WO2025069421A1 - Terminal, wireless communication method, and base station - Google Patents

Abstract

A terminal according to one aspect of the present disclosure comprises: a control unit that controls transmission of a request for network-side performance monitoring related to an artificial intelligence (AI)-based channel state information (CSI) report; and a reception unit that receives an instruction related to the CSI report and transmitted on the basis of settings. According to the aspect of the present disclosure, it is possible to achieve suitable overhead reduction, channel estimation, and resource utilization.

Description

Terminal, wireless communication method and base station

This disclosure relates to terminals, wireless communication methods, and base stations in next-generation mobile communication systems.

Long Term Evolution (LTE) was specified for Universal Mobile Telecommunications System (UMTS) networks with the aim of achieving higher data rates and lower latency (Non-Patent Document 1). In addition, LTE-Advanced (3GPP Rel. 10-14) was specified for the purpose of achieving higher capacity and greater sophistication over LTE (Third Generation Partnership Project (3GPP (registered trademark)) Release (Rel.) 8, 9).

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 and later, etc.) are also under consideration.

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

In terms of 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.

Spatial domain downlink (DL) beam prediction, temporal DL beam prediction, positioning, etc. are being considered as use cases for utilizing AI models. Such beam prediction methods may be called AI-based beam prediction (beam reporting), AI-based positioning, AI-based beam management (BM), etc. Temporal DL beam prediction may be called, for example, time domain Channel State Information (CSI) prediction.

In addition, another use case for utilizing AI models 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 realized, for example, using an autoencoder.

In utilizing such AI, monitoring the performance of the AI model is being considered. The performance monitoring of the AI model may be performed on the terminal side (terminal, user terminal, User Equipment (UE)) or on the network (NW, for example, a base station (Base Station (BS))).

In future wireless communication systems, it is being considered to combine performance monitoring on the UE side and the NW side. However, there has been insufficient consideration given to such combined performance monitoring.

If this consideration is not sufficient, appropriate overhead reduction, highly accurate channel estimation, and efficient resource utilization may not be achieved, which may hinder improvements in communication throughput and communication quality.

Therefore, one of the objectives of this disclosure is to provide a terminal, a wireless communication method, and a base station that can achieve optimal overhead reduction/channel estimation/resource utilization.

A terminal according to one embodiment of the present disclosure has a control unit that controls the transmission of requests for network-side performance monitoring regarding artificial intelligence (AI)-based channel state information (CSI) reporting, and a receiving unit that receives instructions regarding the CSI reporting transmitted based on the settings.

According to one aspect of the present disclosure, it is possible to achieve optimal overhead reduction, channel estimation, and resource utilization.

FIG. 1 is a diagram illustrating an example of a framework for managing AI models. FIG. 2 is a diagram showing an example of specifying an AI model. FIG. 3 is a diagram showing an example of CSI feedback using an encoder/decoder. FIG. 4 illustrates an example life cycle management framework for performance monitoring in a UE according to an embodiment. FIG. 5 illustrates an example life cycle management framework for performance monitoring in a BS according to one embodiment. 6A and 6B are diagrams showing an example of an AI-based beam report. FIG. 7 illustrates an example of performance monitoring of CSI compression at the UE side. FIG. 8 is a diagram showing an example of CSI reconstruction using a proxy model. 9A and 9B are diagrams illustrating an example of NW-side monitoring and UE-side monitoring, respectively. FIG. 10 is a diagram illustrating an example of a monitoring method relating to a combination of UE side monitoring and NW side monitoring. FIG. 11 is a diagram showing an example of generation of CSI elements related to option 4-4. FIG. 12 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment. FIG. 13 is a diagram illustrating an example of the configuration of a base station according to an embodiment. FIG. 14 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment. FIG. 15 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment. FIG. 16 is a diagram illustrating an example of a vehicle according to an embodiment.

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

In this disclosure, CSI includes a Channel Quality Indicator (CQI), a Precoding Matrix Indicator (PMI), a CSI-RS Resource Indicator (CRI), a SS/PBCH Block Resource Indicator (SSBRI), a Layer Indicator (LI), a Rank Indicator (RI), and a Layer 1 Reference Signal Received Power (L1-RSRP). It may include at least one of the following: L1-Reference Signal Received Power (L1-RSRQ), L1-SINR (Signal to Interference plus Noise Ratio), L1-SNR (Signal to Noise Ratio), information on the channel matrix (or channel coefficients), information on the precoding matrix (or precoding coefficients), information on the beam/Transmission Configuration Indication state (TCI state)/spatial relation, etc.

The RS used to generate the CSI may be, for example, at least one of a Channel State Information Reference Signal (CSI-RS), a Synchronization Signal/Physical Broadcast Channel (SS/PBCH) block, a Synchronization Signal (SS), and a DeModulation Reference Signal (DMRS).

In this 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. In addition, CSI-RS may include other reference signals.

The UE may receive configuration information regarding CSI reporting (which may be referred to as CSI report configuration, report setting, etc.) and control CSI reporting based on the configuration information. The report configuration information may be, for example, a Radio Resource Control (RRC) Information Element (IE) "CSI-ReportConfig."

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

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 (one/multiple) opportunity, a CSI opportunity, an opportunity, a CSI-RS resource/CSI-IM resource/SSB resource, a time resource, a frequency resource, an antenna port (e.g., a CSI-RS port), etc. The time unit of a CSI resource may be a slot, a symbol, etc.

The information on the CSI resources may include information on CSI resources for channel measurement, information on CSI resources for interference measurement (NZP-CSI-RS resources), information on CSI-IM resources for interference measurement, etc.

The reporting amount information may specify any one of the above CSI parameters (e.g., CRI, RI, PMI, CQI, LI, L1-RSRP, etc.) or a combination of these.

The report type information may indicate a periodic CSI (Periodic CSI (P-CSI)) report, an aperiodic CSI (A-CSI) report, or a semi-persistent CSI (Semi-Persistent CSI (SP-CSI)) report.

The UE performs CSI-RS/SSB/CSI-IM measurements based on the CSI resource configuration corresponding to the CSI reporting configuration (the CSI resource configuration associated with CSI-ResourceConfigId) and derives the CSI to report based on the measurement results.

The CSI resource configuration (e.g., the CSI-ResourceConfig information element) may include a csi-RS-ResourceSetList field indicating more specific CSI-RS/SSB resources, resource type information (e.g., "resourceType") indicating the time domain behavior of the resource configuration, etc.

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

(Application of Artificial Intelligence (AI)) Technology to Wireless Communications)
Regarding future wireless communication technologies, the use of AI technologies such as machine learning (ML) for network/device control and management is being considered.

For example, it is being considered that terminals (user equipment (UE))/base stations (BS)) will utilize AI technology to improve channel state information (CSI) feedback (e.g., reducing overhead, improving accuracy, prediction), improve beam management (e.g., improving accuracy, prediction in the time/space domain), and improve position measurement (e.g., improving position estimation/prediction).

The AI model may output at least one piece of information such as an estimate, a prediction, a selected action, a classification, etc. based on the input information. The UE/BS may input channel state information, reference signal measurements, etc. to the AI model, and output highly accurate channel state information/measurements/beam selection/position, future channel state information/radio link quality, etc.

In this disclosure, AI may be interpreted as an object (also called a target, object, data, function, program, etc.) having (implementing) at least one of the following characteristics:
- Estimation based on observed or collected information;
- making choices based on observed or collected information;
- Predictions based on observed or collected information.

In this disclosure, estimation, prediction, and inference may be interpreted as interchangeable. Also, in this disclosure, estimate, predict, and infer may be interpreted as interchangeable.

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

In addition, in the present disclosure, an AI model may be interpreted as an object having (implementing) at least one of the following characteristics:
- Producing estimates by feeding information,
- Predicting estimates by providing information
- Discover features by providing information,
- Select an action by providing information.

In addition, 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.

Furthermore, in this disclosure, AI model, model, ML model, predictive analytics, predictive analysis model, tool, autoencoder, encoder, decoder, neural network model, AI algorithm, scheme, etc. may be interchangeable. Furthermore, 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.

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

Furthermore, in this disclosure, encoder, encoding, encoding/encoded, modification/alteration/control by an encoder, compressing, compress/compressed, generating, generate/generated, etc. may be read as interchangeable terms.

Furthermore, in this disclosure, the terms decoder, decoding, decode/decoded, modification/alteration/control by a decoder, decompressing, decompress/decompressed, reconstructing, reconstruct/reconstructed, etc. may be interpreted as interchangeable.

In the present disclosure, a layer (of an AI model) may be interpreted as a layer (input layer, intermediate layer, etc.) used in an AI model. A layer in the present disclosure may correspond to at least one of an input layer, intermediate layer, output layer, batch normalization layer, convolution layer, activation layer, dense layer, normalization layer, pooling layer, attention layer, dropout layer, fully connected layer, etc.

In this disclosure, methods for training an AI model may include supervised learning, unsupervised learning, reinforcement learning, federated learning, and the like. Supervised learning may refer to the process of training a model from inputs and corresponding labels. Unsupervised learning may refer to the process of training a model without labeled data. Reinforcement learning may refer to the process of training a model from inputs (i.e., states) and feedback signals (i.e., rewards) resulting from the model's outputs (i.e., actions) in the environment with which the model interacts.

In this disclosure, terms such as generate, calculate, derive, etc. may be interchangeable. In this disclosure, terms such as implement, operate, operate, execute, etc. may be interchangeable. In this disclosure, terms such as train, learn, update, retrain, etc. may be interchangeable. In this disclosure, terms such as infer, after-training, production use, actual use, etc. may be interchangeable. In this disclosure, terms such as signal and signal/channel may be interchangeable.

Figure 1 shows 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 Life Cycle Management (LCM) of an AI model.

The data collection stage corresponds to the stage of collecting data for generating/updating an AI model. The data collection stage may include data organization (e.g., determining which data to transfer for model training/model inference), data transfer (e.g., transferring data to an entity (e.g., UE, gNB) that performs model training/model inference), etc.

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

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.

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, conversion, etc.), model training/validation, model testing (e.g., checking whether the trained model meets performance thresholds), model exchange (e.g., transferring the model for distributed learning), model deployment/update (deploying/updating the model to the entities that will perform model inference), etc.

