Terminal, wireless communication method, and base station

Abstract

A terminal according to one aspect of the present disclosure comprises: a reception unit that receives a configuration for performance monitoring of an artificial intelligence (AI)-based channel state information (CSI) report; and a control unit that controls, on the basis of the configuration, generation of the CSI report, and measurement and / or storage of AI-based CSI. The one aspect of the present disclosure makes it possible to achieve suitable overhead reduction, channel estimation, and resource utilization.

Description

Terminal, wireless communication method and base station

[0001] The present disclosure relates to a terminal, a wireless communication method, and a base station in a next-generation mobile communication system.

[0002] Long Term Evolution (LTE) has been specified for the Universal Mobile Telecommunications System (UMTS) network with the aim of achieving higher data rates and lower latency (Non-Patent Document 1). Also, LTE-Advanced (3GPP Rel. 10-14) has been specified with the aim of achieving higher capacity and more advanced features than LTE (Third Generation Partnership Project (3GPP (registered trademark)) Release (Rel.) 8, 9).

[0003] Successor systems to LTE (e.g., 5th generation mobile communication system (5G), 5G+ (plus), 6th generation mobile communication system (6G), New Radio (NR), 3GPP Rel. 15 or later, etc.) are also being considered.

[0004] 3GPP TS 36.300 V8.12.0 “Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description; Stage 2 (Release 8)”, April 2010

[0005] Regarding future wireless communication technologies, the use of artificial intelligence (AI) technologies such as machine learning (ML) for network / device control and management is being considered.

[0006] Use cases for utilizing AI models include spatial domain downlink (DL) beam prediction, temporal DL beam prediction, positioning, etc. Such beam prediction methods may be referred to as AI-based beam prediction (beam reporting), AI-based positioning, AI-based beam management (BM), etc. Temporal DL beam prediction may be referred to as time-domain Channel State Information (CSI) prediction, for example.

[0007] Another use case for utilizing an AI model is Channel State Information (CSI) compression using a two-sided AI model. Such a CSI compression method may be called AI-based CSI feedback and may be implemented using, for example, an autoencoder.

[0008] In utilizing such AI, performance monitoring of the AI ​​model is being considered. 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, base station (BS)).

[0009] In future wireless communication systems, a combination of performance monitoring on the UE side and the NW side is being considered, but such combined performance monitoring has not been sufficiently studied.

[0010] If this consideration is insufficient, it may not be possible to achieve appropriate overhead reduction, highly accurate channel estimation, or highly efficient resource utilization, which may hinder improvements in communication throughput and communication quality.

[0011] Therefore, one of the objects of the present disclosure is to provide a terminal, a wireless communication method, and a base station that can achieve suitable overhead reduction / channel estimation / resource utilization.

[0012] A terminal according to one aspect of the present disclosure includes a receiving unit that receives a configuration for performance monitoring of an artificial intelligence (AI)-based channel state information (CSI) report, and a control unit that controls at least one of measuring and storing the AI-based CSI and generating the CSI report based on the configuration.

[0013] According to one aspect of the present disclosure, it is possible to achieve favorable overhead reduction / channel estimation / resource utilization.

[0014] FIG. 1 is a diagram illustrating an example of a framework for managing an AI model. FIG. 2 is a diagram illustrating an example of specifying an AI model. FIG. 3 is a diagram illustrating an example of CSI feedback using an encoder / decoder. FIG. 4 is a diagram illustrating an example of a lifecycle management framework for performance monitoring in a UE according to an embodiment. FIG. 5 is a diagram illustrating an example of a lifecycle management framework for performance monitoring in a BS according to an embodiment. FIGS. 6A and 6B are diagrams illustrating an example of AI-based beam reporting. FIG. 7 is a diagram illustrating an example of performance monitoring of CSI compression on the UE side. FIG. 8 is a diagram illustrating an example of CSI reconstruction using a proxy model. FIG. 9A is a diagram illustrating an example of network-side monitoring. FIG. 9B is a diagram illustrating an example of UE-side monitoring. FIG. 10 is a diagram illustrating an example of a monitoring method involving a combination of UE-side monitoring and network-side monitoring. FIG. 11 is a diagram illustrating an example of generation of CSI elements according 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 a configuration of a base station according to an embodiment. FIG. 14 is a diagram illustrating an example of a configuration of a user terminal according to an embodiment. FIG. 15 is a diagram illustrating an example of hardware configurations 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.

[0015] (Channel State Information (CSI) Measurement / Reporting) This section describes CSI measurement / reporting in existing NR standards (e.g., Rel. 15-17 NR). The UE generates (also referred to as determining, calculating, estimating, measuring, etc.) CSI based on a reference signal (RS) (or a resource for the RS), and transmits (also referred to as reporting, feedback, etc.) the generated CSI to a network (e.g., a base station). 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)).

[0016] In the present disclosure, the CSI may be 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), L1-RSRP (Layer 1 Reference Signal Received Power), L1-RSRQ (Reference Signal Received Quality), L1-SINR (Signal to Interference plus Noise Ratio), L1-SNR (Signal to Noise Ratio), information on a channel matrix (or channel coefficient), information on a precoding matrix (or precoding coefficient), a Beam / Transmission Configuration Indication (BCI), 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), a Layer 1 Reference Signal Received Power (L1-RSRP), a Reference Signal Received Quality (L1-RSRQ), a Signal to Interference plus Noise Ratio (L1-SINR), a Signal to Noise Ratio (L1-SNR), information on a channel matrix (or channel coefficient), information on a precoding matrix (or precoding coefficient), a Beam / Transmission Configuration Indication (BCI), a Precoding Matrix Indicator (PMI), a Beam / Transmission Configuration Indicator (BCI), a Precoding Matrix Indicator (PMI ... Precoding Matrix Indicator (PMI), a Precoding Matrix Indicator (PMI), a Precoding Matrix Indicator (PMI), a Precoding Matrix In The information may include at least one of information regarding the TCI state / spatial relation.

[0017] 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), a Demodulation Reference Signal (DMRS), etc.

[0018] In the present disclosure, RS, CSI-RS, non-zero power (NZP) CSI-RS, zero power (ZP) CSI-RS, CSI interference measurement (CSI-IM), CSI-SSB, and SSB may be interchangeable. Furthermore, CSI-RS may include other reference signals.

[0019] The UE may receive configuration information regarding CSI reporting (which may be referred to as a CSI report configuration, report setting, etc.) and control the 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."

[0020] The CSI reporting configuration may include at least one of the following information: - Information about the CSI resources used for CSI measurements (resource configuration ID, e.g., "CSI-ResourceConfigId"), - Information about one or more quantities (CSI parameters) of CSI to report (report quantity information, e.g., "reportQuantity"), - Report type information indicating the time domain behavior of the reporting configuration (e.g., "reportConfigType").

[0021] In the present disclosure, 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 opportunity / opportunities, 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.

[0022] The information about the CSI resource may include information about the CSI resource for channel measurement, information about the CSI resource for interference measurement (NZP-CSI-RS resource), information about the CSI-IM resource for interference measurement, and the like.

[0023] The reporting quantity information may specify any one or a combination of the above CSI parameters (eg, CRI, RI, PMI, CQI, LI, L1-RSRP, etc.).

[0024] The report type information may indicate a periodic CSI (P-CSI) report, an aperiodic CSI (A-CSI) report, or a semi-persistent CSI (SP-CSI) report.

[0025] 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 the CSI-ResourceConfigId), and derives the CSI to report based on the measurement results.

[0026] The CSI resource configuration (e.g., 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.

[0027] The resource type information may indicate a P-CSI resource, an A-CSI resource, or an SP-CSI resource.

[0028] (Application of Artificial Intelligence (AI) Technology to Wireless Communications) With regard to future wireless communications technologies, the use of AI technology such as machine learning (ML) for network / device control and management is being considered.

[0029] For example, it is being considered that terminals (user terminals, user equipment (UE)) / base stations (BSs) will utilize AI technology to improve Channel State Information (CSI) feedback (e.g., reduced overhead, improved accuracy, prediction), improve beam management (e.g., improved accuracy, prediction in the time / space domain), and improve position measurement (e.g., improved position estimation / prediction).

[0030] Based on the input information, the AI ​​model may output at least one information such as an estimate, a prediction, a selected action, a classification, etc. 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 / location, future channel state information / radio link quality, etc.

[0031] In the present disclosure, AI may be interpreted as an object (also called a subject, object, data, function, program, etc.) that has (performs) at least one of the following characteristics: - Estimation based on observed or collected information; - Selection based on observed or collected information; - Prediction based on observed or collected information.

[0032] In the present disclosure, estimation, prediction, and inference may be used interchangeably. Also, in the present disclosure, estimate, predict, and infer may be used interchangeably.

[0033] In the present disclosure, an object may be, for example, an apparatus, device, etc., such as a UE or a BS. Also, in the present disclosure, an object may correspond to a program / model / entity that operates in the apparatus.

[0034] Also, in the present disclosure, an AI model may be interpreted as an object that has (performs) at least one of the following characteristics: - Generates an estimate by feeding information; - Predicts an estimate by feeding information; - Discovers features by feeding information; - Selects an action by feeding information.

[0035] Additionally, in this disclosure, 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.

[0036] In addition, in the present disclosure, the terms AI model, model, ML model, predictive analytics, predictive analysis model, tool, autoencoder, encoder, decoder, neural network model, AI algorithm, scheme, etc. may be interchangeable. The 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.

[0037] In this disclosure, the term "autoencoder" may be interchangeably referred to as any autoencoder, such as a stacked autoencoder, a convolutional autoencoder, etc. The encoder / decoder of this disclosure may employ a model such as a Residual Network (ResNet), a DenseNet, or a RefineNet.

[0038] Furthermore, in the present disclosure, the terms encoder, encoding, encode / encoded, modification / alteration / control by an encoder, compressing, compress / compressed, generating, generate / generated, etc. may be read interchangeably.

[0039] In addition, in the present disclosure, decoder, decoding, decode / decoded, modification / alteration / control by decoder, decompressing, decompress / decompressed, reconstructing, reconstruct / reconstructed, etc. may be read interchangeably.

[0040] In the present disclosure, a layer (of an AI model) may be interchangeably read as a layer (such as an input layer or an intermediate layer) used in the AI ​​model. The layer in the present disclosure may correspond to at least one of an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolutional layer, an activation layer, a dense layer, a normalization layer, a pooling layer, an attention layer, a dropout layer, a fully connected layer, etc.

[0041] In this disclosure, methods for training an AI model may include supervised learning, unsupervised learning, reinforcement learning, federated learning, etc. 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 an environment with which the model interacts.

[0042] In the present disclosure, terms such as generate, calculate, derive, etc. may be interchangeable. In the present disclosure, terms such as implement, operate, operate, execute, etc. may be interchangeable. In the present disclosure, terms such as train, learn, update, retrain, etc. may be interchangeable. In the present disclosure, terms such as infer, after-training, live use, actual use, etc. may be interchangeable. In the present disclosure, signal may be interchangeable with signal / channel.

[0043] FIG. 1 is a diagram illustrating an example of a framework for managing AI models. In this example, each stage related to an AI model is shown as a block. This example is also referred to as AI model life cycle management (LCM).

[0044] The data collection stage corresponds to a 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.

[0045] Note that data collection may refer to a process in which data is collected by a network node, a management entity, or a UE for the purpose of AI model training / data analysis / inference. In this disclosure, the terms "process" and "procedure" may be interchangeable. Also, in this disclosure, collection may refer to obtaining a data set (e.g., usable as input / output) for AI model training / inference based on measurements (e.g., channel measurements, beam measurements, radio link quality measurements, position estimation, etc.).

[0046] In the present disclosure, offline field data may be data collected from the field (real world) and used for offline training of an AI model. Also, in the present disclosure, online field data may be data collected from the field (real world) and used for online training of an AI model.

[0047] In the model training stage, 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, transformation, etc.), model training / validation, model testing (e.g., verifying whether the trained model meets a performance threshold), model exchange (e.g., transferring the model for distributed learning), and model deployment / update (deploying / updating the model to the entity that will perform model inference).

[0048] It should be noted that 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.

[0049] AI model validation may also refer to a sub-process of training that evaluates the quality of an AI model using a dataset different from the dataset used to train the model, which helps select model parameters that generalize beyond the dataset used to train the model.

[0050] AI model testing may also refer to a sub-process of training for evaluating the performance of the final AI model using a dataset different from that used for model training / validation. Note that, unlike validation, testing does not necessarily require subsequent model tuning.

[0051] In the model inference stage, 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 training the model), and output (providing model output to the actor).

[0052] Additionally, 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.

[0053] Also, a UE side model may refer to an AI model whose inference is performed entirely in the UE, and a network side model may refer to an AI model whose inference is performed entirely in the network (e.g., gNB).

