are set to a specific value (e.g., 0)).
  • At least one of the specific message in option 2-1-1 and the report in option 2-1-2 may be transmitted using, for example, a dedicated field in the monitoring report or a special value in the monitoring report.
  • the specific message in option 2-1-1 and/or the report in option 2-1-2 may be sent using resources configured/instructed according to at least one of the methods described in Supplementary Note 2 below.
  • the specific condition in this embodiment may be, for example, at least one of the following options 2-2-1 to 2-2-4.
  • a particular condition may be, for example, that a metric monitored by the UE is higher/lower (or greater than or equal to/less than) a particular threshold (at a particular time period/timing/instance).
  • the metric may be, for example, a metric related to AI/ML model performance.
  • the particular threshold may be set/indicated, for example, according to at least one of the methods described in Supplementary Note 2 below.
  • the particular condition may, for example, be that the UE fails to process (eg decode) the scheduled PDSCH a particular number of times (in a certain period of time).
  • the particular number of times may be one or more (e.g., L times (consecutive/non-consecutive)).
  • L may be set/indicated, for example, according to at least one of the methods described in Supplementary Note 2 below.
  • the particular condition may be, for example, a trigger to report CSI using a particular method (eg, periodic/aperiodic/semi-persistent).
  • the trigger may be notified according to at least one of the methods described in Supplementary Note 2 below.
  • the particular condition may be, for example, that the UE acquires/stores/possesses a particular number (eg, K) of CSI-H.
  • K may be set/indicated, for example, according to at least one of the methods described in Supplementary Note 2 below.
  • Third Embodiment A third embodiment relates to schedules and UE behavior for historical CSI reporting.
  • the UE may receive configurations/instructions from the NW regarding encoding/quantization/compression/reporting of historical GT CSI.
  • the settings/instructions may be transmitted, for example, according to at least one of the methods described in Supplementary Note 2 below.
  • the UE may report CSI-H based on specific triggers/settings/instructions.
  • the particular trigger/setting/instruction may be performed, for example, according to at least one of the methods described in Supplementary Note 2 below.
  • the particular triggers/settings/instructions may, for example, include information regarding at least one of the following: Number of CSI-Hs reported. -Reporting resources/channels. Number of CSI-H reports for each resource/channel. CSI-H quantization/compression/encoding methods and/or parameters related to said methods. - CSI Report Index/CSI Resource Index.
  • CSI-H can be reported using the appropriate number, appropriate channels/resources, appropriate quantization/compression/encoding methods, etc.
  • the UE may (e.g., by default) report all CSI-Hs (all N CSI-Hs out of N stored CSI-Hs).
  • the UE may report all CSI-H (all N CSI-Hs out of N stored CSI-Hs) when the specific trigger/setting/instruction does not include at least one of information regarding the number of CSI-Hs to be reported and information regarding the number of CSI-Hs to be reported for each resource/channel.
  • the UE may report a number of CSI-Hs based on this information when the specific trigger/setting/instruction includes at least one of information regarding the number of CSI-Hs to be reported and information regarding the number of CSI-Hs to be reported for each resource/channel.
  • the UE may report the M most recent CSI-Hs.
  • the UE may report CSI-H using a specific method (e.g. periodic/aperiodic/semi-persistent) according to the specific trigger/configuration/instruction.
  • a specific method e.g. periodic/aperiodic/semi-persistent
  • the specific trigger/setting/instruction may cause the CSI-H report to include a CSI report index.
  • the UE may then assume/expect that the corresponding CSI reporting configuration includes instructions for one or more PMI reports.
  • the UE does not need to assume/expect CSI-H to be reported at multiple times.
  • the UE may assume/expect to report CSI-H at multiple times.
  • the UE may not assume/expect a CSI-H report to be triggered.
  • a fourth embodiment relates to historical CSI report generation and UE operation.
  • the UE may encode/quantize/compress multiple GT CSIs separately/jointly using a specific method based on a specific configuration.