In addition, 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.

Also, AI model validation may refer to a sub-process of training to evaluate the quality of an AI model using a dataset different from the dataset used to train the model. This sub-process helps select model parameters that generalize beyond the dataset used to train the model.

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

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 performing the model training), output (providing model output to the actor), etc.

In addition, 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.

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

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 where joint inference is performed. Here, joint inference may include AI inference where the inference is performed jointly across the UE and the network, e.g., a first part of the inference may be performed first by the UE and the remaining part by the gNB (or vice versa).

Also, AI model monitoring may refer to the process of monitoring the inference performance of an AI model, and may be interchangeably read as model performance monitoring, performance monitoring, etc.

Note that model registration may refer to making a model executable (registering) by assigning a version identifier to the model and compiling it into the specific hardware used in the inference phase. Model deployment may refer to distributing (or activating at) a fully developed and tested run-time image (or image of the execution environment) of the model to the target (e.g., UE/gNB) where inference will be performed.

Actor stages may include action triggers (e.g., deciding whether to trigger an action on another entity), feedback (e.g., feeding back information needed for training data/inference data/performance feedback), etc.

Note that, for example, training of a model for mobility optimization may be performed in, for example, Operation, Administration and Maintenance (Management) (OAM) in a network (NW)/gNodeB (gNB). 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 absence of data exchange for model deployment are advantageous. Inference of the above model may be performed in, for example, a gNB.

Depending on the use case (i.e., the function of the AI model), the entity performing the training/inference may be different. The function of the AI model may include beam management, beam prediction, autoencoder (or information compression), CSI feedback, positioning, etc.

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.

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

For CSI feedback/channel estimation using autoencoders, the OAM/gNB/UE may perform model training and the gNB/UE may perform model inference (jointly).

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.

Note that model activation may mean activating an AI model for a particular function. Model deactivation may mean disabling an AI model for a particular function. Model switching may mean deactivating a currently active AI model for a particular function and activating a different AI model.

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

Figure 2 shows an example of specifying an AI model. In this example, the UE and 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.

(AI-based CSI feedback)
As a use case of utilizing an AI model, CSI compression using a two-sided AI model is being considered. Such a CSI compression method may be called AI-based CSI feedback, and may be realized, for example, by using an autoencoder.

Figure 3 shows an example of CSI feedback using an encoder/decoder. The UE transmits information (CSI feedback information) including encoded bits that are output by inputting CSI to an encoder from an antenna. The BS inputs the received CSI feedback information bits to a corresponding decoder to obtain the CSI to be output.

The input CSI may include, for example, information on channel coefficients (elements of a channel matrix) or information on precoding coefficients (elements of a precoding matrix). 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.

The CSI output from the decoder may be reconstructed CSI that corresponds to the input to the encoder, or it may be CSI different from the input to the encoder (e.g., if the input information is information on channel coefficients, it may be information on precoding coefficients, etc.).

In addition, the encoder/decoder may also include pre-processing of the input and post-processing of the output.

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

(Performance Monitoring Lifecycle Management Framework)
In the following, each step in the life cycle management framework of performance monitoring at the UE/BS is described for AI-based CSI feedback.

FIG. 4 illustrates an example of a lifecycle management framework for performance monitoring in a UE according to one embodiment.

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

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

In the performance reporting step, the UE reports the above monitored performance to the NW.

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

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.

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

Note that some of the steps shown in the figure (e.g., steps shown with dashed lines) may be performed as necessary.

FIG. 5 illustrates an example of a life cycle management framework for performance monitoring in a BS according to one embodiment.

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

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

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

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

Note that some of the steps shown in the figure (e.g., steps shown with dashed lines) may be performed as necessary.

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

FIGS. 6A and 6B are diagrams showing an example of an AI-based beam report. FIG. 6A shows spatial domain DL beam prediction. The UE may measure a spatially sparse (or thick) beam, input the measurement results, etc., into an AI model, and output a predicted result of the beam quality of a spatially dense (or thin) beam.

Figure 6B shows temporal DL beam prediction. The UE may measure the beam over time, input the measurement results, etc., to an AI model, and output the predicted beam quality of the future beam.

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

Furthermore, the beams/RS related to the output (prediction result) of the AI model may be referred to as set A. The beams/RS related to the input of the AI model may be referred to as set B.

Candidates for input to the AI model for BM Case 1/2 include L1-RSRP (Layer 1 Reference Signal Received Power), assistance information (e.g., beam shape information, UE position/direction information, transmit beam usage information), Channel Impulse Response (CIR) information, and corresponding DL transmit/receive beam IDs.

Possible outputs of the AI model for BM Case 1 include the IDs of the top K (K is an integer) transmit/receive beams, the predicted L1-RSRP of these beams, the probability that each beam is in the top K, and the angles of these beams.

In addition to the candidates for the output of the AI model in BM Case 1, the candidates for the output of the AI model in BM Case 2 include predicted beam failures.

(Performance monitoring of CSI compression at the UE side)
7 is a diagram showing an example of performance monitoring of CSI compression at the UE side, in which the UE may monitor expected performance if an encoder is available at the UE.

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

The CQI in (1) may be, for example, at least one of a wideband CQI, an average of subband CQIs, a weighted average of subband CQIs, a maximum/minimum of subband CQI, etc. 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 may be specified in the standard (e.g., the expected number of symbols, the number of resource blocks, etc.). Furthermore, the certain block error probability may be, for example, at least one of 0.1, 0.00001, etc.

As shown in Figure 7, we assume that the CSI output from the decoder is the reconstructed CSI that corresponds 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 that corresponds to the encoder.

The UE performs channel measurements based on the CSI-RS transmitted from the BS and obtains the channel matrix H. The UE estimates its performance based on H.

If the input of the UE's encoder is a precoding matrix W, the UE may perform a specific process on H (e.g., Singular Value Decomposition (SVD)) to obtain W. The UE estimates performance based on W.

Also, if 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 perform the above-mentioned preprocessing on the above-mentioned W to obtain p-W. The UE may estimate performance based on p-W, or may estimate performance based on W.

The UE may also transmit a performance report to the BS as necessary.

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

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

In this example, the UE performs channel measurement based on the CSI-RS transmitted from the BS, and obtains the H/W/p-W corresponding to the target CSI. The UE also calculates (estimates) the expected performance based on the target CSI and the above-mentioned expected performance information. If performance monitoring is the only task, the UE does not need to operate the encoder.

(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. This can reduce the processing and storage problems of the UE. The proxy model can be different from the actual reconstruction model in the base station. This can avoid the uniqueness problem.

Figure 8 shows an example of CSI reconstruction (pseudo reconstruction) using a proxy model. The UE receives a proxy model for decoding from the NW (base station). The UE uses the proxy model to reconstruct the encoded CSI and outputs it as an estimated CSI. The UE maps the estimated result to the actual CSI and calculates a KPI (Key Performance Indicator) (e.g., SGCS (squared generalized cosine similarity)). In this way, there is a high correlation between the KPI (SGCS) when an actual decoder is used and the KPI (SGCS) using the proxy model.

(Performance monitoring of AI/ML CSI feedback)
It is considered that performance monitoring of AI/ML CSI feedback includes NW side monitoring and UE side monitoring.

The network side monitoring may be based on ground-truth feedback and channel estimation using a UL reference signal (e.g., SRS).

FIG. 9A is a diagram showing an example of network side monitoring. In the example shown in FIG. 9A, first, the UE measures the RS resource for input, and then generates AI/ML CSI depending on whether the matrix to be acquired is H or W. Also, the UE measures the RS resource for input/reference, and then transmits ground-truth feedback/SRS depending on whether the matrix to be acquired is H or W.

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

UE side monitoring may be monitoring based on a proxy CSI reconstruction model.

Figure 9B is a diagram showing an example of UE-side monitoring. In the example shown in Figure 9B, first, the UE measures the RS resource for input, then generates AI/ML CSI depending on whether the matrix to be acquired is H or W, and performs proxy AI/ML CSI reconfiguration based on the obtained bit stream. The UE also measures the RS resource for input/reference. The UE reports CSI based on the CSI generation to the NW, and NMSE/SGCS is calculated based on (comparing) the CSI reconfiguration and the input/reference RS resource measurement. 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.

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

(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.

For example, the network may have sufficient capability (computational power) to accurately monitor multiple models, but may lack target CSI data for monitoring. On the other hand, the UE may have target CSI data, but may not have sufficient capability to accurately monitor multiple models.

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

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

However, there has been insufficient consideration given to this type of combined network-side monitoring and UE-side monitoring. Specifically, there has been insufficient consideration given to the provisions for settings/events related to UE-side monitoring, the operations related to requests for network-side monitoring operations, the method of CSI reporting after UE-side monitoring, and the method of generating said CSI reports.

If these considerations are not sufficient, appropriate overhead reduction, highly accurate channel estimation, and efficient resource utilization may not be achieved, which may hinder improvements in communication throughput and communication quality.

The inventors therefore came up with a way to solve these problems.

Embodiments of the present disclosure will now be described in detail with reference to the drawings. The wireless communication methods according to the embodiments may be applied independently or in combination.

In this disclosure, "A/B" and "at least one of A and B" may be interpreted as interchangeable. Also, in this disclosure, "A/B/C" may mean "at least one of A, B, and C."

In this disclosure, terms such as notify, activate, deactivate, indicate, select, configure, update, and determine may be read as interchangeable terms. In this disclosure, terms such as support, control, capable of control, operate, and capable of operating may be read as interchangeable terms.

In this disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, higher layer parameters, fields, information elements (IEs), settings, etc. may be interchangeable. In this disclosure, Medium Access Control (MAC Control Element (CE)), update commands, activation/deactivation commands, etc. may be interchangeable.

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 protocols (e.g., NR Positioning Protocol A (NRPPa)/LTE Positioning Protocol (LPP)) messages), or a combination of these.

In the present disclosure, the MAC signaling may use, for example, a MAC Control Element (MAC CE), a MAC Protocol Data Unit (PDU), etc. The broadcast information may be, for example, a Master Information Block (MIB), a System Information Block (SIB), Remaining Minimum System Information (RMSI), Other System Information (OSI), etc.