[0054] Also, 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 in which joint inference is performed. Here, joint inference may include AI inference in which 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).

[0055] In addition, AI model monitoring may refer to a process for monitoring the inference performance of an AI model, and may be interchangeably read as model performance monitoring, performance monitoring, etc.

[0056] Note that model registration may refer to assigning a version identifier to a model and making the model executable (registering) the model by compiling it into the specific hardware used in the inference stage. Also, model deployment may refer to distributing (or activating in) a runtime image (or an image of an execution environment) of a fully developed and tested model to (or enabling in) a target (e.g., UE / gNB) where inference will be performed.

[0057] An actor stage 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.

[0058] For example, training of a model for mobility optimization may be performed in, for example, Operation, Administration and Maintenance (Management) (OAM) / gNodeB (gNB) in a network (NW). In the former case, interoperability, large-capacity storage, operator manageability, and model flexibility (feature engineering, etc.) are advantageous. In the latter case, the latency of model updates and the need for data exchange for model deployment are advantageous. Inference of the above model may be performed in, for example, a gNB.

[0059] The entity that performs training / inference may vary depending on the use case (i.e., the function of the AI ​​model), which may include beam management, beam prediction, autoencoder (or information compression), CSI feedback, positioning, etc.

[0060] For example, for AI-assisted beam management based on measurement reports, the OAM / gNB may perform model training and the gNB may perform model inference.

[0061] For AI-assisted UE-assisted positioning, a Location Management Function (LMF) may perform model training and the LMF may perform model inference.

[0062] For CSI feedback / channel estimation using an autoencoder, the OAM / gNB / UE may perform model training and the gNB / UE may perform model inference (jointly).

[0063] For AI-assisted beam management or AI-assisted UE-based positioning based on beam measurements, the OAM / gNB / UE may perform model training and the UE may perform model inference.

[0064] Note that model activation may mean activating an AI model for a specific function, model deactivation may mean disabling an AI model for a specific function, and model switching may mean deactivating a currently active AI model for a specific function and activating a different AI model.

[0065] Model transfer may also refer to distributing an AI model over the air interface. This distribution may include distributing parameters of a model structure already known at the receiving end, or a new model with parameters, or both. This distribution may include a complete model or a partial model. Model download may refer to transferring a model from the network to the UE. Model upload may refer to transferring a model from the UE to the network.

[0066] 2 is a diagram showing an example of specifying an AI model. In this example, a UE and a NW (e.g., a base station (BS)) can recognize models #1 and #2 (although they do not need to fully understand the details of the models). 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.

[0067] (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 using, for example, an autoencoder.

[0068] 3 is a diagram illustrating an example of CSI feedback using an encoder / decoder. The UE inputs CSI to an encoder and transmits information (CSI feedback information) including encoded bits output from an antenna. The BS inputs the received CSI feedback information bits to a corresponding decoder to obtain the output CSI.

[0069] 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). In other words, the CSI may correspond to information on the channel state in the space-frequency domain. Note that the input may include information other than CSI.

[0070] The CSI output from the decoder may be reconstructed CSI corresponding to the input to the encoder, or may be CSI different from the input to the encoder (e.g., information on precoding coefficients if the input information is information on channel coefficients).

[0071] The encoder / decoder may also include pre-processing for the input and post-processing for the output.

[0072] The encoded bits are more compressed than the input information before encoding, and this is expected to reduce the communication overhead required for CSI feedback.

[0073] (Lifecycle Management Framework for Performance Monitoring) In the following, each step in the lifecycle management framework for performance monitoring at the UE / BS will be described for AI-based CSI feedback.

[0074] FIG. 4 illustrates an example lifecycle management framework for performance monitoring in a UE according to an embodiment.

[0075] In the performance monitoring step, the UE monitors the performance of the model and the fallback scheme (non-AI based CSI feedback).

[0076] In the step of model evaluation at the UE, the UE evaluates the performance of the monitored / reported model and fallback scheme (non-AI based CSI feedback).

[0077] In the performance reporting step, the UE reports the monitored performance to the NW.

[0078] In the step of model evaluation in the NW, the NW evaluates the performance of the reported model and fallback scheme.

[0079] In the model request step, the UE sends a request to the NW regarding which model should be applied or whether a fallback scheme should be applied.

[0080] In the model activation / deactivation step, the UE may be instructed which scheme (model) to activate. The UE may activate a model or a fallback scheme.

[0081] Note that some of the steps shown in the figure (for example, steps indicated by dashed lines) may be performed as needed.

[0082] FIG. 5 illustrates an example lifecycle management framework for performance monitoring in a BS according to one embodiment.

[0083] In the step of reporting for performance monitoring, the UE reports information for performance monitoring in the NW (BS).

[0084] In the step of performance monitoring in the NW, the NW monitors the performance of the model and fallback scheme (non-AI based CSI feedback).

[0085] In the step of model evaluation in the NW, the NW evaluates the performance of the model and the fallback scheme.

[0086] In the model activation / deactivation step, the UE may be instructed which scheme (model) to activate. The UE may activate a model or a fallback scheme.

[0087] Note that some of the steps shown in the figure (for example, steps indicated by dashed lines) may be performed as needed.

[0088] (AI-Based Beam Reporting) As a use case of utilizing an AI model, spatial domain downlink (DL) beam prediction or temporal DL beam prediction using a one-sided AI model in a UE or a NW is being considered. Such a beam prediction method may be called AI-based beam prediction (beam reporting), AI-based beam management (BM), etc.

[0089] 6A and 6B are diagrams illustrating an example of AI-based beam reporting. 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.

[0090] 6B shows temporal DL beam prediction. The UE may measure a time series of beams, input the measurement results into an AI model, and output a prediction result of the beam quality of a future beam.

[0091] Note that the spatial domain DL beam prediction may be referred to as BM case 1, and the temporal DL beam prediction may be referred to as BM case 2. Furthermore, the temporal DL beam prediction may be referred to as, for example, time domain CSI prediction.

[0092] Furthermore, the beams / RS associated with the output (prediction result) of the AI ​​model may be referred to as set A. The beams / RS associated with the input of the AI ​​model may be referred to as set B.

[0093] 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.

[0094] 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 will be in the top K, and the angles of these beams.

[0095] In addition to the candidate outputs of the AI ​​model in BM Case 1, the candidate outputs of the AI ​​model in BM Case 2 include predicted beam obstructions.

[0096] (Performance Monitoring of CSI Compression at UE Side) Figure 7 shows an example of performance monitoring of CSI compression at UE side, in which if an encoder is available at the UE, the UE may monitor expected performance.

[0097] The performance (expected performance) monitored in Figure 7 may be at least one of the following: (1) expected communication quality calculated based on the output of the AI ​​model, e.g., expected CQI that meets a certain block error probability under a specific resource allocation assumption, (2) expected performance of the reconstructed CSI compared to the target CSI (e.g., expected noise variance).

[0098] 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 CQIs, etc. Furthermore, 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 (e.g., the expected number of symbols, the number of resource blocks, etc.) may be specified in a standard. Furthermore, the certain block error probability may be, for example, at least one of 0.1, 0.00001, etc.

[0099] As shown in Figure 7, it is assumed that the CSI output from the decoder is the reconstructed CSI corresponding to the input to the encoder. Note that 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 corresponding to the encoder.

[0100] The UE performs channel measurement based on the CSI-RS transmitted from the BS and obtains a channel matrix H. The UE estimates its performance based on H.

[0101] When the input of the encoder included in the UE is a precoding matrix W, the UE may obtain W by performing a specific process (e.g., Singular Value Decomposition (SVD)) on H. The UE estimates performance based on W.

[0102] Furthermore, when the input of the encoder of the UE is a precoding matrix p-W to which preprocessing (e.g., Inverse Discrete Fourier Transform (IDFT) and sampling) has been applied, the UE may obtain p-W by performing the preprocessing on the above-mentioned W. The UE may estimate performance based on p-W or may estimate performance based on W.

[0103] In addition, the UE may transmit a performance report to the BS as needed.

[0104] The UE may receive information on the expected performance of the AI ​​model corresponding to the AI ​​model of the encoder from the vendor's data server or NW. The information may be included in the AI ​​model information.

[0105] In the present disclosure, the data server may be interchangeably referred to as a repository, an uploader, a library, a cloud server, simply a server, etc. Furthermore, the data server in the present disclosure may be provided by any platform such as GitHub (registered trademark), or may be operated by any company / organization.

[0106] In this example, the UE performs channel measurement based on the CSI-RS transmitted from the BS and obtains H / W / p-W corresponding to the target CSI. The UE also calculates (estimates) 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.

[0107] (CSI Reconstruction at UE Side) 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, which can reduce the processing and storage issues in the UE. The proxy model can be different from the actual reconstruction model in the base station, which can avoid the uniqueness issue.

[0108] FIG. 8 is a diagram showing an example of CSI reconstruction (pseudo-reconstruction) using a proxy model. The UE receives a proxy model for decoding from a NW (base station). The UE uses the proxy model to reconstruct encoded CSI and outputs it as an estimated CSI. The UE maps the estimated result to actual CSI and calculates a KPI (Key Performance Indicator) (e.g., squared generalized cosine similarity (SGCS)). As such, there is a strong correlation between the KPI (SGCS) when an actual decoder is used and the KPI (SGCS) using a proxy model.

[0109] (Performance Monitoring of AI / ML CSI Feedback) Performance monitoring of AI / ML CSI feedback is considered to include NW side monitoring and UE side monitoring.

[0110] The NW-side monitoring may be monitoring based on ground-truth feedback and channel estimation using UL reference signals (e.g., SRS).

[0111] 9A is a diagram showing an example of NW-side monitoring. In the example shown in FIG. 9A, first, in the UE, measurement of input RS resources (RS resource for input) is performed, and then AI / ML CSI generation is performed depending on whether the acquired matrix is ​​H or W. Also, in the UE, measurement of input / reference RS resources (RS resource for input / reference) is performed, and then ground-truth feedback / SRS transmission is performed depending on whether the acquired matrix is ​​H or W.

[0112] Then, based on the CSI report based on the generated CSI, AI / ML CSI reconstruction is performed in the NW, and based on (comparing) the CSI reconstruction and the ground-truth feedback / SRS, Normalized Mean Square Error (NMSE) / Squared Generalized Cosine Similarity (SGCS) are calculated as KPIs.

[0113] The UE side monitoring may be monitoring based on a proxy CSI reconstruction model.

[0114] 9B is a diagram showing an example of UE-side monitoring. In the example shown in FIG. 9B, first, the UE measures the input RS resource (RS resource for input). Then, AI / ML CSI is generated depending on whether the acquired matrix is ​​H or W, and proxy AI / ML CSI reconfiguration is performed based on the obtained bitstream. Furthermore, the UE measures the input / reference RS resource (RS resource for input / reference). The UE reports CSI based on the CSI generation to the NW, and NMSE / SGCS are calculated based on (comparing) the CSI reconfiguration and the input / reference RS resource measurement. Furthermore, 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.

[0115] In addition, intermediate KPIs such as SGCS, NMSE, and Recall at Rank (RAR) may be reused as KPIs.

[0116] (Analysis) When performing the above-mentioned NW-side monitoring and UE-side monitoring, the asymmetry of data and computational capabilities between the NW and the UE (particularly, increased complexity on the UE side) becomes a problem.

[0117] For example, the NW may have sufficient capability (computational power) to accurately monitor multiple models but may lack target CSI data for monitoring, whereas the UE may have target CSI data but may not have sufficient capability to accurately monitor multiple models.

[0118] For this reason, in CSI compression, the introduction of a monitoring method that combines network-side monitoring and UE-side monitoring is being considered. This method may, for example, monitor only an active model in the UE (first monitoring, which may also be called coarse monitoring), and monitor multiple models in the NW when a specific event is triggered (second monitoring, which may also be called fine monitoring).

[0119] This method can, for example, reduce the overhead for ground-truth feedback by the UE and utilize the computational power of the NW for accurate model monitoring.

[0120] However, such a combination of NW-side monitoring and UE-side monitoring has not been sufficiently studied, specifically, the specifications for settings / events related to UE-side monitoring, the operations related to requests for NW-side monitoring operations, the method of reporting CSI after UE-side monitoring, and the method of generating the CSI report have not been sufficiently studied.

[0121] If these considerations are not sufficient, it may not be possible to achieve appropriate overhead reduction, highly accurate channel estimation, or highly efficient resource utilization, which may hinder improvements in communication throughput and communication quality.

[0122] Therefore, the present inventors came up with a method for solving these problems.

[0123] Hereinafter, embodiments according to the present disclosure will be described in detail with reference to the drawings. Wireless communication methods according to the embodiments may be applied independently or in combination.

