Terminals, wireless communication methods, base stations and systems

Abstract

A terminal according to one aspect of the present disclosure is characterized by comprising: a control unit that determines input to an artificial intelligence (AI) / machine learning (ML) model for channel state information reference signal (CSI) feedback, and applies the input to the AI / ML model; and a transmission unit that transmits information relating to CSI using output of the AI / ML model. According to the one aspect of the present disclosure, appropriate CSI feedback using AI can be achieved.

Description

[Technical Field]

[0001] This disclosure relates to terminals and wireless communication methods in next-generation mobile communication systems. 、 base station and system Regarding. [Background technology]

[0002] Long Term Evolution (LTE) was specified for Universal Mobile Telecommunications System (UMTS) networks with the aim of achieving even higher data rates and lower latency (Non-Patent Document 1). Furthermore, LTE-Advanced (3GPP Rel.10-14) was specified for the aim of further increasing capacity and sophistication of LTE (Third Generation Partnership Project (3GPP) Release (Rel.) 8, 9).

[0003] Successor systems to LTE (for example, 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 being considered. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] 3GPP TS 36.300 V8.12.0 “Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description; Stage 2 (Release 8)”, April 2010 [Overview of the project] [Problems that the invention aims to solve]

[0005] Regarding future wireless communication technologies, the use of artificial intelligence (AI) technologies such as machine learning (ML) for network / device control and management is being considered. For example, the use of AI technology is being considered for improving channel state information reference signal (CSI) feedback in future wireless communication technologies, such as reducing overhead, improving accuracy, and predicting. AI-based CSI feedback may also be called AI-aided CSI feedback or AI-based CSI feedback.

[0006] However, the specific details of AI-assisted CSI feedback have not yet been considered. If these are not properly defined, there is a risk that appropriate CSI feedback using AI cannot be implemented. This could prevent the achievement of appropriate overhead reduction, high-precision channel estimation, and highly efficient resource utilization, potentially hindering improvements in communication throughput and communication quality.

[0007] Therefore, this disclosure provides a terminal and wireless communication method that can realize appropriate CSI feedback using AI. 、 base station and system One of the objectives is to provide [this]. [Means for solving the problem]

[0008] A terminal relating to one aspect of this disclosure is Channel State Information n(A control unit that determines an input to an Artificial Intelligence (AI) / Machine Learning (ML) model for CSI feedback and applies the input to the AI / ML model, and a transmission unit that transmits information related to CSI using the output of the AI / ML model. Furthermore, if the control unit selects a precoding matrix or eigenvector that has undergone at least one of preprocessing and quantization as the input, it determines that the larger the Rank Indicator (RI), the larger the output payload size of the AI / ML model. It is characterized by doing so.

Effect of the Invention

[0009] According to one aspect of the present disclosure, appropriate CSI feedback using AI can be realized.

Brief Description of the Drawings

[0010] [Figure 1] FIG. 1 is a diagram showing an example of a framework for managing an AI model. [Figure 2] FIG. 2 is a diagram showing an example of specifying an AI model. [Figure 3] FIG. 3 is a diagram showing an example of an AI model. [Figure 4] FIG. 4 is a diagram showing an example of AI-based CSI feedback. [Figure 5] FIG. 5 is a diagram showing an overview of a CSI report. [Figure 6] FIG. 6 is a diagram showing an example of the relationship between ranks and layers and an AI / ML model. [Figure 7] FIG. 7 is a diagram showing an example of encoder selection. <000​​​​​​​​​​​​​​​ [Figure 13] FIG. 13 is a diagram showing an example of selection of an AI / ML model. [Figure 14] FIG. 14 is a diagram showing an example of payload size for each RI. [Figure 15] FIG. 15 is a diagram showing determination of an AI / ML model in the seventh embodiment. [Figure 16] FIG. 16 is a diagram showing an example of a schematic configuration of a wireless communication system according to an embodiment. [Figure 17] FIG. 17 is a diagram showing an example of a configuration of a base station according to an embodiment. [Figure 18] FIG. 18 is a diagram showing an example of a configuration of a user terminal according to an embodiment. [Figure 19] FIG. 19 is a diagram showing an example of a hardware configuration of a base station and a user terminal according to an embodiment. [Figure 20] FIG. 20 is a diagram showing an example of a vehicle according to an embodiment.