In the present disclosure, the physical layer signaling may be, for example, Downlink Control Information (DCI), Uplink Control Information (UCI), etc.

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

In this 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 (CONTROLLER RESOLUTION 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 relationship 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.

In this disclosure, CSI-RS, Non-Zero Power (NZP) CSI-RS, Zero Power (ZP) CSI-RS, and CSI Interference Measurement (CSI-IM) may be interchangeable. In addition, CSI-RS may include other reference signals.

In this disclosure, the measured/reported RS may refer to the RS measured/reported for CSI reporting.

In this disclosure, timing, time, duration, slot, subslot, symbol, subframe, etc. may be interpreted as interchangeable.

In this disclosure, the terms direction, axis, dimension, domain, polarization, polarization component, etc. may be interpreted as interchangeable.

In this disclosure, estimation, prediction, and inference may be interpreted as interchangeable. Also, in this disclosure, estimate, predict, and infer may be interpreted as interchangeable.

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

In this disclosure, bits, bit strings, bit series, series, values, information, values obtained from bits, information obtained from bits, etc. may be interpreted as interchangeable.

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

In this disclosure, RSRP may be interchangeably read as any parameter related to reception power/reception quality, etc. (e.g., RSRQ, SINR, CSI, etc.).

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 (CSI-RS Resource Indicator (CRI)), an SS/PBCH block resource indicator (SS/PBCH Block Indicator (SSBRI)), etc.

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), etc.

In the present disclosure, the terms receive beam assumption, number of receive beams, receive beam index, receive beam selection, receive beam setting, and receive beam instruction may be interchangeable. In the present disclosure, the terms receive beam, transmit beam, DL receive beam, DL transmit beam, and transmit and receive beam pairs 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.

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/update may be instructed (controlled) based on the functionality (e.g., for each function).

A model ID may also refer to an identifier for a model (or a set of models). Multiple models may be assigned the same model ID in an actual deployment. In this case, these models may actually be different models (e.g., have different number of layers, etc.), but may be treated as the same model.

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

In addition, in this 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 regarding the applicability of the model/functionality, the environment, the UE/gNB settings, etc.

In this disclosure, functionality may simply be read as "function."

In this disclosure, functionality, function, functionality ID, model, and model ID may be interpreted interchangeably.

In this disclosure, update, report, and send may be read interchangeably.

In this disclosure, meta information, assistance information, sensing information, KPI, performance KPI, UE status, and status may be interpreted as interchangeable.

In this disclosure, monitor and evaluation may be interpreted interchangeably.

In this disclosure, determination, judgement, and application of a specific action may be interpreted interchangeably.

In this disclosure, entity, specific entity, UE, NW, gNB, and LMF may be read as interchangeable.

In this disclosure, NW, LMF, gNB, and BS may be read as interchangeable.

In this disclosure, the UE side model and UE may be interpreted as interchangeable.

In this disclosure, the model, UE side model, logical model, and physical model may be interchangeable.

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.

In this disclosure, performance indicators and monitoring indicators may be interpreted as interchangeable.

In this disclosure, association, correspondence, and mapping may be interpreted as interchangeable.

In this disclosure, monitor result, monitored result, post-monitoring result, and monitoring result may be read interchangeably.

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

In the present disclosure, for performance monitoring of the UE side model/functionality, the UE may report the following information as monitoring results:
Performance metrics corresponding to the monitored model/functionality.
- An event occurs in the calculation of a performance index corresponding to a monitored model/functionality (eg, the value of a positive index is greater/less than a threshold for a certain duration).

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 a particular functionality. For example, an AI/ML-based CSI report (AI/ML CSI report) may be, for example, at least one of predicted CSI, compressed CSI, advanced CSI, and CSI of any type (e.g., type [x]).

In this disclosure, AI/ML-based CSI reporting and CSI reporting may be read interchangeably.

In this 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. Alternatively, a proxy model may refer to a model that estimates secondary information (CQI/RI, etc.) of the restored CSI.

In this disclosure, AI/ML functionality, AI/ML CSI functionality may refer to functionality that is commanded by the NW or reported by the UE. The functionality may be, for example, at least one of predicted CSI, compressed CSI, advanced CSI, and CSI of any type (e.g., type [x]).

In this disclosure, AI/ML functionality, AI/ML CSI functionality, and functionality may be interpreted interchangeably.

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).

In this disclosure, report quantity, information regarding report quantity, and report quantity information may be read interchangeably.

In this 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.

In this disclosure, report, CSI report, measurement result report, monitoring report, and monitor result report may be read interchangeably.

In this disclosure, historical CSI, historical ground-truth (GT) CSI, historical GT CSI, GT CSI, CSI, CSI reporting, CSI compression, etc. may be interchangeable.

In this disclosure, CSI-RS and PDSCH/DMRS may be interpreted as interchangeable.

In the present disclosure, type X monitoring (results) may refer to monitoring (results) based on precoded RS resources. Also, type Y monitoring (results) may refer to monitoring (results) based on PDSCH/DMRS.

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 CSI report associated with a model ID or specific functionality/feature.

In this disclosure, performance metrics, metrics for monitoring reports, and KPIs may be interpreted interchangeably.

(Wireless communication method)
Fig. 10 is a sequence diagram between a terminal (UE) and a base station (NW) showing an overall picture of each embodiment of the present disclosure. The procedure shown in Fig. 10 is merely an example, and the order of each step can be changed as appropriate as long as no contradiction occurs.

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

The UE may then receive the CSI-RS and perform AI/ML CSI reporting. The UE may then receive each channel (e.g., PDSCH) that is transmitted based on the CSI report, etc.

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

The UE may determine whether to trigger monitoring of an event report based on at least one of the first monitoring and the storage/acquisition of historical correct CSI. The UE may send an event report (e.g., a request for second monitoring) to the NE.

The NW may at least one of transmit configuration for historical CSI feedback and schedule historical CSI feedback based on the event report.

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

The NW may perform a second monitoring based on feedback from the UE. The NW may perform an operation such as model switching/fallback based on the second monitoring.

In this 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, typeII-CJT-r18, additional extended type 2 PS codebook for CJT, type 2 PS codebook for Rel. 18 CJT, typeII-CJT-PortSelection-r18' may be read as interchangeable.

In this disclosure, each embodiment/option may be applied alone or in combination with multiple options.

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

First Embodiment
The first embodiment relates to UE operation and configuration related to preparation of historical CSI reporting in the UE.

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

The setting may include, for example, at least one of information regarding the time/time slot for the start of historical GT CSI storage (e.g., the time window until the latest AI/ML CSI feedback), information regarding an event that triggers (starts) the storage of historical GT CSI (e.g., when the UE's monitoring result is below a certain threshold), information regarding the window length for storing historical GT CSI, and information regarding the amount of historical GT CSI to be stored.

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

The UE may drop the oldest CSI it stores based on this configuration.

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

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

In this disclosure, the CSI relating to this particular format may be referred to as CSI-H for convenience.

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.

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

《Option 1-1》
The UE may measure/store CSI using an input RS resource if the input RS resource is located within a particular time resource (eg, a time window).

The start/end timing (e.g., slot/symbol) of the time resource (e.g., time window) may be set/instructed to the UE. The setting/instruction may be performed according to at least one of the methods described in Supplementary Note 2 below.

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 have to be set/instructed to the UE). For example, the end timing of the time window may be the current slot.

Option 1-1 allows the input RS resource to be appropriately determined based on the time resource.

《Option 1-2》
The UE may be configured/instructed as to an event for triggering CSI measurement/storage, and the UE may determine to measure/storage CSI based on the configured/instructed event.

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

The event may be, for example, when the performance of the AI/ML model monitored by the UE falls below a certain threshold (at a particular time period/timing/instance).

The specific period/timing/instance/threshold may be predefined in the specifications, or may be set/indicated based on at least one of the methods described in Supplementary Note 2 below.

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

The event may also be, for example, when the UE fails to receive (e.g. decode) a scheduled PDSCH a certain number of times (over a certain period of time).

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

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

In the above options 1-1/1-2, the UE may be configured/instructed on at least one of the functionality (and specific conditions), model ID, and CSI reporting index according to at least one method described in Supplementary Note 2 below. The UE may use (only) the input RS resource corresponding to one or more configured/instructed functionality/model ID/CSI reporting index as CSI-H.

Furthermore, in the above options 1-1/1-2, the UE may assume/judge that more than a certain number (e.g., M) of CSI-H (or equal to or greater than a certain number) will not be reported to the NW. In other words, the UE may acquire/store up to M (or M-1) pieces of historical CSI. According to this method, the number of CSIs stored by the UE can be appropriately controlled.

The M may be set/indicated, for example, according to at least one of the methods described in Supplementary Note 2 below, or may be reported by the UE according to at least one of the methods described in Supplementary Note 3 below.

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

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

Second Embodiment
The second embodiment relates to configuration and UE operation regarding a request for second monitoring in the NW.

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

The configuration/trigger signal may be transmitted according to at least one of the methods described in Supplementary Note 2 below.

The configuration may include, for example, at least one of information regarding a particular condition (e.g., at least one of thresholds for the monitored KPIs, a set amount of collected GT CSI, and a type of CSI (e.g., periodic/non-periodic/semi-persistent)), a trigger for an event report regarding the second monitoring, and information regarding resources for reporting.

The UE may report an event regarding the second monitoring using a specific method.

The UE may take the action described in Options 2-1-1/2-1-2 below based on certain conditions.

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

《Option 2-1-1》
The UE may report a specific message (which may be referred to as message A, for example) if a set condition is met.

The particular message may be sent, for example, according to at least one of the methods described in Supplementary Note 3 below.

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

Option 2-1-1 allows the trigger operation related to the second monitoring to be performed appropriately.

《Option 2-1-2》
The UE may report a status regarding whether the set conditions are met or not.

The report may be sent, for example, according to at least one of the methods described in Supplementary Note 3 below.

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

Option 2-1-2 allows the trigger operation related to the second monitoring to be performed appropriately.

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

Furthermore, at least one of the specific messages in option 2-1-1 and the reports in option 2-1-2 may be transmitted using, for example, an existing (defined by Rel. 18/19/20/21) method/content combination (e.g., a CQI/RI combination, or a special value of PMI (e.g., a PMI in which all or part of the information bits are set to a specific value (e.g., 0))).