[0124] In the present disclosure, "A / B" and "at least one of A and B" may be interpreted interchangeably. Also, in the present disclosure, "A / B / C" may mean "at least one of A, B, and C."

[0125] In the present disclosure, terms such as notify, activate, deactivate, indicate (or indicate), select, configure, update, and determine may be read interchangeably. In the present disclosure, terms such as support, control, controllable, operate, and operate may be read interchangeably.

[0126] In the present disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, higher layer parameters, fields, information elements (IEs), settings, etc. may be interchangeable. In the present disclosure, Medium Access Control (MAC) control elements (CEs), update commands, activation / deactivation commands, etc. may be interchangeable.

[0127] In the present disclosure, 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 protocol (e.g., NR Positioning Protocol A (NRPPa) / LTE Positioning Protocol (LPP)) messages), or a combination thereof.

[0128] In the present disclosure, MAC signaling may use, for example, a MAC Control Element (MAC CE), a MAC Protocol Data Unit (PDU), etc. 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.

[0129] In the present disclosure, physical layer signaling may be, for example, Downlink Control Information (DCI), Uplink Control Information (UCI), and the like.

[0130] In the present disclosure, the terms index, identifier (ID), indicator, resource ID, etc. may be interchangeable. In the present disclosure, the terms sequence, list, set, group, cluster, subset, etc. may be interchangeable.

[0131] In the present disclosure, the terms panel, UE panel, panel group, beam, beam group, precoder, Uplink (UL) transmitting entity, Transmission / Reception Point (TRP), base station, Spatial Relation Information (SRI), spatial relation, SRS Resource Indicator (SRI), Control Resource Set (CORESET), Physical Downlink Shared Channel (PDSCH), Codeword (CW), Transport Block (TB), Reference Signal (RS), antenna port (e.g., Demodulation Reference Signal (DMRS) port), antenna port group (e.g., DMRS port group), group (e.g., spatial relation group, Code Division Multiplexing (CDM) group, reference signal group, CORESET group, Physical Uplink Control Channel (PUCCH) group, PUCCH resource group), resource (e.g., reference signal resource, SRS resource), resource set (e.g., reference signal resource set), CORESET pool, downlink Transmission Configuration Indication state (TCI state) (DL TCI state), uplink TCI state (UL TCI state), unified TCI state, common TCI state, Quasi-Co-Location (QCL), QCL assumption, etc. may be read as interchangeable.

[0132] In the present disclosure, CSI-RS, non-zero power (NZP) CSI-RS, zero power (ZP) CSI-RS, and CSI interference measurement (CSI-IM) may be interchangeable. Furthermore, CSI-RS may include other reference signals.

[0133] In this disclosure, a measured / reported RS may refer to an RS that is measured / reported for a CSI report.

[0134] In the present disclosure, timing, time, duration, slot, subslot, symbol, subframe, etc. may be read interchangeably.

[0135] In the present disclosure, the terms direction, axis, dimension, domain, polarization, polarization component, etc. may be read interchangeably.

[0136] In the present disclosure, estimation, prediction, and inference may be used interchangeably. Also, in the present disclosure, estimate, predict, and infer may be used interchangeably.

[0137] In the present disclosure, the terms autoencoder, encoder, decoder, etc. may be replaced with at least one of a model, an ML model, a neural network model, an AI model, an AI algorithm, etc. Furthermore, the term autoencoder may be replaced with any autoencoder, such as a stacked autoencoder or a convolutional autoencoder. The encoder / decoder of the present disclosure may employ a model such as a Residual Network (ResNet), a DenseNet, or a RefineNet.

[0138] In the present disclosure, the terms bit, bit string, bit sequence, sequence, value, information, value obtained from a bit, information obtained from a bit, etc. may be read interchangeably.

[0139] In the present disclosure, a layer (for an encoder) may be interchangeably read as a layer (such as an input layer or an intermediate layer) used in an AI model. The layer in the present disclosure may correspond to at least one of an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolutional layer, an activation layer, a dense layer, a normalization layer, a pooling layer, an attention layer, a dropout layer, a fully connected layer, etc.

[0140] In the present disclosure, RSRP may be interchangeably read as any parameter related to received power / received quality, etc. (e.g., RSRQ, SINR, CSI), etc.

[0141] In the present disclosure, the RS may be, for example, a CSI-RS, an SS / PBCH block (SS block (SSB)), etc. Also, the RS index may be a CSI-RS Resource Indicator (CRI), an SS / PBCH Block Indicator (SSBRI), etc.

[0142] In the present disclosure, 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), and the like.

[0143] In the present disclosure, the terms "receive beam assumption," "number of receive beams," "index of receive beam," "receive beam selection," "receive beam setting," and "receive beam instruction" may be interchangeable. In the present disclosure, the terms "receive beam," "transmit beam," "DL receive beam," "DL transmit beam," and "pair of transmit beam and receive beam" may be interchangeable. In the present disclosure, 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."

[0144] In this disclosure, 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, and updating may be instructed (controlled) based on the functionality (e.g., for each function).

[0145] 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 numbers of layers) but may be treated as the same model.

[0146] In this disclosure, the use cases may include AI / ML for at least one of enhanced CSI feedback, beam management, and enhanced positioning, and may also include other new use cases for AI / ML.

[0147] In the present disclosure, 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 about the applicability of the model / functionality, the environment, the UE / gNB settings, etc.

[0148] In the present disclosure, functionality may be simply read as "function."

[0149] In the present disclosure, functionality, function, functionality ID, model, and model ID may be read interchangeably.

[0150] In the present disclosure, update, report, and transmit may be read interchangeably.

[0151] In the present disclosure, meta information, assistance information, sensing information, KPI, performance KPI, UE status, and status may be read interchangeably.

[0152] In the present disclosure, monitor and evaluation may be read interchangeably.

[0153] In the present disclosure, the terms "decision," "judgement," and "application of a particular action" may be read interchangeably.

[0154] In the present disclosure, the terms entity, specific entity, UE, NW, gNB, and LMF may be read interchangeably.

[0155] In the present disclosure, NW, LMF, gNB, and BS may be read interchangeably.

[0156] In the present disclosure, the UE-side model and the UE may be read interchangeably.

[0157] In the present disclosure, the model, UE-side model, logical model, and physical model may be interchangeable.

[0158] In this disclosure, 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.

[0159] In the present disclosure, the terms performance index and monitoring index may be read interchangeably.

[0160] In the present disclosure, the terms association, correspondence, and mapping may be read interchangeably.

[0161] In the present disclosure, monitor results, monitored results, post-monitoring results, and monitoring results may be read interchangeably.

[0162] In the present disclosure, the monitoring results may include inference results, and information regarding at least one of a performance index, and the content of an event occurrence based on the performance index / whether or not an event has occurred.

[0163] In the present disclosure, for UE-side performance monitoring of models / functionality, the UE may report the following information as monitoring results: Performance indicators corresponding to the monitored models / functionality; Occurrence of an event in the calculation of the performance indicators corresponding to the monitored models / functionality (e.g., the value of a positive indicator is greater than / less than a threshold for a certain duration).

[0164] In the present disclosure, an AI / ML-based CSI report (AI / ML CSI report) may refer to a CSI report associated with at least one of a model ID and specific functionality. For example, the AI / ML-based CSI report (AI / ML CSI report) may be at least one of predicted CSI, compressed CSI, advanced CSI, and CSI of any type (e.g., type [x]).

[0165] In the present disclosure, AI / ML-based CSI reporting and CSI reporting may be read interchangeably.

[0166] In the present disclosure, a proxy model may refer to a model that is used only for performance monitoring and has no other uses other than performance monitoring, or may refer to a model that estimates side information (CQI / RI, etc.) of the recovered CSI.

[0167] In this disclosure, AI / ML functionality and AI / ML CSI functionality may refer to functionality commanded by the NW or reported by the UE, such as predicted CSI, compressed CSI, advanced CSI, or CSI of any type (e.g., type [x]).

[0168] In the present disclosure, AI / ML functionality, AI / ML CSI functionality, and functionality may be read interchangeably.

[0169] In this disclosure, 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).

[0170] In the present disclosure, report quantity, information regarding the report quantity, and report quantity information may be read interchangeably.

[0171] In the present disclosure, reporting settings, CSI reporting settings, settings related to performance monitoring of CSI reports, monitor reporting settings, and reporting settings for monitoring may be read interchangeably.

[0172] In the present disclosure, reports, CSI reports, measurement result reports, monitoring reports, and monitor result reports may be read interchangeably.

[0173] In this disclosure, historical CSI, historical ground-truth (GT) CSI, historical GT CSI, GT CSI, CSI, CSI reporting, CSI compression, etc. may be read interchangeably.

[0174] In the present disclosure, CSI-RS and PDSCH / DMRS may be read as interchangeable.

[0175] In this disclosure, type X monitoring (results) may refer to precoded RS resource-based monitoring (results), and type Y monitoring (results) may refer to PDSCH / DMRS-based monitoring (results).

[0176] In this disclosure, RS Type B may refer to an RS (signal / channel) associated with a measurement report / monitoring result report, and RS Type A may refer to an RS (signal / channel) associated with a model ID or a CSI report associated with a specific functionality / feature.

[0177] In the present disclosure, the terms performance metric, metric for monitoring reporting, and KPI may be read interchangeably.

[0178] (Wireless communication method) Fig. 10 is a sequence diagram between a terminal (UE) and a base station (NW) showing an overview 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.

[0179] As shown in FIG. 9, first, the NW may transmit various settings (e.g., reporting settings for CSI reporting) to the UE.

[0180] The UE may then receive the CSI-RS and perform AI / ML CSI reporting, after which the UE may receive each channel (e.g., PDSCH) transmitted based on the CSI reporting, etc.

[0181] In the example shown in Fig. 10, the UE may start a first monitoring (coarse monitoring) from a specific timing, and may store / acquire historical ground-truth CSI after being triggered to store it.

[0182] The UE may determine whether to trigger monitoring of an event report based on at least one of the first monitoring and the historically stored / obtained correct CSI. The UE may send an event report (e.g., a request for second monitoring) to the NE.

[0183] The NW may at least one of transmit a setting for historical CSI feedback and schedule the historical CSI feedback based on the event report.

[0184] The UE may perform CSI compression on multiple historical CSIs and provide historical correct feedback based on a configuration / schedule from the NW.

[0185] The NW may perform second monitoring based on feedback from the UE, and may perform an operation such as model switching / fallback based on the second monitoring.

[0186] In the present disclosure, the terms "coherent joint transmission (CJT) codebook," "type 2 codebook for CJT," "extended type 2 codebook for CJT," "type 2 codebook for Rel. 18 CJT," "type II-CJT-r18," "additional extended type 2 PS codebook for CJT," "type 2 PS codebook for Rel. 18 CJT," and "type II-CJT-PortSelection-r18'" may be read interchangeably.

[0187] In the present disclosure, each embodiment / option may be applied alone or in combination with other embodiments / options.

[0188] In each embodiment of the present disclosure, an operation related to historical CSI (historical GT CSI) will be described, where "historical" means that the historical CSI is stored in the UE. Therefore, the historical CSI (historical GT CSI) in each embodiment may be simply referred to as "CSI."

[0189] First Embodiment The first embodiment relates to UE operation and configuration related to preparation for historical CSI reporting in the UE.

[0190] The UE may receive a configuration for storing / retrieving historical GT CSI from the NW, which may be transmitted according to at least one of the methods described in Supplementary Note 2 below.

[0191] The setting may include, for example, at least one of information regarding the time / time slot for starting the storage of historical GT CSI (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 monitoring result is equal to or less than 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.

[0192] The UE may store GT CSI for a configured time / storage amount based on the configuration.

[0193] Based on this configuration, the UE may drop the oldest CSI that it stores.

[0194] The UE may assume that the CSI measured using input RS resources for the AI / ML function (e.g., reporting of predicted / compressed CSI) is reported to the NW using a specific format.

[0195] The particular format may be, for example, a format other than the AI / ML-based CSI reporting format.

[0196] In this disclosure, the CSI according to this particular format may be conveniently referred to as CSI-H.

[0197] In the present disclosure, the input RS resource may be an RS resource used for CSI measurement / reporting related to AI / ML-based CSI reporting.

[0198] The UE / NW may follow options 1-1 / 1-2 below.

[0199] <<Option 1-1>> When an input RS resource is located within a specific time resource (e.g., a time window), the UE may measure / store CSI using the input RS resource.

[0200] The start / end timing (e.g., slot / symbol) of the time resource (e.g., time window) may be configured / indicated to the UE, which may be configured / indicated according to at least one of the methods described in Supplementary Note 2 below.