MODE FOR CARRYING OUT THE INVENTION

[0011] (Application of Artificial Intelligence (AI) Technology to Wireless Communication) Regarding future wireless communication technologies, it has been studied to utilize AI technologies such as Machine Learning (ML) for network / device control, management, etc.

[0012] For example, regarding future wireless communication technologies, it has been studied to utilize AI technologies for improvement of Channel State Information Reference Signal (CSI) feedback (e.g., overhead reduction, accuracy improvement, prediction), improvement of beam management (e.g., accuracy improvement, prediction in time / space domain), improvement of position measurement (e.g., improvement of position estimation / prediction), etc.

[0013] Figure 1 shows an example of an AI model management framework. In this example, each stage related to the AI ​​model is shown as a block. This example can also be described as AI model lifecycle management.

[0014] The Data Collection stage is the phase in which data is collected for the generation / updating of an AI model. The Data Collection stage may also include data organization (e.g., deciding which data to transfer for model training / model inference) and data transfer (e.g., transferring data to entities that will be trained / inferred (e.g., UE, gNB)).

[0015] 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., data preprocessing, cleaning, formatting, transformation, etc.), model training / validation, model testing (e.g., verifying whether the trained model meets performance thresholds), model exchange (e.g., transferring the model for distributed learning), and model deployment / update (deploying / updating the model to entities that perform model inference).

[0016] 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., data preprocessing, cleaning, formatting, and transformation), model inference, model monitoring (e.g., monitoring the performance of the model inference), model performance feedback (feeding back model performance to the entities training the model), and output (providing the model output to the actors).

[0017] The Actor stage may include action triggers (e.g., decisions on whether or not to trigger an action on another entity), feedback (e.g., providing feedback on training data / inference data / information needed for performance feedback), etc.

[0018] Furthermore, training a model for mobility optimization, for example, may be performed in a network (NW) maintenance, administration, and maintenance (Management) (OAM) / gNodeB (gNB). The former offers advantages in terms of interoperability, large-capacity storage, operator manageability, and model flexibility (feature engineering, etc.). The latter offers advantages in that it eliminates the need for model update latency and data exchange for model deployment. Inference of the above model may be performed in a gNB, for example.

[0019] Furthermore, the entities used for training / inference may differ depending on the use case.

[0020] For example, in AI-assisted beam management based on measurement reports, OAM / gNB may perform model training and gNB may perform model inference.

[0021] For AI-assisted UE-assisted positioning, a Location Management Function (LMF) may perform model training and model inference.

[0022] For CSI feedback / channel estimation using an autoencoder, the OAM / gNB / UE may perform model training, and the gNB / UE may perform model inference (jointly).

[0023] 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.

[0024] Incidentally, it is desirable that data / AI models be treated as proprietary assets. For example, creating highly accurate AI models is extremely costly and time-consuming, so if the contents of an AI model created by one company become known to another company, it can result in significant disadvantages. For this reason, consideration is being given to making some information about AI models unavailable (or making it impossible to infer) for UE / gNBs provided by different vendors.

[0025] An identifier (ID)-based model approach could be one way to manage AI models in such scenarios. For example, a network / gNB might not know the details of an AI model, but only some information about it (e.g., which ML models are being used for what purpose in the UE) for AI model management purposes.

[0026] Figure 2 shows an example of specifying an AI model. In this example, the UE and NW (for example, the base station (BS)) can recognize models #1 and #2 (they do not need to fully understand the details of the models). The UE may report the performance of model #1 and model #2 to the NW, and the NW may instruct the UE on which AI model to use.