Furthermore, at least one of the specific message in option 2-1-1 and the report in option 2-1-2 may be transmitted using, for example, a dedicated field in the monitoring report or a special value in the monitoring report.

Furthermore, the specific message in option 2-1-1 and/or the report in option 2-1-2 may be sent using resources configured/instructed according to at least one of the methods described in Supplementary Note 2 below.

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.

《Option 2-2-1》
A particular condition may be, for example, that a metric monitored by the UE is higher/lower (or greater than or equal to/less than) a particular threshold (at a particular time period/timing/instance).

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

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

《Option 2-2-2》
The particular condition may, for example, be that the UE fails to process (eg decode) the scheduled PDSCH a particular number of times (in a certain period of time).

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

《Option 2-2-3》
The particular condition may be, for example, a trigger to report CSI using a particular method (eg, periodic/aperiodic/semi-persistent).

The trigger may be notified according to at least one of the methods described in Supplementary Note 2 below.

《Option 2-2-4》
The particular condition may be, for example, that the UE acquires/stores/possesses a particular number (eg, K) of CSI-H.

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

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.

Third Embodiment
A third embodiment relates to schedules and UE behavior for historical CSI reporting.

The UE may receive configurations/instructions from the NW regarding encoding/quantization/compression/reporting of historical GT CSI.

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

The settings/instructions may include, for example, at least one of parameters for generating compressed historical GT CSI feedback and reporting resources.

The UE may report CSI-H based on specific triggers/settings/instructions.

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

The particular triggers/settings/instructions may, for example, include information regarding at least one of the following:
Number of CSI-Hs reported.
-Reporting resources/channels.
Number of CSI-H reports for each resource/channel.
CSI-H quantization/compression/encoding methods and/or parameters related to said methods.
- CSI Report Index/CSI Resource Index.

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

The UE may (e.g., by default) report all CSI-Hs (all N CSI-Hs out of N stored CSI-Hs).

The UE may report all CSI-H (all N CSI-Hs out of N stored CSI-Hs) when the specific trigger/setting/instruction does not include at least one of information regarding the number of CSI-Hs to be reported and information regarding the number of CSI-Hs to be reported for each resource/channel. The UE may report a number of CSI-Hs based on this information when the specific trigger/setting/instruction includes at least one of information regarding the number of CSI-Hs to be reported and information regarding the number of CSI-Hs to be reported for each resource/channel.

When reporting M CSI-Hs out of the N CSI-Hs stored by the UE, the UE may report the M most recent CSI-Hs.

The UE may report CSI-H using a specific method (e.g. periodic/aperiodic/semi-persistent) according to the specific trigger/configuration/instruction.

The specific trigger/setting/instruction may cause the CSI-H report to include a CSI report index. The UE may then assume/expect that the corresponding CSI reporting configuration includes instructions for one or more PMI reports.

The UE does not need to assume/expect CSI-H to be reported at multiple times.

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

If there is no CSI-H available in the UE, the UE may not assume/expect a CSI-H report to be triggered.

According to the third embodiment described above, it is possible to appropriately define the schedule (trigger/setting/instruction) for CSI reporting and the corresponding UE operation.

Fourth Embodiment
A fourth embodiment relates to historical CSI report generation and UE operation.

The UE may encode/quantize/compress multiple GT CSIs separately/jointly using a specific method based on a specific configuration.

The particular method may be, for example, based on at least one of the following: the Lempel-Ziv algorithm, a particular type of extension (e.g., eType II) using a common spatial domain/frequency domain vector and derivatives.

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

The UE may generate CSI (which may mean CSI content/bits/information) for CSI-H reporting configured/instructed using at least one method described in the third embodiment above.

In this disclosure, CSI, CSI contents, CSI elements, CSI sequence, CSI bits, and CSI information bits may be interpreted as interchangeable.

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

Option 4-1
The UE may generate each CSI-H (CSI-H content) separately by scalar quantizing each element in the CSI-H with a particular bit-width.

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

Option 4-2
The UE may generate each CSI-H (CSI-H content) separately using vector quantization.

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

Option 4-3
The UE may first apply options 4-1/4-2 above.

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

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

《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 (a particular resource unit).

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

The quantization/compression method may be, for example, an extension of an existing type of CSI (e.g., Type 2/Extended Type 2 CSI).

For example, the UE may first generate CSI feedback content having the configured parameters (e.g., at least one CSI of type 2, extended type 2, and extended type 2 with extended parameter combination).

For example, the UE may generate an element of CSI (eg, i _n (n is 1 or 2)) according to a codebook defined in existing specifications (eg, up to Rel. 17/18).

The CSI element (eg, i _n (n is 1 or 2)) may be used to represent one CSI-H or multiple CSI-Hs.

If the CSI element (e.g., i _n (n is 1 or 2)) is not used to represent one CSI-H (in other words, if the CSI element is used to represent multiple CSI-Hs), the UE may generate one or a pair of differential values (e.g., delta-i _n (n is 1 or 2)) for the element for each CSI-H.

The UE may also generate one or a pair of differential values (e.g., delta-i _n (n is 1 or 2)) for each CSI-H other than the CSI-H directly represented by the CSI element (e.g., i _n (n is 1 or 2)).

For example, delta-i ₁ and delta-i ₂ may denote the delta of each CSI-H relative to the common portion generated as i ₁ and i _2, respectively.

By using such difference values, it is possible to change part of the CSI-H spatial domain base vectors, change part of the frequency domain base vectors, or change part of the combination coefficients.

For example, delta-i _n may not include all fields corresponding to i _n , in other words, some elements may be common to all CSI-H, and other elements may be configurable using differential values.

The UE may generate CSI-H elements (final elements) that include i _n (n is 1 or 2) and all delta-i _n (n is 1 or 2).

FIG. 11 is a diagram showing an example of the generation of CSI elements relating to option 4-4. In the example shown in FIG. 11, the UE generates CSI1 to CSI3 as multiple pieces of CSI.

In the example shown in Fig. 11, the UE generates PMI1 of CSI1 based on the codebook of extended type 2 (defined in Rel. 18 in the example shown in Fig. 11, for example). In the example shown in Fig. 11, _i1 = [ _{i1,1 i1,2 i1,5 i1,6,1 i1,7,1 i1,8,1} ] and _i2 = [ _{i2,3,1 i2,4,1 i2,5,1} ] are generated as elements of CSI1.

In the example shown in Fig. 11, the UE reuses _i1 for CSI1 in generating PMI2 for CSI2 (in other words, the spatial domain/frequency domain base vector and the reported coefficient are reused). At this time, the UE generates a difference value delta- _i2 = [delta- _i2,3,1 delta- _i2,4,1 delta- _i2,5,1 ] from _i2 for PMI1 in generating PMI2 for CSI2. Note that the reused part does not need to be reported.

In this way, by using the difference value of the amplitude/phase between PMI1 and _i2 , it is possible to reduce the number of bits for quantization.

Furthermore, in the example shown in Fig. 11, the UE reuses [ _{i1,1 i1,2 i1,5 i1,6,1] for CSI1 in generating PMI3 for CSI3 (in other words, the spatial domain/frequency domain base vector is reused, and the reported coefficient is not reused). At this time, the UE generates differential values delta-i1 = [delta - i1,7,1 delta} - _i1,8,1 ] and _i2 = [ _{i2,3,1 i2,4,1 i2,5,1} ] of [ _{i1,7,1 i1,8,1} ] for _i1 of PMI1 in generating PMI2 for CSI2. Note that the reused part does not need to be reported.

The UE may also report information (e.g., a bitmap) indicating which coefficients are to be reported when generating CSI3. This configuration allows the amplitude/phase of new coefficients to be reported with higher resolution, similar to the extended type 2 codebook.

The UE may also jointly quantize multiple CSI-Hs using the (Rel. 18) extended type 2 codebook for predicted PMI or further extensions of the (Rel. 18) extended type 2 codebook for predicted PMI.

The further extension function may support N4 values greater than the length of the Doppler domain (DD)/time domain (TD) basis vectors (DFT basis vectors) (also called the number of DD/TD bases, N4) defined in existing specifications (e.g., Rel. 17/18). By configuring in this way, it is possible to support a number of CSI-Hs greater than eight.

This further extension may also support a non-fixed duration in DD units (e.g. d), in which case the UE may report the actual duration _ds between CSI-H as part of the reporting.

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

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

<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.

The UE may determine whether the conditions for the event trigger are met based on the conditions in the second embodiment described above.

For example, if a trigger condition is met, the UE may report 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 report CSI-H using the resource based on an instruction from the NW (e.g., (UL grant) DCI).

<Additional Information>
[Supplement 1: AI model information]
In this disclosure, AI model information may mean information including at least one of the following:
・Information on input/output of AI model.
- Pre-processing/post-processing information for input/output of AI models.
・Information on AI model parameters.
- Training information for AI models.
-Inference information for AI models.
・Performance information about AI models.

Here, the input/output information of the AI model may include information regarding at least one of the following:
Input/output data content (e.g. RSRP, SINR, amplitude/phase information in the channel matrix (or precoding matrix), information on the Angle of Arrival (AoA), information on the Angle of Departure (AoD), location information).
- Supporting information for the data (may be called meta-information).
- The type of input/output data (e.g. immutable values, floating point numbers).
- Bit width of the input/output data (eg, 64 bits for each input value).
Quantization interval (quantization step size) of input/output data (eg, 1 dBm for L1-RSRP).
The range that the input/output data can take (e.g., [0, 1]).

In the present disclosure, the information regarding AoA may include information regarding at least one of the azimuth angle of arrival and the zenith angle of arrival (ZoA). Furthermore, the information regarding AoD may include information regarding at least one of the azimuth angle of departure and the zenith angle of departure (ZoD).

