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WO2025069420A1 - 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
WO2025069420A1
WO2025069420A1 PCT/JP2023/035755 JP2023035755W WO2025069420A1 WO 2025069420 A1 WO2025069420 A1 WO 2025069420A1 JP 2023035755 W JP2023035755 W JP 2023035755W WO 2025069420 A1 WO2025069420 A1 WO 2025069420A1
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Prior art keywords
csi
information
model
disclosure
report
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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/JP2023/035755 priority Critical patent/WO2025069420A1/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
    • H04W72/23—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 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
  • 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 be reported 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 validation may refer to a sub-process of training to evaluate the quality of an AI model using a dataset different from the dataset used to train the model. This sub-process helps select model parameters that generalize beyond the dataset used to train the model.
  • AI model testing may refer to a sub-process of training to evaluate the performance of the final AI model using a dataset different from the dataset used for model training/validation. Note that testing, unlike validation, does not necessarily require subsequent model tuning.
  • model inference is performed based on the data (inference data) transferred from the collection stage.
  • This stage may include data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model inference, model monitoring (e.g., monitoring the performance of model inference), model performance feedback (feeding back model performance to the entity performing the model training), 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 UE side model may refer to an AI model whose inference is performed entirely in the UE.
  • a network side model may refer to an AI model whose inference is performed entirely in the network (e.g., gNB).
  • a one-sided model may refer to a UE-side model or a network-side model.
  • a two-sided model may refer to a pair of AI models where joint inference is performed.
  • joint inference may include AI inference where the inference is performed jointly across the UE and the network, e.g., a first part of the inference may be performed first by the UE and the remaining part by the gNB (or vice versa).
  • AI model monitoring may refer to the process of monitoring the inference performance of an AI model, and may be interchangeably read as model performance monitoring, performance monitoring, etc.
  • model registration may refer to making a model executable (registering) 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 gNB/UE may perform model inference (jointly).
  • the OAM/gNB/UE may perform model training and the UE may perform model inference.
  • model activation may mean activating an AI model for a particular function.
  • Model deactivation may mean disabling an AI model for a particular function.
  • Model switching may mean deactivating a currently active AI model for a particular function and activating a different AI model.
  • Model transfer may also refer to distributing an AI model over the air interface. This may include distributing either or both of the parameters of the model structure already known at the receiving end, or a new model with the parameters. This may also include a complete model or a partial model.
  • Model download may refer to model transfer from the network to the UE.
  • Model upload may refer to model transfer from the UE to the network.
  • 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 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
  • 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.
  • 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
  • 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 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.
  • historical CSI historical GT CSI
  • historical CSI historical GT CSI
  • 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 input RS resource may be an RS resource used for CSI measurement/reporting related to AI/ML-based CSI reporting.
  • 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 event may also be, for example, when the UE fails to receive (e.g. decode) a scheduled PDSCH a certain number of times (over a certain period of time).
  • 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 be configured/instructed on at least one of the functionality (and specific conditions), model ID, and CSI reporting index according to at least one method described in Supplementary Note 2 below.
  • the UE may use (only) the input RS resource corresponding to one or more configured/instructed functionality/model ID/CSI reporting index as CSI-H.
  • 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
  • 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.
  • 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 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.
  • 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 UE may report CSI-H based on specific triggers/settings/instructions.
  • 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.
  • 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 generate each CSI-H (CSI-H content) separately by scalar quantizing each element in the CSI-H with a particular bit-width.
  • 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
  • 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.
  • 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.
  • 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 réception qui reçoit une configuration pour la surveillance de performances d'un rapport d'informations d'état de canal (CSI) basé sur l'intelligence artificielle (IA) ; et une unité de commande qui commande, sur la base de la configuration, la génération du rapport d'informations CSI, et la mesure et/ou le stockage d'informations CSI basées sur l'IA. L'aspect de la présente divulgation permet d'obtenir une réduction appropriée de surdébits, une estimation appropriée de canal et une utilisation appropriée de ressources.
PCT/JP2023/035755 2023-09-29 2023-09-29 Terminal, procédé de communication sans fil et station de base Pending WO2025069420A1 (fr)

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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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