[0027] In this disclosure, the UE / BS may input channel status information, reference signal measurements, etc., to the ML model and output high-precision channel status information / measurements / beam selection / position, future channel status information / wireless link quality, etc.

[0028] In this disclosure, AI may be interpreted as an object (also called a subject, object, data, function, program, etc.) having (implementing) at least one of the following characteristics: • Estimation based on observed or collected information • Selection based on observed or collected information. • Predictions based on observed or collected information.

[0029] In this disclosure, an object may be, for example, a device such as a terminal or base station. Furthermore, in this disclosure, an object may refer to a program / model / entity operating on such device.

[0030] Furthermore, in this disclosure, the ML model may be replaced with an object having (implementing) at least one of the following features: • By providing information (feeding), estimates are generated. By providing information, predict the estimated value. By providing information, we can discover features. • By providing information, the user can select an action.

[0031] Furthermore, in this disclosure, AI, AI / ML, AI / ML model, ML model, model, AI model, predictive analytics, predictive analytics model, etc., may be interpreted interchangeably. Also, an ML model may be derived using at least one of the following: 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, a model may be interpreted as at least one of the following: encoder, decoder, tool, etc.

[0032] An ML model outputs at least one piece of information based on the input information, such as an estimate, a prediction, a chosen action, or a classification.

[0033] ML models may include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning may be used to learn general rules for mapping inputs to outputs. Unsupervised learning may be used to learn data features. Reinforcement learning may be used to learn actions to maximize an objective (goal).

[0034] In this disclosure, terms such as generation, calculation, and derivation may be interpreted interchangeably. In this disclosure, terms such as implementation, operation, function, and execution may be interpreted interchangeably. In this disclosure, terms such as training, learning, updating, and retraining may be interpreted interchangeably. In this disclosure, terms such as inference, after-training, production use, and actual use may be interpreted interchangeably. Signal may be interpreted interchangeably with signal / channel.

[0035] (CSI report (CSI report or reporting)) In Rel.15 NR, a terminal (also called a user terminal, User Equipment (UE), etc.) generates (determines, calculates, estimates, measures, etc.) channel state information (CSI) based on a reference signal (RS) (or a resource for the RS), and transmits (reports, provides feedback, etc.) the generated CSI to the 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)).

[0036] The RS used to generate the CSI may be at least one of the following: a Channel State Information Reference Signal (CSI-RS), a Synchronization Signal / Physical Broadcast Channel (SS / PBCH) block, a Synchronization Signal (SS), or a Demodulation Reference Signal (DMRS).

[0037] The CSI-RS may include at least one of Non Zero Power (NZP) CSI-RS and CSI-Interference Management (CSI-IM). The SS / PBCH block is a block that includes SS and PBCH (and the corresponding DMRS), and may be called an SS block (SSB), etc. The SS may also include at least one of a Primary Synchronization Signal (PSS) and a Secondary Synchronization Signal (SSS).

[0038] Furthermore, CSI may include at least one of the following: Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), CSI-RS Resource Indicator (CRI), SS / PBCH Block Resource Indicator (SSBRI), Layer Indicator (LI), Rank Indicator (RI), L1-RSRP (Layer 1 Reference Signal Received Power), L1-RSRQ (Reference Signal Received Quality), L1-SINR (Signal to Interference plus Noise Ratio), and L1-SNR (Signal to Noise Ratio).

[0039] The UE may receive information regarding CSI reporting (report configuration information) and control CSI reporting based on said report configuration information. Such report configuration information may be, for example, the "CSI-ReportConfig" information element (IE) of Radio Resource Control (RRC). In this disclosure, RRC IE may be interpreted interchangeably with RRC parameters, higher layer parameters, etc.