In the present disclosure, the location information may be location information regarding the UE/NW. The location information may include at least one of information (e.g., latitude, longitude, altitude) obtained using a positioning system (e.g., a satellite positioning system (Global Navigation Satellite System (GNSS), Global Positioning System (GPS), etc.)), information on the BS adjacent to (or serving) the UE (e.g., a BS/cell identifier (ID), a BS-UE distance, a direction/angle of the BS (UE) as seen from the UE (BS), coordinates of the BS (UE) as seen from the UE (BS) (e.g., coordinates on the X/Y/Z axes), etc.), a specific address of the UE (e.g., an Internet Protocol (IP) address), etc. The location information of the UE is not limited to information based on the position of the BS, and may be information based on a specific point.

The location information may include information about its implementation (e.g., location/position/orientation of antennas, location/orientation of antenna panels, number of antennas, number of antenna panels, etc.).

The location information may include mobility information. The mobility information may include information indicating at least one of the following: a mobility type, a moving speed of the UE, an acceleration of the UE, and a moving direction of the UE.

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

In the present disclosure, environmental information (for data) may be information regarding the environment in which the data is acquired/used, and may correspond to, for example, frequency information (such as a band ID), environmental type information (information indicating at least one of indoor, outdoor, Urban Macro (UMa), Urban Micro (Umi), etc.), information indicating Line Of Site (LOS)/Non-Line Of Site (NLOS), etc.

Here, LOS may mean that the UE and BS are in an environment where they can see each other (or there is no obstruction), and NLOS may mean that the UE and BS are not in an environment where they can see each other (or there is an obstruction). Information indicating LOS/NLOS may indicate a soft value (e.g., the probability of LOS/NLOS) or a hard value (e.g., either LOS or NLOS).

In the present disclosure, meta-information may mean, for example, information regarding input/output information suitable for an AI model, information regarding data that has been acquired/can be acquired, etc. 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, 3 dB beam width, the shape of the pointed beam, the number of beams), layout information of gNB/UE antennas, frequency information, environmental information, meta-information ID, etc. In addition, meta-information may be used as input/output of an AI model.

The pre-processing/post-processing information for the input/output of the AI model may include information regarding at least one of the following:
Whether to apply normalization (e.g., Z-score normalization, min-max normalization).
Parameters for normalization (eg, mean/variance for Z-score normalization, min/max for min-max normalization).
Whether to apply a specific numeric transformation method (e.g., one hot encoding, label encoding, etc.).
- Selection rule for whether or not to use as training data.

For example, the input information x may be subjected to Z-score normalization (x _new = (x - μ) / σ, where μ is the average of x and σ is the standard deviation) as pre-processing, and normalized input information x _new may be input to the AI model, and the output y _out from the AI model may be subjected to post-processing to obtain the final output y.

The information of the parameters of the AI model may include information regarding at least one of the following:
- Weight information (e.g., neuron coefficients (connection coefficients)) in an AI model.
・Structure of the AI model.
- The type of AI model as a model component (e.g., Residual Network (ResNet), DenseNet, RefineNet, Transformer model, CRBlock, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)).
- Functions of the AI model as model components (e.g., decoder, encoder).

In addition, the weight information in the AI model may include information regarding at least one of the following:
- Bit width (size) of the weight information.
Quantization interval of weight information.
- Granularity of weight information.
- The range of possible weight information.
・Weight parameters in AI models.
- Information on the difference from the AI model before the update (if updating).
- Method of weight initialization (e.g., zero initialization, random initialization (based on normal/uniform/truncated normal distribution), Xavier initialization (for sigmoid function), He initialization (for Rectified Linear Units (ReLU))).

The structure of the AI model may also include information regarding at least one of the following:
・Number of layers.
- The type of layer (e.g., convolutional, activation, dense, normalization, pooling, attention).
・Layer information.
Time series specific parameters (e.g. bidirectionality, time step).
Parameters for training (e.g., type of feature (L2 regularization, dropout feature, etc.), where to put this feature (e.g., after which layer)).

The layer information may include information regarding at least one of the following:
- The number of neurons in each layer.
・Kernel size.
- Stride for pooling/convolutional layers.
- Pooling method (MaxPooling, AveragePooling, etc.).
・Residual block information.
・Number of heads.
- Normalization method (batch normalization, instance normalization, layer normalization, etc.).
Activation functions (sigmoid, tanh function, ReLU, leaky ReLU information, Maxout, Softmax).

An AI model may be included as a component of another AI model. For example, an AI model may be an AI model in which processing proceeds in the order of model component #1 (ResNet), model component #2 (a transformer model), a dense layer, and a normalization layer.

Training information for the AI model may include information regarding at least one of the following:
Information for the optimization algorithm (e.g. type of optimization (Stochastic Gradient Descent (SGD)), AdaGrad, Adam, etc.), parameters of the optimization (learning rate, momentum information, etc.).
Loss function information (e.g., information on the metrics of the loss function (Mean Absolute Error (MAE)), Mean Square Error (MSE)), Cross Entropy Loss, NLL Loss, Kullback-Leibler (KL) Divergence, etc.)).
- Parameters to be frozen for training (e.g. layers, weights).
- Parameters to be updated (e.g. layers, weights).
Parameters (e.g. layers, weights) that should be (are used as) initial parameters for training.
How to train/update the AI model (e.g., (recommended) number of epochs, batch size, number of data used for training).

The inference information for the AI model may include information regarding decision tree branch pruning, parameter quantization, and the function of the AI model. Here, the function of the AI model may correspond to at least one of, for example, time domain beam prediction, spatial domain beam prediction, an autoencoder for CSI feedback, and an autoencoder for beam management.

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

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 to output dense (or thin) beam quality.

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.

The performance information regarding the AI model may include information regarding the expected value of a loss function defined for the AI model.

The AI model information in this disclosure may include information regarding the scope of application (scope of applicability) of the AI model. The scope of application may be indicated by a physical cell ID, a serving cell index, etc. Information regarding the scope of application may be included in the above-mentioned environmental information.

AI model information regarding a specific AI model may be predetermined in a standard, or may be notified to the UE from the network (NW). An AI model defined in a standard may be referred to as a reference AI model. AI model information regarding a reference AI model may be referred to as reference AI model information.

The AI model information in the present disclosure may include an index for identifying the AI model (e.g., may be called an AI model index, an AI model ID, a model ID, etc.). The AI model information in the present disclosure may include an AI model index in addition to/instead of the input/output information of the AI model described above. The association between the AI model index and the AI model information (e.g., input/output information of the AI model) may be predetermined in a standard, or may be notified to the UE from the NW.

The AI model information in this disclosure may be associated with an AI model and may be referred to as AI model relevant information, simply relevant information, etc. The AI model relevant information does not need to explicitly include information for identifying the AI model. The AI model relevant information may be information that includes only meta information, for example.

In the present disclosure, the model ID may be interchangeably read as an ID (model set ID) corresponding to a set of AI models. Also, in the present disclosure, the model ID may be interchangeably read as a meta information ID. The meta information (or meta information ID) may be associated with information regarding the beam (beam setting) as described above. For example, the meta information (or meta information ID) may be used by the UE to select an AI model taking into account which beam the BS is using, or may be used to notify the BS of which beam to use to apply the AI model deployed by the UE. Also, 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.

[Supplementary Note 2: Notification of information to UE]
In the above-described embodiment, any information may be notified to the UE (from the NW) (in other words, any information received from the BS in the UE) using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal/channel (e.g., PDCCH, PDSCH, reference signal), or a combination thereof.

When the above notification is performed by a MAC CE, the MAC CE may be identified by including in the MAC subheader a new Logical Channel ID (LCID) that is not specified in existing standards.

When the notification is made by a DCI, the notification may be made by a specific field of the DCI, a Radio Network Temporary Identifier (RNTI) used to scramble Cyclic Redundancy Check (CRC) bits assigned to the DCI, the format of the DCI, etc.

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

[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.

If the notification is made by a MAC CE, the MAC CE may be identified by including a new LCID in the MAC subheader that is not specified in existing standards.

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

Furthermore, in the above-mentioned embodiments, notification of any information from the UE may be performed periodically, semi-persistently, or aperiodically.

[Application of each embodiment]
At least one of the above-mentioned embodiments may be applied when a specific condition is met, which may be specified in a standard or may be notified to a UE/BS using higher layer signaling/physical layer signaling.

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

The particular UE capability may indicate support for particular processing/operations/control/information for at least one of the above embodiments/options/options.

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

The above-mentioned specific UE capabilities may be capabilities that are applied across all duplexing methods (commonly regardless of the duplexing method), or may be capabilities for each duplexing method (e.g., Time Division Duplex (TDD) and Frequency Division Duplex (FDD)).

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

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

(Additional Note)
With respect to one embodiment of the present disclosure, the following invention is noted.
[Appendix A-1]
A terminal having a receiving unit that receives settings for performance monitoring of artificial intelligence (AI)-based channel state information (CSI) reports, and a control unit that controls at least one of measuring and storing AI-based CSI and generating the CSI reports based on the settings.
[Appendix A-2]
The control unit controls measurement and storage of the CSI using reference signal resources arranged within the specific window based on information regarding a specific time window included in the configuration. The terminal according to Supplementary Note A-1.
[Appendix A-3]
The terminal according to Supplementary Note A-1 or Supplementary Note A-2, wherein the control unit controls measurement and storage of the CSI based on information regarding a performance monitoring event included in the configuration.
[Appendix A-4]
When multiple CSIs are generated in generating the CSI report, the control unit generates the multiple CSIs using an element of CSI indicated by an absolute value and an element of CSI indicated by a differential value. A terminal according to any one of Appendix A-1 to Appendix A-3.
[Appendix B-1]
A terminal having a control unit that controls transmission of a request for network-side performance monitoring regarding artificial intelligence (AI)-based channel state information (CSI) reporting, and a receiving unit that receives an instruction regarding the CSI reporting transmitted based on the setting.
[Appendix B-2]
The terminal according to supplementary note B-1, wherein the request is information indicating that a condition regarding the network side monitoring is satisfied, or information indicating whether the network side monitoring is necessary.
[Appendix B-3]
The terminal according to claim 1 or 2, wherein the control unit determines to send the request when a specific condition is satisfied.
[Appendix B-4]
The terminal according to any one of Supplementary Note B-1 to Supplementary Note B-3, wherein the instruction includes at least one of information on the number of CSI to be reported, information on at least one of resources and channels for reporting, information on the number of CSI to be reported in each resource unit, information on at least one method of quantizing, compressing, and encoding the CSI, an index related to the CSI report, and an index related to the CSI resource.