[0201] Furthermore, the start / end timing (e.g., slot / symbol) of the time resource (e.g., time window) may be specified in advance (it does not need to be set / instructed to the UE). For example, the end timing of the time window may be the current slot.

[0202] According to option 1-1, the input RS resource can be appropriately determined based on the time resource.

[0203] <<Option 1-2>> The UE may be configured / instructed about an event that triggers CSI measurement / storage. The UE may determine to measure / storage CSI based on the configured / instructed event.

[0204] The setting / instruction may be performed according to at least one of the methods described in Supplementary Note 2 below.

[0205] 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 certain time period / timing / instance).

[0206] The specific period / timing / instance / threshold may be specified in advance or may be set / instructed based on at least one of the methods described in Supplementary Note 2 below.

[0207] In the present disclosure, the start timing / duration of the first monitoring by the UE may be specified in advance in a specification, may be set / instructed from the NW using higher layer signaling (RRC / MAC CE) / DCI, may be determined based on a report of UE capabilities, may depend on the implementation of the UE, or may be determined based on a combination of at least two of these.

[0208] The event may also be, for example, when the UE fails to receive (eg, decode) a scheduled PDSCH a certain number of times (in a certain period of time).

[0209] The specific number of times may be one or more (e.g., N times (consecutive / non-consecutive)), where N may be set / instructed, for example, according to at least one of the methods described in Supplementary Note 2 below.

[0210] The event may also be, for example, when the UE receives a trigger signal, which may be configured / indicated, for example, according to at least one of the methods described in Supplementary Note 2 below.

[0211] In the above options 1-1 / 1-2, the UE may be configured / instructed to at least one of functionality (and specific conditions), model ID, and CSI reporting index according to at least one method described in the following supplementary note 2. The UE may use (only) input RS resources corresponding to one or more configured / instructed functionality / model ID / CSI reporting index as CSI-H.

[0212] Also, in the above options 1-1 / 1-2, the UE may assume / determine that more than a certain number (e.g., M) of CSI-Hs (or CSIs equal to or greater than a certain number) will not be reported to the NW. In other words, the UE may acquire / store up to M (or M-1) pieces of historical CSI. This method allows the number of CSIs stored by the UE to be appropriately controlled.

[0213] 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.

[0214] For example, the UE may assume that the CSI-H reported to the NW is the CSI measured in the latest slot (time slot).

[0215] According to the first embodiment described above, it is possible to appropriately define configuration / UE operations related to preparation (e.g., measurement / storage) of historical CSI reporting.

[0216] Second Embodiment The second embodiment relates to a setting and a UE operation regarding a request for second monitoring in a NW.

[0217] The UE may receive at least one of the setting and a trigger signal for the request from the NW.

[0218] The setting / triggering signal may be transmitted according to at least one of the methods described in Appendix 2 below.

[0219] The configuration may include, for example, at least one of information regarding specific conditions (e.g., thresholds for monitored KPIs, set amounts of collected GT CSI, and / or types of CSI (e.g., periodic / non-periodic / semi-persistent)), triggers for event reporting regarding the second monitoring, and information regarding resources for reporting.

[0220] The UE may use a specific method to perform an event report regarding the second monitoring.

[0221] The UE may take action according to options 2-1-1 / 2-1-2 below based on certain conditions.

[0222] The specific conditions may be set, for example, according to at least one of the methods described in Supplementary Note 2 below.

[0223] <<Option 2-1-1>> The UE may report a specific message (which may be called, for example, Message A) if a set condition is met.

[0224] The particular message may be sent, for example, according to at least one of the methods described in Appendix 3 below.

[0225] The specific message may be, for example, a message to notify the NW that the conditions for the second monitoring have been met.

[0226] According to option 2-1-1, the trigger operation for the second monitoring can be performed appropriately.

[0227] <<Option 2-1-2>> The UE may report a status regarding whether the set conditions are met or not.

[0228] The report may be transmitted, for example, according to at least one of the methods described in Appendix 3 below.

[0229] For example, the UE may transmit information (eg, one bit of information) indicating a binary state indicating whether or not second monitoring is required.

[0230] According to option 2-1-2, the trigger operation for the second monitoring can be performed appropriately.

[0231] At least one of the specific message in option 2-1-1 and the report in option 2-1-2 may be reported, for example, using a dedicated field in the AI / ML-based CSI feedback.

[0232] In addition, 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, 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))).

[0233] In addition, the specific message in option 2-1-1 and / or 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.

[0234] In addition, the specific message in option 2-1-1 and / or the report in option 2-1-2 may be transmitted using resources configured / instructed, for example, according to at least one of the methods described in Supplementary Note 2 below.

[0235] Furthermore, the specific condition in this embodiment may be, for example, at least one of the following options 2-2-1 to 2-2-4.

[0236] <<Option 2-2-1>> The specific condition may be, for example, that a metric monitored by the UE is higher / lower (or equal to or greater than / less than) a specific threshold (at a specific period / timing / instance).

[0237] The metric may be, for example, a metric related to AI / ML model performance.

[0238] The particular threshold may be set / indicated, for example, according to at least one of the methods described in Supplementary Note 2 below.

[0239] <<Option 2-2-2>> The specific condition may be, for example, that the UE fails to receive (e.g., decode) the scheduled PDSCH a specific number of times (in a certain period of time).

[0240] The specific number of times may be one or more (e.g., L times (consecutive / non-consecutive)), where L may be set / instructed, for example, according to at least one of the methods described in Supplementary Note 2 below.

[0241] <<Option 2-2-3>> The specific condition may be, for example, a trigger to report CSI using a specific method (e.g., periodic / non-periodic / semi-persistent).

[0242] The trigger may be signaled according to at least one of the methods described in Appendix 2 below.

[0243] <<Option 2-2-4>> The specific condition may be, for example, that the UE acquires / stores / possesses a specific number (e.g., K) of CSI-H.

[0244] The K may be set / indicated, for example, according to at least one of the methods described in Supplementary Note 2 below.

[0245] According to the second embodiment described above, it is possible to appropriately define UE settings and UE operations related to the second monitoring in the network.

[0246] Third Embodiment The third embodiment relates to a schedule and UE behavior for historical CSI reporting.

[0247] The UE may receive configuration / instructions regarding encoding / quantization / compression / reporting of historical GT CSI from the NW.

[0248] The settings / instructions may be transmitted, for example, according to at least one of the methods described in Supplementary Note 2 below.

[0249] The settings / instructions may include, for example, at least one of parameters regarding generation of compressed historical GT CSI feedback and reporting resources.

[0250] The UE may report CSI-H based on a specific trigger / configuration / instruction.

[0251] The particular trigger / setting / instruction may be performed, for example, according to at least one of the methods described in Appendix 2 below.

[0252] The specific trigger / setting / instruction may include, for example, information regarding at least one of the following: - Number of CSI-H to be reported; - Resources / channels for reporting; - Number of CSI-H reports per resource / channel; - Quantization / compression / encoding method for CSI-H and / or parameters for said method; - CSI report index / CSI resource index.

[0253] By notifying the UE of the above information, CSI-H can be reported using an appropriate number, appropriate channels / resources, appropriate quantization / compression / encoding methods, etc.

[0254] The UE may (for example, by default) report all CSI-Hs (all N CSI-Hs out of N stored CSI-Hs).

[0255] When the specific trigger / configuration / instruction does not include at least one of information on the number of CSI-Hs to be reported and information on the number of CSI-Hs to be reported for each resource / channel, the UE may report all CSI-Hs (all N CSI-Hs out of N stored CSI-Hs).When the specific trigger / configuration / instruction includes at least one of information on the number of CSI-Hs to be reported and information on the number of CSI-Hs to be reported for each resource / channel, the UE may report the number of CSI-Hs based on this information.

[0256] When a UE reports M CSI-Hs among N CSI-Hs stored therein, the UE may report the most recent M CSI-Hs.

[0257] The UE may report CSI-H using a specific method (eg, periodic / aperiodic / semi-persistent) according to the specific trigger / configuration / instruction.

[0258] The CSI reporting index may be included in the CSI-H report according to the specific trigger / configuration / instruction, and the UE may assume / expect that the corresponding CSI reporting configuration includes an instruction for reporting one or more PMIs.

[0259] The UE may not assume / expect to report CSI-H at multiple times.

[0260] The UE may assume / expect to report CSI-H at multiple times.

[0261] If there is no CSI-H available at the UE, the UE may not assume / expect to be triggered to report CSI-H.

[0262] According to the third embodiment described above, it is possible to appropriately define a schedule (trigger / configuration / instruction) for CSI reporting and corresponding UE operations.

[0263] Fourth Embodiment The fourth embodiment relates to the generation of historical CSI reports and UE operation.

[0264] The UE may separately / jointly encode / quantize / compress multiple GT CSIs using a particular method based on a particular configuration.

[0265] The particular method may be, for example, based on the Lempel-Ziv algorithm and / or a particular type (e.g., eType II) of extension using a common spatial domain / frequency domain vector and derivatives.

[0266] The UE may transmit coded / quantized / compressed historical GT CSI using resources configured / instructed using a specific method.

[0267] 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.

[0268] In the present disclosure, CSI, CSI contents, CSI elements, CSI sequences, CSI bits, and CSI information bits may be interpreted as interchangeable.

[0269] The UE may generate CSI according to at least one of options 4-1 to 4-4 below.

[0270] <<Option 4-1>> The UE may generate each CSI-H (CSI-H content) individually by scalar quantizing each element in the CSI-H with a specific bit width.

[0271] The particular bit width may be set / indicated, for example, according to at least one of the methods described in Supplementary Note 2 below.

[0272] <<Option 4-2>> The UE may generate each CSI-H (CSI-H content) individually using vector quantization.

[0273] For example, the UE may individually generate at least one feedback content of Type II, enhanced Type II (eType II), and enhanced Type II with enhanced parameter combinations (PC) for each CSI-H.

[0274] <<Option 4-3>> The UE may first apply options 4-1 / 4-2 above.

[0275] The UE may then compress multiple (e.g., all) CSI-H generated contents using the configured compression parameters, which may be, for example, Lempel-Ziv compression using the configured compression parameters (e.g., compression ratio).

[0276] The compression parameters may be set, for example, according to at least one of the methods described in Supplementary Note 2 below.

[0277] <Option 4-4> 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 (specific resource unit).

[0278] The UE may also divide the CSI-H reported in one resource (specific resource unit) into multiple groups, and may generate CSI-H (CSI-H content) for each group using a certain quantization / compression method.

[0279] The certain quantization / compression method may be, for example, a method that is extended from an existing type of CSI (e.g., Type 2 / Extended Type 2 CSI).

[0280] For example, the UE may first generate CSI feedback content with the set parameters (e.g., at least one CSI of Type 2, Extended Type 2, and Extended Type 2 with Extended Parameter Combination).

[0281] For example, the UE may use the CSI elements (e.g., i ｎ (n is 1 or 2)) may be generated.

[0282] The elements of the CSI (e.g., i ｎ (n is 1 or 2) may be used to represent one CSI-H or multiple CSI-Hs.

[0283] The elements of the CSI (e.g., i ｎ (n is 1 or 2)) is not used to represent one CSI-H (in other words, when the CSI element is used to represent multiple CSI-Hs), the UE may use one or a pair of differential values ​​(e.g., delta-i n (n is 1 or 2)) may be generated.

[0284] In addition, the UE may use the CSI element (e.g., i ｎ For each CSI-H other than the CSI-H directly represented by (n is 1 or 2), one or a pair of difference values ​​(e.g., delta-i n (n is 1 or 2)) may be generated.

[0285] For example, delta-i1 and delta-i2 are i １ and i ２ The difference of each CSI-H with respect to the common part may be expressed as:

[0286] These differential values ​​can be used to modify some of the spatial domain base vectors of the CSI-H, modify some of the frequency domain base vectors, or modify some of the combination coefficients.

[0287] For example, delta-i n is i ｎ In other words, for all CSI-H, some elements may be common, and other elements may be changeable using differential values.

[0288] The UE is ｎ (n is 1 or 2) and all delta-i n (n is 1 or 2) may be used to generate elements (final elements) of the CSI-H.

[0289] 11 is a diagram showing an example of generating elements of CSI according to option 4-4. In the example shown in Fig. 11, the UE generates CSI1 to CSI3 as multiple pieces of CSI.

[0290] In the example shown in Fig. 11, the UE generates PMI1 of CSI1 based on the codebook of extended type 2 (specified in Rel. 18 in the example shown in Fig. 11). １ = [i １，１ i １，２ i １，５ i １，６，１ i １，７，１ i １，８，１ ] and i２ = [i ２，３，１ i ２，４，１ i ２，５，１ ] is generated.