[0040] The reporting configuration information (for example, "CSI-ReportConfig" in RRC IE) may include at least one of the following: • Information regarding the type of CSI report (report type information, e.g., "reportConfigType" in RRC IE) • Information regarding one or more CSI quantities (one or more CSI parameters) to be reported (report quantity information, e.g., "reportQuantity" in RRC IE) • Information regarding the RS resource used to generate the quantity (the CSI parameter) in question (resource information, for example, "CSI-ResourceConfigId" in RRC IE). • Information regarding the frequency domain covered by the CSI report (frequency domain information, for example, "reportFreqConfiguration" in RRC IE)

[0041] For example, the reporting type information may indicate a periodic CSI (P-CSI) report, an aperiodic CSI (A-CSI) report, or a semi-persistent CSI (SP-CSI) report.

[0042] Furthermore, the reported quantity information may specify at least one combination of the above CSI parameters (e.g., CRI, RI, PMI, CQI, LI, L1-RSRP, etc.).

[0043] Furthermore, resource information may also be the ID of a resource for RS. Such RS resources may include, for example, a non-zero-power CSI-RS resource or SSB and a CSI-IM resource (for example, a zero-power CSI-RS resource).

[0044] Furthermore, frequency domain information may indicate the frequency granularity of the CSI report. This frequency granularity may include, for example, wideband and subband. The wideband is the entire CSI reporting band. The wideband may be, for example, the entire carrier (component carrier (CC)), cell, serving cell, or the entire bandwidth part (BWP) within a carrier. The wideband may also be referred to as the CSI reporting band, the entire CSI reporting band, etc.

[0045] Furthermore, a subband may be part of the wideband and may consist of one or more resource blocks (RBs) or physical resource blocks (PRBs). The size of the subband may be determined according to the size of the BWP (number of PRBs).

[0046] Frequency domain information may indicate whether to report wideband or subband PMI (frequency domain information may include, for example, the RRC IE's "pmi-FormatIndicator" used to determine whether to report wideband or subband PMI). The UE may determine the frequency granularity of the CSI report (i.e., whether to report wideband or subband PMI) based on at least one of the above reported quantity information and frequency domain information.

[0047] If wideband PMI reporting is established (decided), one wideband PMI may be reported for the entire CSI reporting band. On the other hand, if subband PMI reporting is established, a single wideband indication i1 may be reported for the entire CSI reporting band, and one or more subband indications i2 (e.g., subband indications for each subband) may be reported for each subband within the entire CSI reporting band.

[0048] The UE performs channel estimation using the received RS and estimates the channel matrix H. The UE then feeds back the index (PMI) determined based on the estimated channel matrix.

[0049] PMI may represent a precoder matrix (also simply called a precoder) that the UE considers appropriate for use in downlink (DL) transmissions to the UE. Each value of PMI may correspond to a single precoder matrix. A set of PMI values ​​may correspond to a different set of precoder matrices called a precoder codebook (also simply called a codebook).

[0050] In a spatial domain, a CSI report may include one or more types of CSI. For example, the CSI may include at least one of a first type (Type 1 CSI) used for single-beam selection and a second type (Type 2 CSI) used for multi-beam selection. Single-beam can be rephrased as a single layer, and multi-beam can be rephrased as multiple beams. Furthermore, Type 1 CSI may not assume multi-user multiple input multiple output (MIMO), while Type 2 CSI may assume multi-user MIMO.

[0051] The above codebooks may include a codebook for Type 1 CSI (also called a Type 1 codebook, etc.) and a codebook for Type 2 CSI (also called a Type 2 codebook, etc.). Furthermore, Type 1 CSI may include Type 1 single-panel CSI and Type 1 multi-panel CSI, and different codebooks (Type 1 single-panel codebook and Type 1 multi-panel codebook) may be specified for each.

[0052] In this disclosure, Type 1 and Type I may be interpreted as interchangeable. In this disclosure, Type 2 and Type II may be interpreted as interchangeable.

[0053] The Uphill Control Information (UCI) type may include at least one of the following: Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), scheduling request (SR), or CSI. The UCI may be carried by PUCCH or by PUSCH.