(Wireless communication system)
A 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 of these methods.

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

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.

In EN-DC, the LTE (E-UTRA) base station (eNB) is the master node (MN), and the NR base station (gNB) is the 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.

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

The wireless communication system 1 may include a base station 11 that forms a macrocell C1 with a relatively wide coverage, and base stations 12 (12a-12c) that are arranged within the macrocell C1 and form a small cell C2 that is narrower than the macrocell C1. A user terminal 20 may be located within at least one of the cells. The arrangement and number of each cell and user terminal 20 are not limited to the embodiment shown in the figure. Hereinafter, when there is no need to distinguish between the base stations 11 and 12, they will be collectively referred to as base station 10.

The user terminal 20 may be connected to at least one of the multiple base stations 10. The user terminal 20 may utilize at least one of carrier aggregation (CA) using multiple component carriers (CC) and dual connectivity (DC).

Each CC may be included in at least one of a first frequency band (Frequency Range 1 (FR1)) and a second frequency band (Frequency Range 2 (FR2)). Macro cell C1 may be included in FR1, and small cell C2 may be included in FR2. For example, FR1 may be a frequency band below 6 GHz (sub-6 GHz), and FR2 may be a frequency band above 24 GHz (above-24 GHz). Note that the frequency bands and definitions of FR1 and FR2 are not limited to these, and for example, FR1 may correspond to a higher frequency band than FR2.

In addition, the user terminal 20 may communicate using at least one of Time Division Duplex (TDD) and Frequency Division Duplex (FDD) in each CC.

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

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

The core network 30 may include network functions (Network Functions (NF)) such as, for example, a User Plane Function (UPF), an Access and Mobility management Function (AMF), a Session Management Function (SMF), a Unified Data Management (UDM), an Application Function (AF), a Data Network (DN), a Location Management Function (LMF), and Operation, Administration and Maintenance (Management) (OAM). Note that multiple functions may be provided by one network node. In addition, communication with an external network (e.g., the Internet) may be performed via the DN.

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

In the wireless communication system 1, a wireless access method based on Orthogonal Frequency Division Multiplexing (OFDM) may be used. For example, in at least one of the downlink (DL) and uplink (UL), 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), etc. may be used.

The radio access method may also be called a waveform. Note that in the wireless communication system 1, other radio access methods (e.g., other single-carrier transmission methods, other multi-carrier transmission methods) may be used for the UL and DL radio access methods.

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.

In addition, 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)), etc. may be used as an uplink channel.

User data, upper layer control information, System Information Block (SIB), etc. are transmitted via PDSCH. User data, upper layer control information, etc. may also be transmitted via PUSCH. Furthermore, Master Information Block (MIB) may also be transmitted via PBCH.

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

Note that the DCI for scheduling the PDSCH may be called a DL assignment or DL DCI, and the DCI for scheduling the PUSCH may be called a UL grant or UL DCI. Note that the PDSCH may be interpreted as DL data, and the PUSCH may be interpreted as UL data.

A control resource set (COntrol REsource SET (CORESET)) and a search space may be used to detect the PDCCH. The CORESET corresponds to the resources to search for DCI. The search space corresponds to the search region and search method of PDCCH candidates. One CORESET may be associated with one or multiple search spaces. The UE may monitor the CORESET associated with a search space based on the search space configuration.

A search space may correspond to PDCCH candidates corresponding to one or more aggregation levels. One or more search spaces may be referred to as a search space set. Note that the terms "search space," "search space set," "search space setting," "search space set setting," "CORESET," "CORESET setting," etc. in this disclosure may be read as interchangeable.

The PUCCH may transmit uplink control information (UCI) including at least one of channel state information (CSI), delivery confirmation information (which may be called, for example, Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), ACK/NACK, etc.), and a scheduling request (SR). The PRACH may transmit a random access preamble for establishing a connection with a cell.

Note that in this disclosure, downlink, uplink, etc. may be expressed without adding "link." Also, various channels may be expressed without adding "Physical" to the beginning.

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.

The synchronization signal may be, for example, at least one of a Primary Synchronization Signal (PSS) and a Secondary Synchronization Signal (SSS). A signal block including an SS (PSS, SSS) and a PBCH (and a DMRS for PBCH) may be called an SS/PBCH block, an SS Block (SSB), etc. In addition, the SS, SSB, etc. may also be called a reference signal.

In addition, in the wireless communication system 1, a measurement reference signal (Sounding Reference Signal (SRS)), a demodulation reference signal (DMRS), etc. 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).

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

Note that this example mainly shows the functional blocks of the characteristic parts of this embodiment, and the base station 10 may also be assumed to have other functional blocks necessary for wireless communication. Some of the processing of each part described below may be omitted.

The control unit 110 controls the entire base station 10. The control unit 110 can be configured from a controller, a control circuit, etc., which are described based on a common understanding in the technical field to which this disclosure pertains.

The control unit 110 may control signal generation, scheduling (e.g., resource allocation, mapping), etc. The control unit 110 may control transmission and reception using the transceiver unit 120, the transceiver antenna 130, and the transmission path interface 140, measurement, etc. The control unit 110 may generate data, control information, sequences, etc. to be transmitted as signals, and transfer them to the transceiver unit 120. The control unit 110 may perform call processing of communication channels (setting, release, etc.), status management of the base station 10, management of radio resources, etc.

The transceiver unit 120 may include a baseband unit 121, a radio frequency (RF) unit 122, and a measurement unit 123. The baseband unit 121 may include a transmission processing unit 1211 and a reception processing unit 1212. The transceiver unit 120 may be composed of a transmitter/receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transceiver circuit, etc., which are described based on a common understanding in the technical field to which the present disclosure relates.

The transceiver 120 may be configured as an integrated transceiver, or may be composed of a transmitter and a receiver. The transmitter may be composed of a transmission processing unit 1211 and an RF unit 122. The receiver may be composed of a reception processing unit 1212, an RF unit 122, and a measurement unit 123.

The transmitting/receiving antenna 130 can be configured as an antenna described based on common understanding in the technical field to which this disclosure pertains, such as an array antenna.

The transceiver 120 may transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transceiver 120 may receive the above-mentioned uplink channel, uplink reference signal, etc.

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

The transceiver 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 and control information obtained from the control unit 110 to generate a bit string to be transmitted.

The transceiver 120 (transmission processor 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.

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.

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.

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, and acquire user data, etc.

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.

The transmission path interface 140 may transmit and receive signals (backhaul signaling) between devices included in the core network 30 (e.g., network nodes providing NF), other base stations 10, etc., and may acquire and transmit user data (user plane data), control plane data, etc. for the user terminal 20.

Note that the transmitting section and receiving section of the base station 10 in this disclosure may be configured with at least one of the transmitting/receiving section 120, the transmitting/receiving antenna 130, and the transmission path interface 140.

The transceiver unit 120 may transmit a configuration for performance monitoring of artificial intelligence (AI)-based channel state information (CSI) reporting. The control unit 110 may use the configuration to instruct at least one of measuring and storing AI-based CSI and generating the CSI report (first/fourth embodiment).

The control unit 110 may control the reception of requests for network-side performance monitoring regarding artificial intelligence (AI)-based channel state information (CSI) reporting. The transceiver unit 120 may transmit instructions regarding the CSI reporting based on the configuration (second/third embodiment).

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

Note that this example mainly shows the functional blocks of the characteristic parts of this embodiment, and the user terminal 20 may also be assumed to have other functional blocks necessary for wireless communication. Some of the processing of each part described below may be omitted.

The control unit 210 controls the entire user terminal 20. The control unit 210 can be configured from a controller, a control circuit, etc., which are described based on a common understanding in the technical field to which this disclosure pertains.

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

The transceiver unit 220 may include a baseband unit 221, an RF unit 222, and a measurement unit 223. The baseband unit 221 may include a transmission processing unit 2211 and a reception processing unit 2212. The transceiver unit 220 may be composed of a transmitter/receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transceiver circuit, etc., which are described based on a common understanding in the technical field to which the present disclosure relates.

The transceiver unit 220 may be configured as an integrated transceiver unit, or may be composed of a transmission unit and a reception unit. The transmission unit may be composed of a transmission processing unit 2211 and an RF unit 222. The reception unit may be composed of a reception processing unit 2212, an RF unit 222, and a measurement unit 223.

The transmitting/receiving antenna 230 can be configured as an antenna described based on common understanding in the technical field to which this disclosure pertains, such as an array antenna.

The transceiver 220 may receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transceiver 220 may transmit the above-mentioned uplink channel, uplink reference signal, etc.

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

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

The transceiver 220 (transmission processor 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.

Whether or not to apply DFT processing may be based on the settings 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 above-mentioned transmission processing in order to transmit the channel using a DFT-s-OFDM waveform, and when transform precoding is not enabled, it is not necessary to perform DFT processing as the above-mentioned transmission processing.

The transceiver unit 220 (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.

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.

The transceiver 220 (reception processor 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 to acquire user data, etc.

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.

The measurement unit 223 may derive channel measurements for CSI calculation based on channel measurement resources. The channel measurement resources may be, for example, non-zero power (NZP) CSI-RS resources. The measurement unit 223 may derive interference measurements for CSI calculation based on interference measurement resources. The interference measurement resources may be at least one of NZP CSI-RS resources for interference measurement, CSI-Interference Measurement (IM) resources, etc. CSI-IM may be called CSI-Interference Management (IM) or may be interchangeably read as Zero Power (ZP) CSI-RS. In this disclosure, CSI-RS, NZP CSI-RS, ZP CSI-RS, CSI-IM, CSI-SSB, etc. may be read as interchangeable.

In addition, the transmitting unit and receiving unit of the user terminal 20 in this disclosure may be configured by at least one of the transmitting/receiving unit 220 and the transmitting/receiving antenna 230.

The transceiver unit 220 may receive a configuration for monitoring the performance of an artificial intelligence (AI)-based channel state information (CSI) report. The control unit 210 may control at least one of measuring and storing the AI-based CSI and generating the CSI report based on the configuration (first/fourth embodiment).