  • the particular method may be, for example, based on at least one of the following: the Lempel-Ziv algorithm, a particular type of extension (e.g., eType II) using a common spatial domain/frequency domain vector and derivatives.
  • a particular type of extension e.g., eType II
  • the UE may transmit coded/quantized/compressed historical GT CSI using resources configured/instructed using a specific method.
  • the UE may generate CSI (which may mean CSI content/bits/information) for CSI-H reporting configured/instructed using at least one method described in the third embodiment above.
  • CSI which may mean CSI content/bits/information
  • CSI CSI, CSI contents, CSI elements, CSI sequence, CSI bits, and CSI information bits may be interpreted as interchangeable.
  • the UE may generate CSI according to at least one of options 4-1 to 4-4 below.
  • the UE may generate each CSI-H (CSI-H content) separately by scalar quantizing each element in the CSI-H with a particular bit-width.
  • the particular bit width may be set/indicated, for example, according to at least one of the methods described in Supplementary Note 2 below.
  • the UE may generate each CSI-H (CSI-H content) separately using vector quantization.
  • the UE may generate at least one feedback content of Type II, enhanced Type II, and enhanced Type II with enhanced parameter combinations (PC) for each CSI-H separately.
  • PC enhanced parameter combinations
  • Option 4-3 The UE may first apply options 4-1/4-2 above.
  • the UE may then compress multiple (e.g., all) CSI-H generated contents using the configured compression parameters.
  • the compression may be, for example, Lempel-Ziv compression using the configured compression parameters (e.g., compression ratio).
  • the compression parameters may be set, for example, according to at least one of the methods described in Supplementary Note 2 below.
  • the UE may jointly generate the CSI-H (CSI-H content) using a certain quantization/compression method for multiple (e.g., all) CSI-Hs reported in one resource (a particular resource unit).
  • the UE may also divide the CSI-H reported in one resource (specific resource unit) into multiple groups. For each group, the UE may generate the CSI-H (CSI-H content) using a certain quantization/compression method.
  • the quantization/compression method may be, for example, an extension of an existing type of CSI (e.g., Type 2/Extended Type 2 CSI).
  • the UE may first generate CSI feedback content having the configured parameters (e.g., at least one CSI of type 2, extended type 2, and extended type 2 with extended parameter combination).
  • the configured parameters e.g., at least one CSI of type 2, extended type 2, and extended type 2 with extended parameter combination.
  • the UE may generate an element of CSI (eg, i n (n is 1 or 2)) according to a codebook defined in existing specifications (eg, up to Rel. 17/18).
  • a codebook defined in existing specifications eg, up to Rel. 17/18.
  • the CSI element (eg, i n (n is 1 or 2)) may be used to represent one CSI-H or multiple CSI-Hs.
  • the UE may generate one or a pair of differential values (e.g., delta-i n (n is 1 or 2)) for the element for each CSI-H.
  • the UE may also generate one or a pair of differential values (e.g., delta-i n (n is 1 or 2)) for each CSI-H other than the CSI-H directly represented by the CSI element (e.g., i n (n is 1 or 2)).
  • delta-i n n is 1 or 2
  • i n n is 1 or 2
  • delta-i 1 and delta-i 2 may denote the delta of each CSI-H relative to the common portion generated as i 1 and i 2, respectively.
  • delta-i n may not include all fields corresponding to i n , in other words, some elements may be common to all CSI-H, and other elements may be configurable using differential values.
  • the UE may generate CSI-H elements (final elements) that include i n (n is 1 or 2) and all delta-i n (n is 1 or 2).
  • FIG. 11 is a diagram showing an example of the generation of CSI elements relating to option 4-4.
  • the UE generates CSI1 to CSI3 as multiple pieces of CSI.
  • the UE In the example shown in Fig. 11, the UE generates PMI1 of CSI1 based on the codebook of extended type 2 (defined in Rel. 18 in the example shown in Fig. 11, for example).
  • i1 [ i1,1 i1,2 i1,5 i1,6,1 i1,7,1 i1,8,1 ]
  • i2 [ i2,3,1 i2,4,1 i2,5,1 ] are generated as elements of CSI1.