[0291] In the example shown in FIG. 11, the UE generates PMI2 for CSI2 by １ (In other words, the spatial domain / frequency domain base vectors and the reported coefficients are reused.) At this time, the UE reuses i of PMI1 in generating PMI2 of CSI2. ２ The difference value delta-i2 = [delta-i 2,3,1 delta-i 2,4,1 delta-i 2,5,1 ]. Note that the reused part does not need to be reported.

[0292] In this way, the i ２ By using the amplitude / phase difference value between the two, the number of bits for quantization can be reduced.

[0293] Furthermore, in the example shown in FIG. 11, the UE generates PMI3 for CSI3 by using [i １，１ i １，２ i １，５ i １，６，１ ] (in other words, the spatial domain / frequency domain base vectors are reused, and the reported coefficients are not reused). At this time, the UE reuses i of PMI1 in generating PMI2 of CSI2. １ [i] relating to １，７，１ i １，８，１ ]Difference value delta-i1=[delta-i 1,7,1 delta-i 1,8,1 ] and i ２ = [i ２，３，１ i ２，４，１ i ２，５，１ ]. Note that the reused part does not need to be reported.

[0294] The UE may report information (e.g., a bitmap) indicating which coefficients are to be reported in generating CSI 3. This configuration allows the amplitude / phase of new coefficients to be reported with higher resolution, similar to the extended type 2 codebook.

[0295] 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.

[0296] The further extension may support a value of N4 greater than the length of the Doppler domain (DD) / time domain (TD) basis vectors (DFT basis vectors) (also referred to as 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.

[0297] This further extension may also support a non-fixed duration in DD units (e.g., d), where the UE must report the actual duration d between CSI-H intervals as part of the report. ｓ may be reported.

[0298] Furthermore, the further extension may support a Q value greater than the number of DD basis vectors (which may also be referred to as Q) defined up to the existing specifications (e.g., Rel. 17 / 18). By configuring in this way, it is possible to support a larger number of Doppler domain base vectors, thereby improving the CSI accuracy.

[0299] According to the fourth embodiment described above, historical CSI reports can be generated appropriately.

[0300] <Variations> 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.

[0301] The UE may determine whether the conditions for the event trigger are met based on the conditions in the second embodiment.

[0302] For example, when a trigger condition is satisfied, the UE may report the CSI-H using a specific UL channel (e.g., PUSCH). In this case, the UE may report a scheduling request to request the UL channel resource, and then may report the CSI-H using the resource based on an instruction from the NW (e.g., (UL grant) DCI).

[0303] <Supplementary Notes> [Supplementary Note 1: AI Model Information] In the present disclosure, AI model information may refer to information including at least one of the following: - Input / output information of the AI ​​model. - Pre-processing / post-processing information for the input / output of the AI ​​model. - Parameter information of the AI ​​model. - Training information for the AI ​​model. - Inference information for the AI ​​model. - Performance information regarding the AI ​​model.

[0304] Here, the input / output information of the AI ​​model may include information on at least one of the following: - Contents of the input / output data (e.g., RSRP, SINR, amplitude / phase information in the channel matrix (or precoding matrix), information on the angle of arrival (Angle of Arrival (AoA)), information on the angle of departure (Angle of Departure (AoD)), location information); - Auxiliary information of the data (which may be called meta-information); - Type of the input / output data (e.g., immutable value, floating-point number); - Bit width of the input / output data (e.g., 64 bits for each input value); - Quantization interval (quantization step size) of the input / output data (e.g., 1 dBm for L1-RSRP); - Range that the input / output data can take (e.g., [0, 1]).

[0305] In the present disclosure, the information on AoA may include information on at least one of an azimuth angle of arrival and a zenith angle of arrival (ZoA). The information on AoD may include information on at least one of an azimuth angle of departure and a zenith angle of departure (ZoD).

[0306] In the present disclosure, location information may be location information related to a 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 about a BS neighboring (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., X / Y / Z axis coordinates), etc.), a specific address of the UE (e.g., an Internet Protocol (IP) address), etc. 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.

[0307] 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.).

[0308] The location information may include mobility information, which may include information indicating at least one of information indicating a mobility type, a moving speed of the UE, an acceleration of the UE, and a moving direction of the UE.

[0309] Here, the mobility type may correspond to at least one of a fixed location UE, a movable / moving UE, a no mobility UE, a low mobility UE, a middle mobility UE, a high mobility UE, a cell-edge UE, a not-cell-edge UE, etc.

[0310] In the present disclosure, environmental information (for data) may be information about 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.

[0311] Here, LOS may mean that the UE and the BS are in an environment where they can see each other (or there is no obstruction), and NLOS may mean that the UE and the BS are not in an environment where they can see each other (or there is an obstruction). The 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).

[0312] In the present disclosure, meta-information may mean, for example, information regarding input / output information suitable for an AI model, information regarding acquired / acquirable data, etc. Specifically, meta-information may include information regarding beams of RS (e.g., CSI-RS / SRS / SSB, etc.) (e.g., the pointing angle of each beam, the 3 dB beam width, the shape of the pointed beam, the number of beams), layout information of the gNB / UE antenna, frequency information, environmental information, meta-information ID, etc. Note that meta-information may be used as input / output of the AI ​​model.

[0313] The pre-processing / post-processing information for the input / output of the AI ​​model may include information on at least one of the following: Whether to apply normalization (e.g., Z-score normalization (standardization), min-max normalization); Parameters for normalization (e.g., mean / variance for Z-score normalization, min / max for min-max normalization); Whether to apply a specific numerical conversion method (e.g., one hot encoding, label encoding, etc.); Selection rules for whether to use as training data.

[0314] For example, Z-score normalization (x) is performed as a preprocessing step for input information x. ｎｅｗ = (x - μ) / σ, where μ is the mean of x and σ is the standard deviation) ｎｅｗ may be input to the AI ​​model, and the output y ｏｕｔ may be subjected to post-processing to obtain the final output y.

[0315] The information on the parameters of the AI ​​model may include information on at least one of the following: - Information on weights in the AI ​​model (e.g., neuron coefficients (connection coefficients)); - Structure of the AI ​​model; - Type of the 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)); - Function of the AI ​​model as a model component (e.g., decoder, encoder).

[0316] Note that the weight information in the AI ​​model may include information on at least one of the following: - Bit width (size) of the weight information; - Quantization interval of the weight information; - Granularity of the weight information; - Range that the weight information can take; - Weight parameters in the AI ​​model; - Information on the difference from the AI ​​model before update (if updating); - Weight initialization method (e.g., zero initialization, random initialization (based on normal distribution / uniform distribution / truncated normal distribution), Xavier initialization (for sigmoid function), He initialization (for rectified linear units (ReLU))).

[0317] The structure of the AI ​​model may also include information about at least one of the following: the number of layers, the type of layer (e.g., convolutional layer, activation layer, dense layer, normalization layer, pooling layer, attention layer), layer information, time series specific parameters (e.g., bidirectionality, time step), parameters for training (e.g., type of function (e.g., L2 regularization, dropout function, etc.), where (e.g., after which layer) to place this function).

[0318] The layer information may include information about at least one of the following: 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 function (Sigmoid, tanh function, ReLU, leaky ReLU information, Maxout, Softmax).

[0319] An AI model may be included as a component of another AI model, for example, an AI model that includes model component #1, ResNet, model component #2, a Transformer model, a dense layer, and a normalization layer in that order.

[0320] The training information for the AI ​​model may include information about 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.); - Information on the loss function (e.g., information on metrics of the loss function (Mean Absolute Error (MAE)), Mean Square Error (MSE), Cross Entropy Loss, NLL Loss, Kullback-Leibler (KL) Divergence, etc.)); - Parameters to be frozen for training (e.g., layers, weights); - Parameters to be updated (e.g., layers, weights); - Parameters to be (used as) initial parameters for training (e.g., layers, weights); - Method of training / updating the AI ​​model (e.g., (recommended) number of epochs, batch size, number of data to use for training).

[0321] The inference information for the AI ​​model may include information regarding decision tree branch pruning, parameter quantization, and functions of the AI ​​model, etc. Here, the functions 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.

[0322] An autoencoder for CSI feedback may be used as follows: The UE inputs the CSI / channel matrix / precoding matrix into the AI ​​model of the encoder and transmits the encoded bits as CSI feedback (CSI report). The BS inputs the received encoded bits into the AI ​​model of the decoder to reconstruct the CSI / channel matrix / precoding matrix, which is the output.

[0323] In spatial domain beam prediction, the UE / BS may input measurement results (beam quality, e.g., RSRP) based on sparse (or thick) beams into an AI model and output dense (or thin) beam quality.

[0324] In time domain beam prediction, 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.

[0325] The performance information about the AI ​​model may include information about the expected value of a loss function defined for the AI ​​model.

[0326] The AI ​​model information in the present disclosure may include information regarding the application range (applicable range) of the AI ​​model. The application range may be indicated by a physical cell ID, a serving cell index, etc. The information regarding the application range may be included in the above-mentioned environment information.

[0327] AI model information regarding a specific AI model may be predetermined in a standard or may be notified to a UE from a 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.

[0328] Note that the AI ​​model information in the present disclosure may include an index for identifying the AI ​​model (which may be referred to as, for example, 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 (for example, input / output information of the AI ​​model) may be predetermined in a standard, or may be notified to the UE from the NW.

[0329] The AI ​​model information in the present 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.

[0330] In the present disclosure, the model ID may be interchangeably read as an ID (model set ID) corresponding to a set of AI models. Furthermore, in the present disclosure, the model ID may be interchangeably read as a meta information ID. The meta information (or the meta information ID) may be associated with information about a beam (beam setting) as described above. For example, the meta information (or the 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. Furthermore, in the present disclosure, the meta information ID may be interchangeably read as an ID (meta information set ID) corresponding to a set of meta information.

[0331] [Supplementary Note 2: Notification of Information to UE] In the above-described embodiments, any information may be notified (from the NW) to the UE (in other words, reception of any information from the BS by 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.

[0332] When the notification is performed by a MAC CE, the MAC CE may be identified by including a new Logical Channel ID (LCID) in the MAC subheader, which is not defined in existing standards.

[0333] When the notification is made by 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.

[0334] Furthermore, notification of any information to the UE in the above embodiments may be performed periodically, semi-persistently, or aperiodically.

[0335] [Supplementary Note 3: Notification of Information from UE] In the above-described embodiments, notification of any information from the UE (to the NW) (in other words, transmission / report of any information from the UE to the BS) 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.

[0336] When the notification is performed by a MAC CE, the MAC CE may be identified by including a new LCID, which is not defined in existing standards, in the MAC subheader.

[0337] If the notification is made by UCI, the notification may be transmitted using PUCCH or PUSCH.

[0338] Furthermore, any information in the above-described embodiments may be notified from the UE periodically, semi-persistently, or aperiodically.

[0339] [Application of Each Embodiment] At least one of the above-described embodiments may be applied when a specific condition is met. The specific condition may be defined in a standard or may be notified to a UE / BS using higher layer signaling / physical layer signaling.

[0340] At least one of the above embodiments may be applied only to UEs that have reported or support specific UE capabilities, for example (the following is by way of example only): Supporting specific processing / operations / control / information for at least one of the above embodiments; Supporting AI / ML-based CSI reporting; Supporting (reporting of) performance monitoring based on a CSI framework; Supporting at least UE-side performance monitoring; Supporting combined UE-side and NW-side performance monitoring.

[0341] The particular UE capability may indicate support for particular processes / operations / controls / information for at least one of the above embodiments / options / choices.

[0342] Furthermore, the above-mentioned specific UE capability may be a capability that is applied across all frequencies (commonly regardless of frequency), or may be a capability for each frequency (e.g., one or a combination of a cell, a band, a band combination, a BWP, a component carrier, etc.), or may be a capability for each frequency range (e.g., Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or may be a capability for each subcarrier spacing (SubCarrier Spacing (SCS)), or may be a capability for each Feature Set (FS) or Feature Set Per Component-carrier (FSPC).

[0343] Furthermore, the specific UE capability may be a capability that is applied to all duplexing methods (commonly regardless of the duplexing method), or may be a capability for each duplexing method (e.g., Time Division Duplex (TDD) or Frequency Division Duplex (FDD)).

[0344] Furthermore, at least one of the above-described embodiments may be applied when the UE configures / activates / triggers specific information related to the above-described embodiments (or performs the operations of the above-described embodiments) through higher layer signaling / physical layer signaling. For example, the specific information may be information indicating that LCM based on a model / functionality ID is enabled, any RRC parameter for a specific release (e.g., Rel. 18 / 19 / 20), etc.

[0345] If the UE does not support at least one of the specific UE capabilities or is not configured with the specific information, the UE may apply, for example, the behavior of Rel. 15 / 16 / 17.