The control unit 210 may control the measurement and storage of the CSI using reference signal resources that are placed within a specific time window based on information about the specific time window included in the configuration (first embodiment).

The control unit 210 may control the measurement and storage of the CSI based on information about performance monitoring events included in the settings (first embodiment).

If multiple CSIs are generated in generating the CSI report, the control unit 210 may generate the multiple CSIs using an element of CSI represented by an absolute value and an element of CSI represented by a differential value (fourth embodiment).

The control unit 210 may control the transmission of requests for network-side performance monitoring regarding artificial intelligence (AI)-based channel state information (CSI) reporting. The transceiver unit 220 may receive instructions regarding the CSI reporting, which are transmitted based on the settings (second/third embodiment).

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

The control unit 210 may determine to send the request if certain conditions are met (second embodiment).

The instructions may include at least one of information regarding the number of CSIs to be reported, information regarding at least one of the resources and channels for reporting, information regarding the number of CSIs to be reported in each resource unit, information regarding at least one method of quantizing, compressing, and encoding the CSI, an index regarding the CSI report, and an index regarding the CSI resource (third embodiment).

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

Here, the functions include, but are not limited to, judgement, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, deeming, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs the transmission function may be called a transmitting unit, a transmitter, and the like. In either case, as mentioned above, there are no particular limitations on the method of realization.

For example, a base station, a user terminal, etc. in one embodiment of the present disclosure may function as a computer that performs processing of the wireless communication method of the present disclosure. FIG. 15 is a diagram showing an example of the hardware configuration of a base station and a user terminal according to one embodiment. The above-mentioned base station 10 and user terminal 20 may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

In addition, in this disclosure, terms such as apparatus, circuit, device, section, and unit may be interpreted as interchangeable. The hardware configurations of the base station 10 and the user terminal 20 may be configured to include one or more of the devices shown in the figures, or may be configured to exclude some of the devices.

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, sequentially, or using other techniques. Furthermore, the processor 1001 may be implemented by one or more chips.

The functions of the base station 10 and the user terminal 20 are realized, for example, by loading specific software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communications via the communication device 1004, and control at least one of the reading and writing of data in the memory 1002 and storage 1003.

The processor 1001, for example, operates an operating system to control the entire computer. The processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, registers, etc. For example, at least a portion of the above-mentioned control unit 110 (210), transmission/reception unit 120 (220), etc. may be realized by the processor 1001.

The processor 1001 also reads out programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to these. The programs used are those that cause a computer to execute at least some of the operations described in the above embodiments. For example, the control unit 110 (210) may be realized by a control program stored in the memory 1002 and running on the processor 1001, and similar implementations may be made for other functional blocks.

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

Storage 1003 is a computer-readable recording medium and may be composed of at least one of a flexible disk, a floppy disk, a magneto-optical disk (e.g., a compact disk (Compact Disc ROM (CD-ROM)), a digital versatile disk, a Blu-ray disk), a removable disk, a hard disk drive, a smart card, a flash memory device (e.g., a card, a stick, a key drive), a magnetic stripe, a database, a server, or other suitable storage medium. Storage 1003 may also be referred to as an auxiliary storage device.

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

The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, a Light Emitting Diode (LED) lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one structure (e.g., a touch panel).

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

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 the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

(Modification)
In addition, the terms described in this disclosure and the terms necessary for understanding this 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 read as mutually interchangeable. A signal may also be a message. A reference signal may be abbreviated as RS, and may be called a pilot, a pilot signal, or the like depending on the applied standard. A component carrier (CC) may also be called a cell, a frequency carrier, a carrier frequency, or the like.

A radio frame may be composed of one or more periods (frames) in the time domain. Each of the one or more periods (frames) constituting a radio frame may be called a subframe. 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.

Here, the numerology may be a communication parameter that is applied to at least one of the transmission and reception of a signal or channel. The numerology may indicate, for example, at least one of the following: SubCarrier Spacing (SCS), bandwidth, symbol length, cyclic prefix length, Transmission Time Interval (TTI), number of symbols per TTI, radio frame configuration, a specific filtering process performed by the transceiver in the frequency domain, a specific windowing process performed by the transceiver in the time domain, etc.

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

A slot may include multiple minislots. Each minislot may consist of one or multiple symbols in the time domain. A minislot may also be called a subslot. A minislot may consist of fewer symbols than a slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a minislot may be called PDSCH (PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using a minislot may be called PDSCH (PUSCH) mapping type B.

A radio frame, subframe, slot, minislot, and symbol all represent time units when transmitting a signal. A different name may be used for radio frame, subframe, slot, minislot, and symbol. Note that the time units such as frame, subframe, slot, minislot, and symbol in this disclosure may be read as interchangeable.

For example, one subframe may be called a TTI, multiple consecutive subframes may be called a TTI, or one slot or one minislot may be called a TTI. In other words, at least one of the subframe and the TTI may be a subframe (1 ms) in existing LTE, a period shorter than 1 ms (e.g., 1-13 symbols), or a period longer than 1 ms. Note that the unit representing the TTI may be called a slot, minislot, etc., instead of a subframe.

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

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

Note that 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 of scheduling. In addition, the number of slots (minislots) that constitute the minimum time unit of scheduling may be controlled.

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

Note that 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 equal to or greater than 1 ms.

A resource block (RB) is a resource allocation unit in the time domain and frequency domain, and may include one or more consecutive subcarriers in the frequency domain. The number of subcarriers included in an RB may be the same regardless of numerology, and may be, for example, 12. The number of subcarriers included in an RB may be determined based on numerology.

Furthermore, an RB may include one or more symbols in the time domain and may be one slot, one minislot, one subframe, or one TTI in length. One TTI, one subframe, etc. may each be composed of one or more resource blocks.

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.

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

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 an index of the RB relative to a common reference point of the carrier. PRBs may be defined in a BWP and numbered within the BWP.

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

At least one of the configured BWPs may be active, and the UE may not expect to transmit or receive a given signal/channel outside the active BWP. Note that "cell," "carrier," etc. in this disclosure may be read as "BWP."

Note that the above-mentioned structures of radio frames, subframes, slots, minislots, and symbols 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, as well as the number of symbols in a TTI, the symbol length, and the cyclic prefix (CP) length can be changed in various ways.

In addition, the information, parameters, etc. described in this disclosure may be represented using absolute values, may be represented using relative values from a predetermined value, or may be represented using other corresponding information. For example, a radio resource may be indicated by a predetermined index.

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

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

In addition, information, signals, etc. may be output from a higher layer to a lower layer and/or from a lower layer to a higher layer. Information, signals, etc. may be input/output via multiple network nodes.

Input/output information, signals, etc. may be stored in a specific location (e.g., memory) or may be managed using a management table. Input/output information, signals, etc. may be overwritten, updated, or added to. Output information, signals, etc. may be deleted. Input information, signals, etc. may be transmitted to another device.

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

The physical layer signaling may be called Layer 1/Layer 2 (L1/L2) control information (L1/L2 control signal), L1 control information (L1 control signal), etc. The RRC signaling may be called an RRC message, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, etc. The MAC signaling may be notified, for example, using a MAC Control Element (CE).

Furthermore, notification of specified information (e.g., notification that "X is the case") is not limited to explicit notification, but may be implicit (e.g., by not notifying the specified information or by notifying other information).

The determination may be based on a value represented by a single bit (0 or 1), a Boolean value represented by true or false, or a comparison of numerical values (e.g., with a predetermined value).

Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

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

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

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.

In the present disclosure, the 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 resource may be interchangeably read as a resource for any signal/channel (e.g., a reference signal resource, an SRS resource, etc.). The resource may include time/frequency/code/space/power resources. The spatial domain transmission filter may include at least one of a spatial domain transmission filter and a spatial domain reception filter.

The above groups may include, for example, at least one of a spatial relationship group, a Code Division Multiplexing (CDM) group, a Reference Signal (RS) group, a Control Resource Set (CORESET) group, a PUCCH group, an antenna port group (e.g., a DMRS port group), a layer group, a resource group, a beam group, an antenna group, a panel group, etc.

Furthermore, in this disclosure, beam, SRS Resource Indicator (SRI), CORESET, CORESET pool, PDSCH, PUSCH, codeword (CW), transport block (TB), RS, etc. may be read as interchangeable.

Furthermore, in this 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 interpreted as interchangeable.

Furthermore, in this disclosure, "QCL", "QCL assumptions", "QCL relationship", "QCL type information", "QCL property/properties", "specific QCL type (e.g., Type A, Type D) characteristics", "specific QCL type (e.g., Type A, Type D)", etc. may be read as interchangeable.

In this disclosure, the terms index, identifier (ID), indicator, indication, resource ID, etc. may be interchangeable. In this disclosure, the terms sequence, list, set, group, cluster, subset, etc. may be interchangeable.

Furthermore, the spatial relationship information identifier (ID) (TCI state ID) and the spatial relationship information (TCI state) may be interchangeable. "Spatial relationship information (TCI state)" may be interchangeable as "set of spatial relationship information (TCI state)", "one or more pieces of spatial relationship information", etc. TCI state and TCI may be interchangeable. Spatial relationship information and spatial relationship may be interchangeable.

In this 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.

A base station can accommodate one or more (e.g., three) cells. When a base station accommodates multiple cells, the entire coverage area of the base station can be divided into multiple smaller areas, and each smaller area can also provide communication services by a base station subsystem (e.g., a small base station for indoor use (Remote Radio Head (RRH))). The term "cell" or "sector" refers to a part or the entire coverage area of at least one of the base station and base station subsystems that provide communication services in this coverage.

In this 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.

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

A mobile station may also be referred to as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

At least one of the base station and the mobile station may be called a transmitting device, a receiving device, a wireless communication device, etc. In addition, at least one of the base station and the mobile station may be a device mounted on a moving object, the moving object itself, etc.

The moving body in question refers to an object that can move, and the moving speed is arbitrary, and of course includes the case where the moving body is stationary. The moving body in question includes, but is not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcarts, rickshaws, ships and other watercraft, airplanes, rockets, artificial satellites, drones, multicopters, quadcopters, balloons, and objects mounted on these. The moving body in question may also be a moving body that moves autonomously based on an operating command.

The moving object may be a vehicle (e.g., a car, an airplane, etc.), an unmanned moving object (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned). 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.