  • the UE reuses i1 for CSI1 in generating PMI2 for CSI2 (in other words, the spatial domain/frequency domain base vector and the reported coefficient are reused).
  • the UE reuses [ i1,1 i1,2 i1,5 i1,6,1] for CSI1 in generating PMI3 for CSI3 (in other words, the spatial domain/frequency domain base vector is reused, and the reported coefficient is not reused).
  • the UE may also report information (e.g., a bitmap) indicating which coefficients are to be reported when generating CSI3. This configuration allows the amplitude/phase of new coefficients to be reported with higher resolution, similar to the extended type 2 codebook.
  • information e.g., a bitmap
  • the UE may also jointly quantize multiple CSI-Hs using the (Rel. 18) extended type 2 codebook for predicted PMI or further extensions of the (Rel. 18) extended type 2 codebook for predicted PMI.
  • the further extension function may support N4 values greater than the length of the Doppler domain (DD)/time domain (TD) basis vectors (DFT basis vectors) (also called the number of DD/TD bases, N4) defined in existing specifications (e.g., Rel. 17/18). By configuring in this way, it is possible to support a number of CSI-Hs greater than eight.
  • DD Doppler domain
  • TD time domain
  • This further extension may also support a non-fixed duration in DD units (e.g. d), in which case the UE may report the actual duration ds between CSI-H as part of the reporting.
  • a non-fixed duration in DD units e.g. d
  • the UE may report the actual duration ds between CSI-H as part of the reporting.
  • the further extension function may support a Q value (which may also be referred to as Q) that is larger than the number of DD basis vectors defined in existing specifications (e.g., Rel. 17/18).
  • Q a Q value
  • the number of DD basis vectors defined in existing specifications e.g., Rel. 17/18.
  • historical CSI reports can be generated appropriately.
  • the UE may prepare (e.g., acquire/store/generate/quantize/compress) the CSI-H based on the first embodiment, without assuming/hoping to be configured/instructed to transmit any CSI other than the CSI-H.
  • the UE may determine whether the conditions for the event trigger are met based on the conditions in the second embodiment described above.
  • the UE may report CSI-H using a specific UL channel (e.g., PUSCH).
  • a specific UL channel e.g., PUSCH
  • the UE may report a scheduling request to request the UL channel resource, and then report CSI-H using the resource based on an instruction from the NW (e.g., (UL grant) DCI).
  • NW e.g., (UL grant) DCI
  • AI model information may mean information including at least one of the following: ⁇ Information on input/output of AI model. - Pre-processing/post-processing information for input/output of AI models. ⁇ Information on AI model parameters. - Training information for AI models. -Inference information for AI models. ⁇ Performance information about AI models.
  • the input/output information of the AI model may include information regarding at least one of the following: Input/output data content (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).
  • Input/output data content 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).
  • Supporting information for the data may be called meta-information).
  • the type of input/output data e.g. immutable values, floating point numbers).
  • Bit width of the input/output data eg, 64 bits for each input value).
  • Quantization interval (quantization step size) of input/output data eg, 1 dBm for L1-RSRP). The range that the input/output data can take
  • 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 (eg, 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 eg, 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.
  • Selection rule for whether or not to use as training data.
  • the information of the parameters of the AI model may include information regarding at least one of the following: - Weight information (e.g., neuron coefficients (connection coefficients)) in an AI model. ⁇ 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).
  • ResNet Residual Network
  • DenseNet DenseNet
  • RefineNet Transformer model
  • CRBlock Recurrent Neural Network
  • RNN Recurrent Neural Network
  • LSTM Long Short-Term Memory
  • GRU Gated Recurrent Unit
  • the weight information in the AI model may include information regarding at least one of the following: - Bit width (size) of the weight information. Quantization interval of weight information. - Granularity of weight information. - The range of possible weight information. ⁇ Weight parameters in AI models. - 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. - The type of layer (e.g., convolutional, activation, dense, normalization, pooling, attention). ⁇ 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)).