[0054] In Rel.15 NR, the UCI may include one CSI part for wideband PMI feedback. CSI report #n will include PMI wideband information if reported.

[0055] In Rel.15 NR, the UCI may include two CSI parts for subband PMI feedback. CSI part 1 contains wideband PMI information. CSI part 2 contains one wideband PMI piece and several subband PMI pieces. CSI parts 1 and 2 are encoded separately. (AI model information) In this disclosure, AI model information may mean information including at least one of the following: • Input / output information of the AI ​​model, • Pre-processing / post-processing information for AI model input / output, • Information on AI model parameters, • Training information for AI models • Inference information for AI models, • Performance information regarding the AI ​​model.

[0056] Here, the input / output information of the above AI model may include information about at least one of the following: • Contents of input / output data (e.g., RSRP, SINR, amplitude / phase information in the channel matrix (or precoding matrix), information on the angle of arrival (AoA), information on the angle of departure (AoD), position information), • Supplementary information about the data (may also be called metadata) • Input / output data type (e.g., immutable value, floating-point number) • Quantization interval (quantization step size) of input / output data (e.g., 1 dBm for L1-RSRP), • The range of possible input / output data (e.g., [0, 1]).

[0057] In this disclosure, AoA information may include information on at least one of the azimuth angle of arrival and the zenith angle of arrival (ZoA). AoD information may include, for example, information on at least one of the azimuth angle of departure and the zenith angle of departure (ZoD).

[0058] In this disclosure, location information may be location information relating to the UE / NW. Location information may include at least one of the following: information obtained using a positioning system (e.g., satellite positioning system (Global Navigation Satellite System (GNSS), Global Positioning System (GPS), etc.)) (e.g., latitude, longitude, altitude); information about a base station adjacent to (or serving) the UE (e.g., base station / cell identifier (ID), distance between BS and UE, direction / angle of BS(UE) as seen from UE(BS), coordinates of BS(UE) as seen from UE(BS) (e.g., X / Y / Z axis coordinates), etc.); and a specific address of the UE (e.g., Internet Protocol (IP) address). The location information of the UE is not limited to information based on the location of the BS, but may also be information based on a specific point.

[0059] Location information may include information about its own implementation (for example, the location / position of the antenna, the location / position of the antenna panel, the number of antennas, the number of antenna panels, etc.).

[0060] Location information may include mobility information. Mobility information may include information indicating the mobility type, the movement speed of the UE, the acceleration of the UE, and the direction of movement of the UE, or at least one of these.

[0061] Here, the mobility type may fall under at least one of the following categories: 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.

[0062] In this disclosure, the environmental information (for the data) may also include information about the environment in which the data is acquired / used, and may include, for example, frequency information (such as a band ID) and environment type information (information indicating at least one of the following: indoor, outdoor, Urban Macro (UMa), Urban Micro (Umi)).

[0063] In this disclosure, metadata may mean, for example, information about input / output information suitable for an AI model, information about acquired / acquirable data, etc. Specifically, metadata may include information about RS (e.g., CSI-RS / SRS / SSB, etc.) beams (e.g., the angle of each beam, 3dB beamwidth, shape of the beam being directed, number of beams), gNB / UE antenna layout information, frequency information, environmental information, metadata ID, etc. The metadata may also be used as input / output for the AI ​​model.

[0064] The pre-processing / post-processing information for the input / output of the above AI model may include information about at least one of the following: Whether or not to apply normalization (e.g., Z-score normalization, min-max normalization), • Parameters for normalization (e.g., mean / variance for Z-score normalization, minimum / maximum value for minimum-maximum normalization), Whether or not to apply a specific numerical conversion method (e.g., one-hot encoding, label encoding, etc.) • Selection rules for whether or not to use the data for training.