FIG. 16 is a diagram showing an example of a vehicle according to an embodiment. The vehicle 40 includes a drive unit 41, a steering unit 42, an accelerator pedal 43, a brake pedal 44, a shift lever 45, left and right front wheels 46, left and right rear wheels 47, an axle 48, an electronic control unit 49, various sensors (including a current sensor 50, a rotation speed sensor 51, an air pressure sensor 52, a vehicle speed sensor 53, an acceleration sensor 54, an accelerator pedal sensor 55, a brake pedal sensor 56, a shift lever sensor 57, and an object detection sensor 58), an information service unit 59, and a communication module 60.

The drive unit 41 is composed of at least one of an engine, a motor, and a hybrid of an engine and a motor, for example. The steering unit 42 includes at least a steering wheel (also called a handlebar), and is configured to steer at least one of the front wheels 46 and the rear wheels 47 based on the operation of the steering wheel operated by the user.

The electronic control unit 49 is composed of a microprocessor 61, memory (ROM, RAM) 62, and a communication port (e.g., an Input/Output (IO) port) 63. Signals are input to the electronic control unit 49 from various sensors 50-58 provided in the vehicle. The electronic control unit 49 may also be called an Electronic Control Unit (ECU).

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

The information service unit 59 is composed of various devices, such as a car navigation system, audio system, speakers, displays, televisions, and radios, for providing (outputting) various information such as driving information, traffic information, and entertainment information, and one or more ECUs that control these devices. The information service unit 59 uses information acquired from external devices via the communication module 60, etc., to provide various information/services (e.g., multimedia information/multimedia services) to the occupants of the vehicle 40.

The information service unit 59 may include input devices (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, a touch panel, etc.) that accept input from the outside, and may also include output devices (e.g., a display, a speaker, an LED lamp, a touch panel, etc.) that perform output to the outside.

The driving assistance system unit 64 is composed of various devices that provide functions for preventing accidents and reducing the driver's driving load, such as a millimeter wave radar, a Light Detection and Ranging (LiDAR), a camera, a positioning locator (e.g., a Global Navigation Satellite System (GNSS)), map information (e.g., a High Definition (HD) map, an Autonomous Vehicle (AV) map, etc.), a gyro system (e.g., an Inertial Measurement Unit (IMU), an Inertial Navigation System (INS), etc.), an Artificial Intelligence (AI) chip, and an AI processor, and one or more ECUs that control these devices. The driving assistance system unit 64 also transmits and receives various information via the communication module 60 to realize a driving assistance function or an autonomous driving function.

The communication module 60 can communicate with the microprocessor 61 and components of the vehicle 40 via the communication port 63. For example, the communication module 60 transmits and receives data (information) via the communication port 63 between the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, the microprocessor 61 and memory (ROM, RAM) 62 in the electronic control unit 49, and the various sensors 50-58 that are provided on the vehicle 40.

The communication module 60 is a communication device that can be controlled by the microprocessor 61 of the electronic control unit 49 and can communicate with an external device. For example, it transmits and receives various information to and from the external device via wireless communication. The communication module 60 may be located either inside or outside the electronic control unit 49. The external device may be, for example, the above-mentioned base station 10 or user terminal 20. The communication module 60 may also be, for example, at least one of the above-mentioned base station 10 and user terminal 20 (it may function as at least one of the base station 10 and user terminal 20).

The communication module 60 may transmit at least one of the signals from the various sensors 50-58 described above input to the electronic control unit 49, information obtained based on the signals, and information based on input from the outside (user) obtained via the information service unit 59 to an external device via wireless communication. The electronic control unit 49, the various sensors 50-58, the information service unit 59, etc. may be referred to as input units that accept input. For example, the PUSCH transmitted by the communication module 60 may include information based on the above input.

The communication module 60 receives various information (traffic information, signal information, vehicle distance information, etc.) transmitted from an external device and displays it on an information service unit 59 provided in the vehicle. The information service unit 59 may also be called an output unit that outputs information (for example, outputs information to a device such as a display or speaker based on the PDSCH (or data/information decoded from the PDSCH) received by the communication module 60).

The communication module 60 also stores various information received from external devices in memory 62 that can be used by the microprocessor 61. Based on the information stored in memory 62, the microprocessor 61 may control the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, various sensors 50-58, and the like provided on the vehicle 40.

Furthermore, the base station in the present disclosure may be read as a user terminal. For example, each aspect/embodiment of the present disclosure may be applied to a configuration in which communication between a base station and a user terminal is replaced with communication between multiple user terminals (which may be called, for example, Device-to-Device (D2D), Vehicle-to-Everything (V2X), etc.). 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, the uplink channel, downlink channel, etc. may be read as the sidelink channel.

Similarly, the user terminal in this disclosure may be interpreted as a base station. In this case, the base station 10 may be configured to have the functions of the user terminal 20 described above.

In this disclosure, operations that are described as being performed by a base station may in some cases be performed by its upper node. In a network that includes one or more network nodes having base stations, it is clear that various operations performed for communication with terminals may be performed by the base station, one or more network nodes other than the base station (such as, but not limited to, a Mobility Management Entity (MME) or a Serving-Gateway (S-GW)), or a combination of these.

Each aspect/embodiment described in this disclosure may be used alone, in combination, or switched between depending on the implementation. In addition, the processing procedures, sequences, flow charts, etc. of each aspect/embodiment described in this disclosure may be rearranged as long as there is no inconsistency. For example, the methods described in this disclosure present elements of various steps in an exemplary order, and are not limited to the particular order presented.

Each aspect/embodiment described in this disclosure includes 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)), Future Radio Access (FRA), New-Radio The present invention may be applied to systems that use 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 802.20, Ultra-Wide Band (UWB), Bluetooth (registered trademark), and other appropriate wireless communication methods, as well as next-generation systems that are expanded, modified, created, or defined based on these. In addition, multiple systems may be combined (for example, a combination of LTE or LTE-A and 5G, etc.).

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."

Any reference to an element using a designation such as "first," "second," etc., used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and second element does not imply that only two elements may be employed or that the first element must precede the second element in some way.

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 in a table, database, or other data structure), ascertaining, etc.

"Determining" may also be considered to mean "determining" receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in a memory), etc.

Furthermore, "judgment (decision)" may be considered to mean "judging (deciding)" resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment (decision)" may be considered to mean "judging (deciding)" some kind of action. In this disclosure, "judgment (decision)" may be read as interchangeably with the actions described above.

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

In the present disclosure, "expect" may be read as "be expected". For example, "expect(s) ..." ("..." may be expressed, for example, as a that clause, a to infinitive, etc.) may be read as "be expected ...". "does not expect ..." may be read as "be not expected ...". Also, "An apparatus A is not expected ..." may be read as "An apparatus B other than apparatus A does not expect ..." (for example, if apparatus A is a UE, apparatus B may be a base station).

The "maximum transmit power" referred to in this disclosure may mean the maximum value of transmit power, may mean the nominal UE maximum transmit power, or may mean the rated UE maximum transmit power.

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

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, and the like, as well as using electromagnetic energy having wavelengths in the radio frequency range, microwave range, light (both visible and invisible) range, and the like, as some non-limiting and non-exhaustive examples.

In this 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 "combined" may also be interpreted in the same way as "different."

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." Additionally, the term "or," as used in this disclosure, is not intended to be an exclusive or.

In this disclosure, where articles have been added through translation, such as a, an, and the in English, this disclosure may include that the nouns following these articles are plural.

In this disclosure, terms such as "less than", "less than", "greater than", "more than", "equal to", etc. may be read as interchangeable. In addition, in this disclosure, terms meaning "good", "bad", "big", "small", "high", "low", "fast", "slow", "wide", "narrow", etc. may be read as interchangeable, not limited to positive, comparative and superlative. In addition, in this disclosure, terms meaning "good", "bad", "big", "small", "high", "low", "fast", "slow", "wide", "narrow", etc. may be read as interchangeable, not limited to positive, comparative and superlative, as expressions with "ith" (i is any integer) (for example, "best" may be read as "ith best").

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

In the present disclosure, "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", "B until A" and the like may be read as interchangeable. Note that A, B, etc. here may be replaced with appropriate expressions such as nouns, gerunds, and normal sentences depending on the context. Note that the time difference between A and B may be almost 0 (immediately after or immediately before). Also, a time offset may be applied to the time when A occurs. For example, "A" may be read interchangeably as "before/after the time offset at which A occurs." The time offset (e.g., one or more symbols/slots) may be predefined or may be identified by the UE based on signaled information.

In this disclosure, timing, time, duration, time instance, any time unit (e.g., slot, subslot, symbol, subframe), period, occasion, resource, etc. may be interpreted as interchangeable.

The invention disclosed herein has been described in detail above, but it is clear to those skilled in the art that the invention disclosed herein is not limited to the embodiments described herein. The description of the present disclosure is intended for illustrative purposes only and does not imply any limitations on the invention disclosed herein.

Claims (6)

A control unit for controlling transmission of a request for network side performance monitoring regarding artificial intelligence (AI) based channel state information (CSI) reporting;
A terminal having a receiving unit that receives an instruction regarding the CSI report, the instruction being transmitted based on the setting. The terminal according to claim 1, wherein the request is information indicating that a condition related to the network-side monitoring has been satisfied, or information indicating whether the network-side monitoring is necessary. The terminal according to claim 1, wherein the control unit determines to send the request when a specific condition is met. The terminal of claim 1, wherein the instructions include at least one of information regarding the number of CSIs to be reported, information regarding at least one of resources and channels for reporting, information regarding the number of CSIs to be reported in each resource unit, information regarding at least one method of quantizing, compressing, and encoding the CSI, an index regarding the CSI report, and an index regarding the CSI resource. controlling transmission of requests for network side performance monitoring for artificial intelligence (AI) based channel state information (CSI) reporting;
A wireless communication method for a terminal, comprising: a step of receiving an instruction regarding the CSI report, the instruction being transmitted based on the setting. A control unit for controlling reception of a request for network side performance monitoring regarding artificial intelligence (AI) based channel state information (CSI) reporting;
A base station comprising: a transmitter that transmits an instruction regarding the CSI report based on the setting.

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