[0346] (Supplementary Notes) The following inventions are supplemented with respect to one embodiment of the present disclosure. [Supplementary Note A-1] A terminal having a receiving unit that receives a configuration for performance monitoring of artificial intelligence (AI)-based channel state information (CSI) reporting, and a control unit that controls at least one of AI-based CSI measurement and storage and generation of the CSI report based on the configuration. [Supplementary Note A-2] The terminal described in Supplementary Note A-1, wherein the control unit controls the measurement and storage of the CSI using reference signal resources that are arranged within a specific time window based on information regarding the specific window included in the configuration. [Supplementary Note A-3] The terminal described in Supplementary Note A-1 or Supplementary Note A-2, wherein the control unit controls the measurement and storage of the CSI based on information regarding a performance monitoring event included in the configuration. [Supplementary Note A-4] The terminal described in any of Supplements A-1 to A-3, wherein, when multiple CSIs are generated in generating the CSI report, the control unit generates the multiple CSIs using a CSI element indicated by an absolute value and a CSI element indicated by a differential value. [Supplementary Note B-1] A terminal having a controller that controls transmission of a request for network-side performance monitoring related to artificial intelligence (AI)-based channel state information (CSI) reporting, and a receiver that receives an instruction related to the CSI reporting, the instruction being transmitted based on the configuration. [Supplementary Note B-2] The terminal according to Supplementary Note B-1, wherein the request is information indicating that a condition related to the network-side monitoring has been met, or information indicating whether the network-side monitoring is necessary. [Supplementary Note B-3] The terminal according to Supplementary Note B-1 or Supplementary Note B-2, wherein the controller determines to transmit the request when a specific condition is met. [Supplementary Note B-4] The terminal according to any one of Supplements B-1 to 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 CSI, an index related to CSI reporting, and an index related to CSI resources.

[0347] (Wireless Communication System) The configuration of a wireless communication system according to an embodiment of the present disclosure will be described below. In this wireless communication system, communication is performed using any one of the wireless communication methods according to the above embodiments of the present disclosure or a combination thereof.

[0348] 12 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment. The wireless communication system 1 (which may be simply referred to as system 1) may be a system that realizes communication using Long Term Evolution (LTE) or 5th generation mobile communication system New Radio (5G NR) specified by the Third Generation Partnership Project (3GPP).

[0349] 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.

[0350] In EN-DC, the LTE (E-UTRA) base station (eNB) is the master node (Master Node (MN)), and the NR base station (gNB) is the secondary node (Secondary Node (SN)). In NE-DC, the NR base station (gNB) is the MN, and the LTE (E-UTRA) base station (eNB) is the SN.

[0351] The wireless communication system 1 may support dual connectivity between multiple base stations within the same RAT (for example, dual connectivity in which both the MN and SN are NR base stations (gNBs) (NR-NR Dual Connectivity (NN-DC))).

[0352] 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 located within the macrocell C1 and form small cells C2 that are smaller than the macrocell C1. A user terminal 20 may be located within at least one of the cells. The locations and numbers of the cells and user terminals 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.

[0353] 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 (CCs) and dual connectivity (DC).

[0354] 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)). The macro cell C1 may be included in FR1, and the small cell C2 may be included in FR2. For example, FR1 may be a frequency band of 6 GHz or less (sub-6 GHz), and FR2 may be a frequency band higher than 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.

[0355] Furthermore, the user terminal 20 may perform communication using at least one of time division duplex (TDD) and frequency division duplex (FDD) in each CC.

[0356] The multiple base stations 10 may be connected by wire (e.g., optical fiber compliant with the Common Public Radio Interface (CPRI), an X2 interface, etc.) or wirelessly (e.g., NR communication). For example, when NR communication is used as a backhaul between the base stations 11 and 12, the base station 11 corresponding to the upper station may be called an Integrated Access Backhaul (IAB) donor, and the base station 12 corresponding to the relay station (relay) may be called an IAB node.

[0357] The base station 10 may be connected to the core network 30 directly or via another base station 10. The core network 30 may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), a Next Generation Core (NGC), and the like.

[0358] The core network 30 may include network functions (Network Functions (NF)) such as 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). A single network node may provide multiple functions. Communication with an external network (e.g., the Internet) may also be performed via the DN.

[0359] The user terminal 20 may be a terminal that supports at least one of communication methods such as LTE, LTE-A, and 5G.

[0360] An Orthogonal Frequency Division Multiplexing (OFDM)-based radio access scheme may be used in the wireless communication system 1. For example, Cyclic Prefix OFDM (CP-OFDM), Discrete Fourier Transform Spread OFDM (DFT-s-OFDM), Orthogonal Frequency Division Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-FDMA), or the like may be used in at least one of the downlink (DL) and uplink (UL).

[0361] The radio access scheme may also be called a waveform. Note that in the wireless communication system 1, other radio access schemes (e.g., other single-carrier transmission schemes, other multi-carrier transmission schemes) may be used as the UL and DL radio access schemes.

[0362] In the wireless communication system 1, 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.

[0363] Furthermore, in the wireless communication system 1, 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)), or the like may be used as an uplink channel.

[0364] The PDSCH transmits user data, higher layer control information, a System Information Block (SIB), etc. The PUSCH may transmit user data, higher layer control information, etc. Furthermore, the PBCH may transmit a Master Information Block (MIB).

[0365] Lower layer control information may be transmitted by the PDCCH. The lower layer control information may include, for example, Downlink Control Information (DCI) including scheduling information for at least one of the PDSCH and the PUSCH.

[0366] Note that the DCI for scheduling the PDSCH may be referred to as a DL assignment, a DL DCI, etc., and the DCI for scheduling the PUSCH may be referred to as a UL grant, a UL DCI, etc. Note that the PDSCH may be replaced with DL data, and the PUSCH may be replaced with UL data.

[0367] A control resource set (CORESET) and a search space may be used to detect the PDCCH. The CORESET corresponds to resources for searching for DCI. The search space corresponds to a search region and a search method for PDCCH candidates. One CORESET may be associated with one or more search spaces. The UE may monitor the CORESET associated with a certain search space based on the search space configuration.

[0368] One search space may correspond to PDCCH candidates corresponding to one or more aggregation levels. One or more search spaces may be referred to as a search space set. Note that the terms "search space," "search space set," "search space configuration," "search space set configuration," "CORESET," "CORESET configuration," and the like in the present disclosure may be read interchangeably.

[0369] 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 scheduling request (SR). The PRACH may transmit a random access preamble for establishing a connection with a cell.

[0370] In the present disclosure, downlink, uplink, etc. may be expressed without adding "link." Also, various channels may be expressed without adding "Physical" to the beginning.

[0371] In the wireless communication system 1, a synchronization signal (SS), a downlink reference signal (DL-RS), etc. may be transmitted. In the wireless communication system 1, as the DL-RS, 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.

[0372] 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 the PBCH) may be referred to as an SS / PBCH block, an SS Block (SSB), or the like. Note that the SS, SSB, and the like may also be referred to as a reference signal.

[0373] Furthermore, in the wireless communication system 1, a sounding reference signal (SRS), a demodulation reference signal (DMRS), or the like may be transmitted as an uplink reference signal (UL-RS). Note that the DMRS may also be called a user equipment-specific reference signal (UE-specific reference signal).

[0374] (Base Station) Fig. 13 is a diagram showing an example of the configuration of a base station according to an embodiment. The base station 10 includes a control unit 110, a transceiver unit 120, a transceiver antenna 130, and a transmission line interface 140. Note that the base station may include one or more of each of the control unit 110, the transceiver unit 120, the transceiver antenna 130, and the transmission line interface 140.

[0375] In this example, the functional blocks of the characteristic parts of the present embodiment are mainly shown, and it may be assumed that the base station 10 also has other functional blocks necessary for wireless communication. Some of the processing of each unit described below may be omitted.

[0376] The control unit 110 performs overall control of the base station 10. The control unit 110 can be configured from a controller, a control circuit, and the like that are explained based on common understanding in the technical field to which the present disclosure relates.

[0377] 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 (setting up, releasing, etc.) of communication channels, status management of the base station 10, management of radio resources, etc.

[0378] 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 configured with 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 common understanding in the technical field related to the present disclosure.

[0379] The transmitting / receiving unit 120 may be configured as an integrated transmitting / receiving unit, or may be configured from a transmitting unit and a receiving unit. The transmitting unit may be configured from a transmission processing unit 1211 and an RF unit 122. The receiving unit may be configured from a reception processing unit 1212, the RF unit 122, and a measurement unit 123.

[0380] The transmitting and receiving antenna 130 can be configured from an antenna described based on common understanding in the technical field to which the present disclosure relates, such as an array antenna.

[0381] 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.

[0382] The transceiver 120 may form at least one of the transmit beam and the receive beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.

[0383] The transmitter / receiver unit 120 (transmission processing unit 1211) 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, control information, etc. obtained from the control unit 110, and generate a bit string to be transmitted.

[0384] The transmitter / receiver unit 120 (transmission processing unit 1211) 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.

[0385] The transceiver unit 120 (RF unit 122) 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.

[0386] On the other hand, the transceiver unit 120 (RF unit 122) may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency band signal received by the transceiver antenna 130.

[0387] The transceiver 120 (reception processing unit 1212) 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, thereby acquiring user data, etc.

[0388] The transceiver 120 (measurement unit 123) may perform measurements on the received signal. For example, 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. The measurement results may be output to the control unit 110.

[0389] The transmission path interface 140 may transmit and receive signals (backhaul signaling) between devices included in the core network 30 (e.g., network nodes that provide 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.

[0390] The transmitting section and receiving section of the base station 10 in the present disclosure may be configured by at least one of the transmitting / receiving section 120, the transmitting / receiving antenna 130, and the transmission path interface 140.

[0391] The transceiver 120 may transmit a configuration for monitoring performance of an artificial intelligence (AI)-based channel state information (CSI) report. The controller 110 may use the configuration to instruct at least one of measuring and storing AI-based CSI and generating the CSI report (first / fourth embodiments).

[0392] The control unit 110 may control reception of a request for network-side performance monitoring regarding artificial intelligence (AI)-based channel state information (CSI) reporting, and the transceiver unit 120 may transmit an instruction regarding the CSI reporting based on the configuration (second / third embodiment).

[0393] (User Terminal) Fig. 14 is a diagram showing an example of the configuration of a user terminal according to one embodiment. The user terminal 20 includes a control unit 210, a transceiver unit 220, and a transceiver antenna 230. Note that the user terminal 20 may include one or more of each of the control unit 210, the transceiver unit 220, and the transceiver antenna 230.

[0394] In this example, the functional blocks of the characteristic parts of the present embodiment are mainly shown, and it may be assumed that the user terminal 20 also has other functional blocks necessary for wireless communication. Some of the processing of each unit described below may be omitted.

[0395] The control unit 210 performs overall control of the user terminal 20. The control unit 210 can be configured from a controller, a control circuit, etc., which are described based on common understanding in the technical field to which the present disclosure relates.

[0396] The control unit 210 may control signal generation, mapping, etc. The control unit 210 may control transmission and reception, measurement, etc. using the transceiver unit 220 and the transceiver antenna 230. The control unit 210 may generate data, control information, sequences, etc. to be transmitted as signals and transfer them to the transceiver unit 220.

[0397] 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 configured with 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 common understanding in the technical field related to the present disclosure.

[0398] The transmitting / receiving unit 220 may be configured as an integrated transmitting / receiving unit, or may be composed of a transmitting unit and a receiving unit. The transmitting unit may be composed of a transmission processing unit 2211 and an RF unit 222. The receiving unit may be composed of a reception processing unit 2212, an RF unit 222, and a measurement unit 223.

[0399] The transmitting / receiving antenna 230 can be configured from an antenna described based on common understanding in the technical field to which the present disclosure relates, such as an array antenna.

[0400] 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.

[0401] The transceiver unit 220 may form at least one of the transmit beam and the receive beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.

[0402] The transceiver unit 220 (transmission processing unit 2211) may perform PDCP layer processing, RLC layer processing (e.g., RLC retransmission control), MAC layer processing (e.g., HARQ retransmission control), etc. on data, control information, etc. obtained from the control unit 210, and generate a bit string to be transmitted.

[0403] The transmitter / receiver unit 220 (transmission processing unit 2211) 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.

[0404] Whether or not to apply DFT processing may be based on the setting of transform precoding. When transform precoding is enabled for a certain channel (e.g., PUSCH), the transceiver unit 220 (transmission processing unit 2211) may perform DFT processing as the transmission processing to transmit the channel using a DFT-s-OFDM waveform, and if not, it may not be necessary to perform DFT processing as the transmission processing.

[0405] The transceiver unit 220 (RF unit 222) 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.

[0406] On the other hand, the transceiver unit 220 (RF unit 222) may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency band signal received by the transceiver antenna 230.