[0065] For example, Z-score normalization (x) is performed as a preprocessing step for input information x. new Normalized input information x = (x-μ) / σ, where μ is the mean of x and σ is the standard deviation. new You can also input this into the AI ​​model, and the output y from the AI ​​model out The final output y may be obtained by applying post-processing to the result.

[0066] The parameter information for the above AI model may include information on at least one of the following: • Weight information in AI models (e.g., neuron coefficients (connection coefficients)), • Structure of the AI ​​model • Types of AI models as model components (e.g., Residual Network (ResNet), DenseNet, RefineNet, Transformer models, CRBlock, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)) • Functionality of the AI ​​model as a model component (e.g., decoder, encoder).

[0067] Furthermore, the weight information in the above AI model may include information about at least one of the following: • Bit width (size) of weight information, • Quantization interval of weight information, • Granularity of weight information, • The range of possible weight information, • Weight parameters in AI models • Information on the differences from the AI ​​model before the update (if updating), • Methods for weight initialization (e.g., zero initialization, random initialization (based on normal distribution / uniform distribution / truncated normal distribution), Xavier initialization (for sigmoid function), He initialization (for Rectified Linear Units (ReLU))).

[0068] Furthermore, the structure of the above AI model may include information about at least one of the following: • Number of layers, • Layer type (e.g., convolutional layer, activation layer, dense layer, normalization layer, pooling layer, attention layer), Layer information, • Time series-specific parameters (e.g., bidirectionality, time step), • Parameters for training (e.g., the type of function (L2 regularization, dropout function, etc.), and where to place this function (e.g., after which layer)).

[0069] The above layer information may include information about at least one of the following: • Number of neurons in each layer, • Kernel size, • Stride for pooling layer / convolutional layer, • Pooling methods (MaxPooling, AveragePooling, etc.) Command residual block information, • Number of heads, • Normalization methods (batch normalization, instance normalization, layer normalization, etc.) • Activation functions (sigmoid, tanh function, ReLU, leaky ReLU information, Maxout, Softmax).

[0070] Figure 3 shows an example of an AI model. This example shows an AI model that includes ResNet as model component #1, a transformer model as model component #2, a dense layer, and a normalization layer. Thus, one AI model may be included as a component of another AI model. Note that Figure 3 may also show an AI model where processing proceeds from left to right.

[0071] The training information for the above AI model may include information about at least one of the following: • Information for the optimization algorithm (e.g., type of optimization (Stochastic Gradient Descent (SGD)), AdaGrad, Adam, etc.), optimization parameters (learning rate, momentum information, etc.), • Information on the loss function (for example, information on the metrics of the loss function (mean absolute error (MAE), mean squared error (MSE), cross-entropy loss, NLLLoss, Kullback-Leibler (KL) divergence, etc.)), • Parameters to be frozen for training (e.g., layers, weights) • Parameters to be updated (e.g., layer, weights) • Parameters that should be used as initial parameters for training (e.g., layers, weights), • How to train / update the AI ​​model (e.g., (recommended) number of epochs, batch size, and amount of data to use for training).

[0072] The inference information for the above AI model may include information regarding decision tree branch pruning, parameter quantization, and the functionality of the AI ​​model. Here, the functionality of the AI ​​model may be at least one of the following: time-domain beam prediction, spatial-domain beam prediction, autoencoder for CSI feedback, autoencoder for beam management, etc.

[0073] Autoencoders for CSI feedback may be used as follows: The UE inputs the CSI / channel matrix / precoding matrix to the encoder's AI model and sends the encoded bits, which are output, as CSI feedback (CSI report). BS reconstructs the CSI / channel matrix / precoding matrix, which is output by inputting the received encoded bits into the decoder's AI model.

[0074] In spatial domain beam prediction, the UE / BS may input measurement results (beam quality, e.g., RSRP) based on a sparse (or wide) beam into an AI model and output a dense (or narrow) beam quality.

[0075] In time-domain beam forecasting, the UE / BS may input time-series (past, present, etc.) measurement results (beam quality, e.g., RSRP) into an AI model to output future beam quality.