  • ⁇ Number of layers e.g., convolutional, activation, dense, normalization, pooling, attention.
  • ⁇ 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)).
  • the layer information may include information regarding at least one of the following: - The number of neurons in each layer. ⁇ Kernel size. - Stride for pooling/convolutional layers. - Pooling method (MaxPooling, AveragePooling, etc.). ⁇ Residual block information. ⁇ 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 the 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
  • Parameters that should be (are 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).
  • 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, an autoencoder for CSI feedback, and an 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 output 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 to 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 met, 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 certain UE capabilities, for example, as described below (by way of example only): - Supporting specific processing/operations/control/information for at least one of the above embodiments.
  • Support performance monitoring (reporting) based on the CSI framework.
  • 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 a 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 above-mentioned 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 activation of LCM based on a model/functionality ID, any RRC parameters for a specific release (e.g., Rel. 18/19/20), etc.
  • the UE may apply, for example, the behavior of Rel. 15/16/17.
  • Appendix A-1 A terminal having a receiving unit that receives settings for performance monitoring of artificial intelligence (AI)-based channel state information (CSI) reports, and a control unit that controls at least one of measuring and storing AI-based CSI and generating the CSI reports based on the settings.
  • AI artificial intelligence
  • CSI channel state information
  • the control unit controls measurement and storage of the CSI using reference signal resources arranged within the specific window based on information regarding a specific time window included in the configuration.
  • the terminal according to Supplementary Note A-1.
  • the control unit controls measurement and storage of the CSI based on information regarding a performance monitoring event included in the configuration.
  • the control unit When multiple CSIs are generated in generating the CSI report, the control unit generates the multiple CSIs using an element of CSI indicated by an absolute value and an element of CSI indicated by a differential value.
  • AI artificial intelligence
  • CSI channel state information
  • Appendix B-4 The terminal according to any one of Supplementary Note B-1 to Supplementary Note B-3, wherein the instruction includes at least one of information on the number of CSI to be reported, information on at least one of resources and channels for reporting, information on the number of CSI to be reported in each resource unit, information on at least one method of quantizing, compressing, and encoding the CSI, an index related to the CSI report, and an index related to the CSI resource.
  • 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 methods.
  • FIG. 12 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 via another base station 10 or directly.
  • the core network 30 may include, for example, at least one of 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 13 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.
  • the control unit 110, the transceiver unit 120, the transceiver antenna 130, and the transmission line interface 140 may each be provided in one or more units.
  • 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 120 may be configured as an integrated transceiver, or may be composed of a transmitter and a receiver.
  • the transmitter may be composed of a transmission processing unit 1211 and an RF unit 122.
  • the receiver 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 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 to 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 transmitting section and receiving section of the base station 10 in this disclosure may be configured with at least one of the transmitting/receiving section 120, the transmitting/receiving antenna 130, and the transmission path interface 140.
  • the transceiver unit 120 may transmit a configuration for performance monitoring of artificial intelligence (AI)-based channel state information (CSI) reporting.
  • the control unit 110 may use the configuration to instruct at least one of measuring and storing AI-based CSI and generating the CSI report (first/fourth embodiment).
  • the control unit 110 may control the reception of requests for network-side performance monitoring regarding artificial intelligence (AI)-based channel state information (CSI) reporting.
  • the transceiver unit 120 may transmit instructions regarding the CSI reporting based on the configuration (second/third embodiment).
  • the user terminal 14 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 unit 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.
  • CSI-RS, NZP CSI-RS, ZP CSI-RS, CSI-IM, CSI-SSB, etc. may be read as interchangeable.
  • 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 unit 220 may receive a configuration for monitoring the performance of an artificial intelligence (AI)-based channel state information (CSI) report.
  • the control unit 210 may control at least one of measuring and storing the AI-based CSI and generating the CSI report based on the configuration (first/fourth embodiment).
  • the control unit 210 may control the measurement and storage of the CSI using reference signal resources that are placed within a specific time window based on information about the specific time window included in the configuration (first embodiment).