[0407] The transceiver unit 220 (reception processing unit 2212) 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, and acquire user data, etc.

[0408] The transceiver 220 (measurement unit 223) 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.

[0409] The measurement unit 223 may derive channel measurements for CSI calculation based on the channel measurement resources. The channel measurement resources may be, for example, non-zero power (NZP) CSI-RS resources. The measurement unit 223 may also derive interference measurements for CSI calculation based on the interference measurement resources. The interference measurement resources may be at least one of an NZP CSI-RS resource for interference measurement, a CSI-Interference Measurement (IM) resource, etc. Note that CSI-IM may be referred to as CSI-Interference Management (IM) or may be interchangeably read as Zero Power (ZP) CSI-RS. Note that in the present disclosure, CSI-RS, NZP CSI-RS, ZP CSI-RS, CSI-IM, CSI-SSB, etc. may be interchangeably read as interchangeable.

[0410] The transmitting unit and receiving unit of the user terminal 20 in the present disclosure may be configured by at least one of the transmitting / receiving unit 220 and the transmitting / receiving antenna 230.

[0411] The transceiver 220 may receive a configuration for monitoring performance of an artificial intelligence (AI)-based channel state information (CSI) report, and the controller 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 embodiments).

[0412] The control unit 210 may control the measurement and storage of the CSI using reference signal resources arranged within a specific time window based on information regarding the specific time window included in the configuration (first embodiment).

[0413] 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).

[0414] When multiple CSIs are generated in generating the CSI report, the control unit 210 may generate the multiple CSIs using elements of CSI represented by absolute values ​​and elements of CSI represented by differential values ​​(fourth embodiment).

[0415] The control unit 210 may control transmission of a request for network-side performance monitoring regarding artificial intelligence (AI)-based channel state information (CSI) reporting, and the transceiver unit 220 may receive an instruction regarding the CSI reporting, which is transmitted based on the configuration (second / third embodiment).

[0416] The request may be information indicating that a condition regarding the network-side monitoring has been met, or information indicating whether the network-side monitoring is necessary (second embodiment).

[0417] The control unit 210 may determine to send the request when a specific condition is met (second embodiment).

[0418] The instruction may include at least one of information regarding the number of CSI to be reported, information regarding at least one of resources and channels for reporting, information regarding the number of CSI 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).

[0419] (Hardware Configuration) Note that the block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wires, wirelessly, etc.) and these multiple devices. The functional block may be realized by combining software with the single device or the multiple devices.

[0420] Here, the functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, deeming, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission may be called a transmitting unit, transmitter, etc. As described above, the implementation method of each is not particularly limited.

[0421] For example, a base station, a user terminal, etc. according to an 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 illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment. The above-described 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.

[0422] In the present disclosure, the terms apparatus, circuit, device, section, unit, etc. may be used interchangeably. 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 drawings, or may be configured to exclude some of the devices.

[0423] For example, although only one processor 1001 is shown, there may be multiple processors. Furthermore, processing may be performed by one processor, or processing may be performed by two or more processors simultaneously, serially, or in other ways. Furthermore, processor 1001 may be implemented by one or more chips.

[0424] Each function in the base station 10 and the user terminal 20 is realized, for example, by loading specified software (programs) onto hardware such as a processor 1001 and a memory 1002, causing the processor 1001 to perform calculations, control communication via the communication device 1004, and control at least one of reading and writing data in the memory 1002 and the storage 1003.

[0425] The processor 1001, for example, runs an operating system to control the entire computer. The processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, at least a part of the above-mentioned control unit 110 (210), transceiver unit 120 (220), etc. may be realized by the processor 1001.

[0426] The processor 1001 also reads 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 in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the control unit 110 (210) may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and the other functional blocks may be implemented in a similar manner.

[0427] The memory 1002 is a computer-readable recording medium and may be configured by at least one of, for example, Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically EEPROM (EEPROM), Random Access Memory (RAM), or other suitable storage medium. The memory 1002 may also be referred to as a register, cache, main memory, etc. The memory 1002 may store executable programs (program codes), software modules, etc. for implementing a wireless communication method according to an embodiment of the present disclosure.

[0428] Storage 1003 is a computer-readable recording medium and may be composed of at least one of, for example, a flexible disk, a floppy disk, a magneto-optical disk (e.g., a compact disc (e.g., a Compact Disc ROM (CD-ROM)), a digital versatile disc, a Blu-ray disc), 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.

[0429] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the above-mentioned transmission / reception unit 120 (220), transmission / reception antenna 130 (230), etc. may be realized by the communication device 1004. The transmission / reception unit 120 (220) may be implemented as a transmission unit 120a (220a) and a reception unit 120b (220b) that are physically or logically separated.

[0430] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, a light emitting diode (LED) lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0431] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0432] Furthermore, 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 this hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0433] (Modifications) Note that terms described in the present disclosure and terms necessary for understanding the present disclosure may be replaced with terms having the same or similar meanings. For example, a channel, a symbol, and a signal (signal or signaling) may be interchangeable. A signal may also be a message. A reference signal may be abbreviated as RS, and may also be called a pilot, pilot signal, etc. depending on the applicable standard. A component carrier (CC) may also be called a cell, frequency carrier, carrier frequency, etc.

[0434] A radio frame may be composed of one or more periods (frames) in the time domain. Each of the one or more periods (frames) constituting a radio frame may be called a subframe. Furthermore, a subframe may be composed of one or more slots in the time domain. A subframe may have a fixed time length (e.g., 1 ms) that is independent of numerology.

[0435] Here, the numerology may be a communication parameter applied to at least one of transmission and reception of a signal or channel, and may indicate at least one of, for example, Subcarrier Spacing (SCS), bandwidth, symbol length, cyclic prefix length, Transmission Time Interval (TTI), number of symbols per TTI, radio frame structure, specific filtering performed by a transceiver in the frequency domain, and specific windowing performed by a transceiver in the time domain.

[0436] A slot may be composed of one or more symbols (such as an Orthogonal Frequency Division Multiplexing (OFDM) symbol or a Single Carrier Frequency Division Multiple Access (SC-FDMA) symbol) in the time domain. A slot may also be a time unit based on numerology.

[0437] 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.

[0438] A radio frame, a subframe, a slot, a minislot, and a symbol all represent time units for transmitting signals. The radio frame, the subframe, the slot, the minislot, and the symbol may be referred to by other names corresponding to the radio frame, the subframe, the slot, the minislot, and the symbol. Note that the time units such as a frame, a subframe, a slot, a minislot, and a symbol in the present disclosure may be interchangeable.

[0439] For example, one subframe may be referred to as a TTI, or multiple consecutive subframes may be referred to as a TTI, or one slot or one minislot may be referred to as a TTI. That is, at least one of the subframe and the TTI may be a subframe (1 ms) in existing LTE, a period shorter than 1 ms (for example, 1-13 symbols), or a period longer than 1 ms. Note that the unit representing the TTI may be called a slot, minislot, etc. instead of a subframe.

[0440] Here, TTI refers to, for example, the smallest time unit for scheduling in wireless communication. For example, in an LTE system, a base station performs scheduling to allocate radio resources (such as frequency bandwidth and transmission power that can be used by each user terminal) to each user terminal in TTI units. Note that the definition of TTI is not limited to this.

[0441] The TTI may be a transmission time unit for a channel-encoded data packet (transport block), a code block, a code word, etc., or may be a processing unit for scheduling, link adaptation, etc. When a TTI is given, the time interval (e.g., the number of symbols) to which a transport block, a code block, a code word, etc. is actually mapped may be shorter than the TTI.

[0442] When one slot or one minislot is called a TTI, one or more TTIs (i.e., one or more slots or one or more minislots) may be the minimum time unit for scheduling. Also, the number of slots (minislots) constituting the minimum time unit for scheduling may be controlled.

[0443] A TTI having a time length of 1 ms may be called a regular TTI (TTI in 3GPP Rel. 8-12), normal TTI, long TTI, regular subframe, normal subframe, long subframe, slot, etc. A TTI shorter than a regular TTI may be called a shortened TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, minislot, subslot, slot, etc.

[0444] In addition, 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, and a short TTI (e.g., a shortened TTI, etc.) may be interpreted as a TTI having a TTI length shorter than the TTI length of a long TTI and greater than or equal to 1 ms.

[0445] A resource block (RB) is a resource allocation unit in the time domain and the 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, for example, 12. The number of subcarriers included in an RB may be determined based on numerology.

[0446] In addition, an RB may include one or more symbols in the time domain and may have a length of one slot, one minislot, one subframe, or one TTI, each of which may be composed of one or more resource blocks.

[0447] In addition, 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.

[0448] Furthermore, a resource block may be composed of one or more resource elements (REs). For example, one RE may be a radio resource region of one subcarrier and one symbol.

[0449] A Bandwidth Part (BWP), 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 their index relative to a Common Reference Point of the carrier. PRBs may be defined in a BWP and numbered within the BWP.

[0450] The BWP may include a UL BWP (BWP for UL) and a DL BWP (BWP for DL). One or more BWPs may be configured for a UE within one carrier.

[0451] 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. Note that the terms "cell," "carrier," etc. in this disclosure may be read as "BWP."

[0452] The above-described structures of radio frames, subframes, slots, minislots, symbols, etc. are merely examples. For example, the number of subframes included in a radio frame, the number of slots per subframe or radio frame, the number of minislots included in a slot, the number of symbols and RBs included in a slot or minislot, the number of subcarriers included in an RB, the number of symbols in a TTI, the symbol length, the cyclic prefix (CP) length, etc. may be changed in various ways.

[0453] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by a predetermined index.

[0454] The names used for parameters and the like in this disclosure are not intended to be limiting in any way. Furthermore, the mathematical expressions and the like using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0455] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0456] Furthermore, 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.

[0457] Input and output information, signals, etc. may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information, signals, etc. may be overwritten, updated, or added. Output information, signals, etc. may be deleted. Input information, signals, etc. may be transmitted to another device.

[0458] The notification of information is not limited to the aspects / embodiments described in the present disclosure, and may be performed using other methods. For example, the notification of information in the present disclosure may be performed by physical layer signaling (e.g., Downlink Control Information (DCI) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB) and System Information Block (SIB)), Medium Access Control (MAC) signaling), other signals, or a combination thereof.

[0459] Note that the physical layer signaling may be referred to as Layer 1 / Layer 2 (L1 / L2) control information (L1 / L2 control signal), L1 control information (L1 control signal), etc. Furthermore, the RRC signaling may be referred to as an RRC message, such as an RRC Connection Setup message or an RRC Connection Reconfiguration message. Furthermore, the MAC signaling may be notified using, for example, a MAC Control Element (CE).

[0460] Furthermore, notification of specified information (e.g., notification that "it is X") is not limited to explicit notification, but may be made implicitly (e.g., by not notifying the specified information or by notifying other information).

[0461] The determination may be made by a value represented by one bit (0 or 1), by a Boolean value represented by true or false, or by a comparison of numerical values ​​(e.g., comparison with a predetermined value).

[0462] 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.

[0463] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), these wired and / or wireless technologies are included within the definition of transmission media.

[0464] As used in this disclosure, the terms "system" and "network" may be used interchangeably. A "network" may refer to devices included in the network (e.g., base stations).

[0465] In this disclosure, terms such as "precoding," "precoder," "weight (precoding weight)," "Quasi-Co-Location (QCL)," "Transmission Configuration Indication state (TCI state)," "spatial relation," "spatial domain filter," "transmit power," "phase rotation," "antenna port," "layer," "number of layers," "rank," "resource," "resource set," "beam," "beam width," "beam angle," "antenna," "antenna element," "panel," "UE panel," "transmitting entity," "receiving entity," etc. may be used interchangeably.

[0466] In the present disclosure, the term "antenna port" may be interchangeably read as an antenna port for any signal / channel (e.g., a demodulation reference signal (DMRS) port). In the present disclosure, the term "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. Furthermore, the spatial domain transmission filter may include at least one of a spatial domain transmission filter and a spatial domain reception filter.

[0467] The group 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, and the like.

[0468] In addition, in the present disclosure, beam, SRS Resource Indicator (SRI), CORESET, CORESET pool, PDSCH, PUSCH, codeword (CW), transport block (TB), RS, etc. may be read as interchangeable terms.

[0469] In addition, in the present disclosure, the terms TCI state, downlink TCI state (DL TCI state), uplink TCI state (UL TCI state), unified TCI state, common TCI state, joint TCI state, etc. may be read interchangeably.

[0470] Furthermore, in the present disclosure, terms such as "QCL," "QCL assumption," "QCL relationship," "QCL type information," "QCL property / properties," "specific QCL type (e.g., Type A, Type D) property," and "specific QCL type (e.g., Type A, Type D)" may be interchangeable.