[0076] The performance information for the above AI model may include information regarding the expected value of the loss function defined for the AI ​​model.

[0077] The AI ​​model information in this disclosure may include information regarding the scope of application (applicability) of the AI ​​model. This scope may be indicated by physical cell IDs, serving cell indexes, etc. Information regarding the scope may be included in the environmental information described above.

[0078] AI model information for a specific AI model may be predetermined in the standard, or it may be notified to the UE from the Network (NW). An AI model defined in the standard may be called a reference AI model. AI model information for a reference AI model may be called reference AI model information.

[0079] Furthermore, the AI ​​model information in this disclosure may include an index for identifying the AI ​​model (which may be called, for example, an AI model index, an AI model ID, or a model ID). The AI ​​model information in this disclosure may include, in addition to or instead of, the AI ​​model index, in addition to the AI ​​model input / output information, etc. The association between the AI ​​model index and the AI ​​model information (for example, the AI ​​model input / output information) may be predetermined in the standard or notified from the network to the UE.

[0080] The AI ​​model information in this disclosure may also be referred to as AI model-related information (relevant information), or simply related information. AI model-related information does not necessarily have to explicitly include information for identifying an AI model. AI model-related information may include, for example, only metadata.

[0081] (Receiving information) In this disclosure, the UE may receive various types of information (e.g., information regarding configuration / instructions) from the NW (base station, gNB) using upper-layer signaling / physical-layer signaling (e.g., RRC signaling / MAC CE / DCI).

[0082] MAC CE may have a new Logical Channel ID (LCID) in its subheader. Existing MAC CE may be extended. For example, a new octet may be introduced.

[0083] DCI may have existing DCI fields or newly introduced DCI fields. DCI may be scrambled with Cyclic Redundancy Check (CRC) using existing Radio Network Temporary Identifiers (RNTIs) or newly introduced RNTIs. The DCI may be in an existing DCI format (DCI formats 0_0~0_2, 1_0~1_2, 2_0~2_6, 3_0~3_1) or a newly introduced DCI format.

[0084] The UE may receive information from the NW in the following types: periodically, semi-persistent (triggered by instructions from the UE or gNB), or aperiodic (triggered by instructions from the UE or gNB).

[0085] (Reporting information) In this disclosure, the UE may transmit (report) various types of information to the NW (base stations, gNBs) using at least one of the following: higher layer signaling (e.g., RRC messages), MAC CE, or UCI.

[0086] MAC CE may have a new Logical Channel ID (LCID) in its subheader. Existing MAC CE may be extended. For example, a new octet may be introduced.

[0087] UCI may be transmitted via either PUCCH or PUSCH.

[0088] The UE may transmit information to the NW in a periodic, semi-persistent (triggered by instructions from the UE or gNB), or aperiodic (triggered by instructions from the UE or gNB) manner.

[0089] (AI-based CSI feedback) As a typical sub-use case, spatial-frequency domain CSI compression using a two-side AI model is being considered.

[0090] Figure 4 shows an example of AI-based CSI feedback. The UE performs preprocessing, AI / ML-based CSI generation, and postprocessing on measurement results related to CSI, and transmits the encoded bits (CSI feedback information) to the NW (base station). The NW (base station) performs preprocessing, AI / ML-based CSI reconstruction, and postprocessing on the received bits to obtain the CSI (channel / precoding matrix).

[0091] Figure 5 shows an overview of the CSI report. The UE performs channel measurements on the received CSI-RS and calculates the CRI. The UE may calculate the CSI parameters assuming LI, CQI, PMI, and RI dependencies between the CSI parameters (if reported). For example, LI is calculated based on the reported CQI, PMI, RI, and CRI. CQI is calculated based on the reported PMI, RI, and CRI. PMI is calculated based on the reported RI and CRI. RI is calculated based on the reported CRI.