  • the control unit 210 may control the measurement and storage of the CSI based on information about performance monitoring events included in the settings (first embodiment).
  • control unit 210 may generate the multiple CSIs using an element of CSI represented by an absolute value and an element of CSI represented by a differential value (fourth embodiment).
  • the control unit 210 may control the transmission of requests for network-side performance monitoring regarding artificial intelligence (AI)-based channel state information (CSI) reporting.
  • the transceiver unit 220 may receive instructions regarding the CSI reporting, which are transmitted based on the settings (second/third embodiment).
  • the request may be information indicating that a condition related to the network-side monitoring has been met, or information indicating whether the network-side monitoring is necessary (second embodiment).
  • the control unit 210 may determine to send the request if certain conditions are met (second embodiment).
  • the instructions may include at least one of information regarding the number of CSIs to be reported, information regarding at least one of the resources and channels for reporting, information regarding the number of CSIs to be reported in each resource unit, information regarding at least one method of quantizing, compressing, and encoding the CSI, an index regarding the CSI report, and an index regarding the CSI resource (third 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, selection, 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. 15 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 hardware configurations 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 operates 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, a communication module, etc.
  • 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, for example, 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 performs output to the outside. Note that 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, subframe, slot, minislot, and symbol all represent time units when transmitting a signal.
  • a different name may be used for radio frame, subframe, slot, minislot, and symbol. Note that the time units such as frame, subframe, slot, minislot, and 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 (PRB), a sub-carrier group (SCG), a resource element group (REG), a PRB pair, an RB pair, etc.
  • PRB physical resource block
  • 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 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/code/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. 16 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 in 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-Wide Band (UWB), Bluetooth (registered trademark), and other appropriate wireless communication methods, as well as next-generation systems that are expanded, modified, created
  • 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 an element using a designation 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 read as 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 refers 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 "access.”
  • 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)

Abstract

Un terminal selon un aspect de la présente divulgation comprend : une unité de commande qui commande la transmission d'une demande de surveillance de performances côté réseau relativement à un rapport d'informations d'état de canal (CSI) basé sur l'intelligence artificielle (IA) ; et une unité de réception qui reçoit une instruction relative au rapport d'informations CSI et transmise sur la base de réglages. Selon l'aspect de la présente divulgation, il est possible d'obtenir une réduction appropriée de surdébits, une estimation appropriée de canal et une utilisation appropriée de ressources.
PCT/JP2023/035756 2023-09-29 2023-09-29 Terminal, procédé de communication sans fil et station de base Pending WO2025069421A1 (fr)

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PCT/JP2023/035756 WO2025069421A1 (fr) 2023-09-29 2023-09-29 Terminal, procédé de communication sans fil et station de base

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020194639A1 (fr) * 2019-03-27 2020-10-01 株式会社Ｎｔｔドコモ Terminal
WO2023012999A1 (fr) * 2021-08-05 2023-02-09 株式会社Ｎｔｔドコモ Terminal, procédé de communication sans fil et station de base
WO2023079946A1 (fr) * 2021-11-08 2023-05-11 日本電気株式会社 Terminal sans fil, nœud de réseau d'accès radio, et procédés associés
WO2023152991A1 (fr) * 2022-02-14 2023-08-17 株式会社Ｎｔｔドコモ Terminal, procédé de communication sans fil et station de base

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020194639A1 (fr) * 2019-03-27 2020-10-01 株式会社Ｎｔｔドコモ Terminal
WO2023012999A1 (fr) * 2021-08-05 2023-02-09 株式会社Ｎｔｔドコモ Terminal, procédé de communication sans fil et station de base
WO2023079946A1 (fr) * 2021-11-08 2023-05-11 日本電気株式会社 Terminal sans fil, nœud de réseau d'accès radio, et procédés associés
WO2023152991A1 (fr) * 2022-02-14 2023-08-17 株式会社Ｎｔｔドコモ Terminal, procédé de communication sans fil et station de base

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