[0471] In the present disclosure, terms such as index, identifier (ID), indicator, indication, and resource ID may be interchangeable. In the present disclosure, terms such as sequence, list, set, group, cluster, and subset may be interchangeable.

[0472] Furthermore, the spatial relationship information identifier (ID) (TCI state ID) and the spatial relationship information (TCI state) may be interchangeable. The "spatial relationship information (TCI state)" may be interchangeable with "set of spatial relationship information (TCI state)", "one or more pieces of spatial relationship information", etc. The TCI state and the TCI may be interchangeable. The spatial relationship information and the spatial relationship may be interchangeable.

[0473] In the present disclosure, terms such as "base station (BS)," "radio base station," "fixed station," "NodeB," "eNB (eNodeB)," "gNB (gNodeB)," "access point," "transmission point (TP)," "reception point (RP)," "transmission / reception point (TRP)," "panel," "cell," "sector," "cell group," "carrier," "component carrier," etc. may be used interchangeably. Base stations may also be referred to by terms such as macrocell, small cell, femtocell, picocell, etc.

[0474] A base station can accommodate one or more (e.g., three) cells. When a base station accommodates multiple cells, the overall coverage area of ​​the base station can be partitioned into multiple smaller areas, and each smaller area can be provided with communication service by a base station subsystem (e.g., a small indoor base station (Remote Radio Head (RRH))). The terms "cell" or "sector" refer to part or all of the coverage area of ​​a base station and / or base station subsystem that provides communication service within that coverage.

[0475] In the present disclosure, 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.

[0476] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," etc. may be used interchangeably.

[0477] 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.

[0478] 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. Note that at least one of the base station and the mobile station may be a device mounted on a moving object, the moving object itself, etc.

[0479] The mobile body is a movable object that can move at any speed and naturally includes cases where the mobile body is stationary. Examples of the mobile body include, but are not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcars, rickshaws, ships and other watercraft, airplanes, rockets, satellites, drones, multicopters, quadcopters, balloons, and objects mounted thereon. The mobile body may also be a mobile body that moves autonomously based on an operation command.

[0480] The mobile object may be a vehicle (e.g., a car, an airplane, etc.), an unmanned mobile object (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned). Note that at least one of the base station and the mobile station may also include devices that do not necessarily move during communication operations. For example, at least one of the base station and the mobile station may be an Internet of Things (IoT) device such as a sensor.

[0481] 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, axles 48, an electronic control unit 49, various sensors (including a current sensor 50, an RPM 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.

[0482] The drive unit 41 is configured with 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 handle) 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 a user.

[0483] The electronic control unit 49 is composed of a microprocessor 61, memory (ROM, RAM) 62, and a communication port (for example, 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).

[0484] The signals from the various sensors 50-58 include a current signal from a current sensor 50 that senses the current of the motor, a rotation speed signal of the front wheels 46 / rear wheels 47 obtained by a rotation speed sensor 51, an air pressure signal of the front wheels 46 / rear wheels 47 obtained by an air pressure sensor 52, a vehicle speed signal obtained by a vehicle speed sensor 53, an acceleration signal obtained by an acceleration sensor 54, a depression amount signal of the accelerator pedal 43 obtained by an accelerator pedal sensor 55, a depression amount signal of the brake pedal 44 obtained by a brake pedal sensor 56, an operation signal of the shift lever 45 obtained by a shift lever sensor 57, and a detection signal for detecting obstacles, vehicles, pedestrians, etc. obtained by an object detection sensor 58.

[0485] The information service unit 59 is composed of various devices, such as a car navigation system, an audio system, speakers, a display, a television, and a radio, for providing (outputting) various information such as driving information, traffic information, and entertainment information, and one or more ECUs for controlling these devices. The information service unit 59 uses information acquired from external devices via the communication module 60 or the like to provide various information / services (e.g., multimedia information / multimedia services) to the occupants of the vehicle 40.

[0486] The information service unit 59 may include input devices (e.g., keyboards, mice, microphones, switches, buttons, sensors, touch panels, etc.) that accept input from the outside, and may also include output devices (e.g., displays, speakers, LED lamps, touch panels, etc.) that output to the outside.

[0487] The driving assistance system unit 64 includes various devices for providing functions to prevent accidents and reduce the driver's driving burden, such as millimeter-wave radar, Light Detection and Ranging (LiDAR), cameras, positioning locators (e.g., Global Navigation Satellite System (GNSS)), map information (e.g., High Definition (HD) maps, Autonomous Vehicle (AV) maps), gyro systems (e.g., Inertial Measurement Units (IMUs), Inertial Navigation Systems (INSs)), artificial intelligence (AI) chips, and AI processors, as well as 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 driving assistance functions or autonomous driving functions.

[0488] The communication module 60 can communicate with the microprocessor 61 and components of the vehicle 40 via the communication port 63. For example, the communication module 60 transmits and receives data (information) via the communication port 63 to and from 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, which are provided in the vehicle 40.

[0489] 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 base station 10 or the user terminal 20 described above. Furthermore, the communication module 60 may be, for example, at least one of the base station 10 and the user terminal 20 described above (or may function as at least one of the base station 10 and the user terminal 20).

[0490] The communication module 60 may transmit at least one of signals from the above-mentioned various sensors 50-58 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. For example, the PUSCH transmitted by the communication module 60 may include information based on the above-mentioned input.

[0491] The communication module 60 receives various information (traffic information, traffic 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 received by the communication module 60 (or data / information decoded from the PDSCH)).

[0492] Furthermore, the communication module 60 stores various information received from external devices in a memory 62 that can be used by the microprocessor 61. Based on the information stored in the 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 in the vehicle 40.

[0493] Furthermore, a base station in the present disclosure may be read as a user terminal. For example, the aspects / embodiments 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) or Vehicle-to-Everything (V2X)). In this case, the user terminal 20 may be configured to have the functions of the base station 10 described above. Furthermore, terms such as "uplink" and "downlink" may be read as terms corresponding to terminal-to-terminal communication (for example, "sidelink"). For example, terms such as an uplink channel and a downlink channel may be read as a sidelink channel.

[0494] Similarly, the user terminal in the present disclosure may be read as a base station, in which case the base station 10 may be configured to have the functions of the user terminal 20 described above.

[0495] In the present disclosure, an operation described as being performed by a base station may be performed by its upper node in some cases. It is apparent that in a network including one or more network nodes having a base station, various operations performed for communication with a terminal may be performed by the base station, one or more network nodes other than the base station (such as, but not limited to, a Mobility Management Entity (MME), a Serving-Gateway (S-GW), etc.), or a combination thereof.

[0496] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Furthermore, the order of the processing procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0497] Each aspect / embodiment described in the present disclosure may be a technology other than Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 6th generation mobile communication system (6G), xth generation mobile communication system (xG (x is, for example, an integer or decimal number)), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.17 (WiMAX (registered trademark)), IEEE 802.19 (WiMAX (registered trademark)), IEEE 802.20 (WiMAX (registered trademark)), IEEE 802.21 (Wi-Fi (registered trademark)), IEEE 802.22 (WiMAX (registered trademark)), IEEE 802.23 (WiMAX (registered trademark)), IEEE 802.24 (WiMAX (registered trademark)), IEEE 802.25 (WiMAX (registered trademark)), IEEE 802.26 (WiMAX (registered trademark)), IEEE 802.27 (WiMAX (registered trademark)), IEEE 802.28 (WiMAX (registered trademark)), IEEE 802.29 (WiMAX (registered trademark)), IEEE 802.30 (WiMAX (registered trademark)), IEEE 802.31 (Wi-Fi (registered trademark)), IEEE 802.32 (WiMAX (registered trademark)), IEEE 802.33 (WiMAX (registered trademark)), IEEE 802. The present invention may be applied to systems that use IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), or other suitable wireless communication methods, or to next-generation systems that are expanded, modified, created, or defined based on these. Furthermore, the present invention may be applied to a combination of multiple systems (e.g., a combination of LTE or LTE-A and 5G).

[0498] As used in this disclosure, 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."

[0499] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. 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 a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0500] The term "determining" as used in this disclosure 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 up in a table, database, or another data structure), ascertaining, etc.

[0501] Additionally, "determining" may be considered to be "determining" receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), etc.

[0502] Furthermore, "determination" may be considered to be "determining" resolving, selecting, choosing, establishing, comparing, etc. In other words, "determination" may be considered to be "determining" some kind of action. In the present disclosure, "determination" may be read interchangeably with the above-mentioned actions.

[0503] Furthermore, in this disclosure, "determine / determining" may be interchangeably read as "assume / assuming," "expect / expecting," "consider / considering," etc. Furthermore, in this disclosure, "does not expect to do..." may be interchangeably read as "assumes not to do...."

[0504] In the present disclosure, "expect" may be interchangeably read as "be expected." For example, "expect(s) ..." ("..." may be expressed, for example, as a that clause, a to-infinitive, etc.) may be interchangeably read as "be expected ...." "does not expect ..." may be interchangeably read as "be not expected ...." Furthermore, "An apparatus A is not expected ..." may be interchangeably read as "an apparatus B other than apparatus A does not expect ... from apparatus A" (e.g., if apparatus A is a UE, apparatus B may be a base station).

[0505] The "maximum transmit power" in this disclosure may mean the maximum value of transmit power, the nominal UE maximum transmit power, or the rated UE maximum transmit power.

[0506] As used in this disclosure, the terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access."

[0507] In this disclosure, when two elements are connected, they may be considered to be "connected" or "coupled" to one another using one or more wires, cables, printed electrical connections, etc., as well as using electromagnetic energy having wavelengths in the radio frequency range, microwave range, light (both visible and invisible) range, etc., as some non-limiting and non-exhaustive examples.

[0508] In the present disclosure, the term "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 "coupled" may also be interpreted in the same way as "different."

[0509] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0510] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0511] In the present disclosure, terms such as "less than or equal to," "less than," "greater than," "more than," "equal to," etc. may be interchangeable. Furthermore, in the present disclosure, terms meaning "good," "bad," "big," "small," "high," "low," "fast," "slow," "wide," "narrow," etc. may be interchangeable, not limited to the positive, comparative, and superlative. Furthermore, in the present disclosure, terms meaning "good," "bad," "big," "small," "high," "low," "fast," "slow," "wide," "narrow," etc. may be interchangeable, not limited to the positive, comparative, and superlative, as expressions with "i-th" (i is an arbitrary integer) attached (for example, "highest" may be interchangeable with "i-th highest").

[0512] In this disclosure, the terms "of," "for," "regarding," "related to," "associated with," etc. may be read interchangeably.

[0513] In the present disclosure, terms such as "when A, B," "if A, (then) B," "B upon A," "B in response to A," "B based on A," "B during / while A," "B before A," "B at (the same time as) / on A," "B after A," "B since A," and "B until A" may be interchangeable. Note that A, B, and the like herein may be replaced with appropriate expressions such as nouns, gerunds, and regular sentences, depending on the context. Note that the time difference between A and B may be approximately zero (immediately after or immediately before). A time offset may also be applied to the time at which A occurs. For example, "A" may be interchangeable with "before / after a time offset at which A occurs." The time offset (eg, one or more symbols / slots) may be predefined or may be specified by the UE based on signaled information.

[0514] In the present disclosure, timing, time, duration, time instance, any time unit (e.g., slot, subslot, symbol, subframe), period, occasion, resource, etc. may be read interchangeably.

[0515] Although the invention according to the present disclosure has been described in detail above, it is clear to those skilled in the art that the invention according to the present disclosure is not limited to the embodiments described in the present disclosure. The description of the present disclosure is for illustrative purposes only and does not impose any limiting meaning on the invention according to the present disclosure.

Claims

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.

2. The terminal according to claim 1, wherein the control unit controls the measurement and storage of the CSI using reference signal resources arranged within a specific time window based on information regarding the specific time window included in the configuration.

3. The terminal according to claim 1, wherein the control unit controls the measurement and storage of the CSI based on information regarding a performance monitoring event included in the configuration.

4. The terminal of claim 1, wherein when multiple CSIs are generated in generating the CSI report, the control unit generates the multiple CSIs using a CSI element indicated by an absolute value and a CSI element indicated by a differential value.

5. A wireless communication method for a terminal, comprising: a step of receiving a configuration for performance monitoring of an artificial intelligence (AI)-based channel state information (CSI) report; and a step of controlling at least one of measuring and storing AI-based CSI and generating the CSI report based on the configuration.

6. A base station having a transmitter that transmits settings for performance monitoring of artificial intelligence (AI)-based channel state information (CSI) reports, and a control unit that uses the settings to instruct at least one of measuring and storing AI-based CSI and generating the CSI reports.

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