[0092] Figure 6 shows an example of the relationship between rank, layer, and AI / ML model. When the number of receiving antennas > 1 and Rank > 1, the AI / ML model for CSI compression of eigenvectors will have multiple modes. For example, as Mode 1, a per-layer model is applied. As Mode 2, a per-rank model is applied. Rank corresponds to the number of layers.

[0093] When the number of receiving antennas > 1 and the Rank > 1, if a rank-specific model is used for CSI compression, there are multiple options for the AI / ML model. For example, a 3D AI / ML model such as 3D-convolutional neural networks (CNN) may be selected. Using a 3D AI / ML model enables compression using correlation between layers. Alternatively, a pre-processed 2D AI / ML model may be selected. The 2D AI / ML model is obtained by integrating functions using 2D attention and processing with a transformer on the 3D AI / ML model.

[0094] (Encoder selection) Figure 7 shows an example of encoder selection. The UE is configured with two encoders (encoder #1 and #2) from the base station (gNB) and selects one of the encoders from among several encoders.

[0095] The UE may select the encoder to use based on, for example, upper-layer signaling / physical-layer signaling (RRC / MAC CE / DCI), or other information received from the base station (e.g., target accuracy of the autoencoder, target compression ratio).

[0096] The UE may select the encoder to use based on specific rules and report information about the selected encoder, such as the encoder index, to the BS (for example, using CSI Part 1 / MAC CE / RRC in the CSI report).

[0097] For example, if the target performance cannot be achieved using the encoders notified by the base station (encoder #1, #2), the UE may decide not to use the encoders. In that case, the UE may perform an existing PMI calculation on the input information based on upper-layer signaling / physical-layer signaling (RRC / MAC CE / DCI) or specific rules, and transmit information indicating that PMI (CSI feedback) from the antenna (solid line in Figure 7).

[0098] (analysis) AI / ML-based CSI feedback (AI-assisted CSI feedback) is being considered. For example, compressing and reporting the channel matrix (H) is being explored. However, the following points remain unclear: How should RI be reported? How should the inputs for each rank level be configured? How should we configure or assume the AI / ML models corresponding to each rank level?

[0099] Compressing and reporting eigenvectors (W) in AI / ML models is being considered. However, the following points remain unclear, for example: How are the "per layer" and "per rank" modes determined? • In layer-by-layer mode, how do you configure and assume the AI / ML models corresponding to each layer? How do you report RI in rank-based modes? • In rank-based modes, how are the AI / ML models configured and assumed to correspond to each rank level?

[0100] However, if these are not properly defined, it may not be possible to achieve appropriate CSI feedback using AI. This could prevent the achievement of appropriate overhead reduction, highly accurate channel estimation, and efficient resource utilization, potentially hindering improvements in communication throughput and communication quality.

[0101] The embodiments of this disclosure will be described in detail below with reference to the drawings. Each wireless communication method according to the embodiments may be applied individually or in combination.

[0102] In this disclosure, "A / B" and "at least one of A and B" may be interpreted as mutually exclusive. In this disclosure, "A / B / C" may mean "at least one of A, B, and C".

[0103] In this disclosure, terms such as activate, deactivate, indicate, select, configure, update, and determine may be interpreted interchangeably. In this disclosure, terms such as support, control, controllable, operate, and operable may be interpreted interchangeably.

[0104] In this disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, higher-layer parameters, fields, Information Elements (IE), settings, etc., may be interpreted interchangeably. In this disclosure, Medium Access Control elements (MAC Control Element (CE)), update commands, activation / deactivation commands, etc., may be interpreted interchangeably.

[0105] In this disclosure, the upper-layer signaling may be, for example, Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information, or a combination thereof.

[0106] In this disclosure, MAC signaling may include, for example, MAC Control Elements (MAC CEs) and MAC Protocol Data Units (PDUs). Broadcast information may include, for example, Master Information Blocks (MIBs), System Information Blocks (SIBs), Remaining Minimum System Information (RMSIs), and Other System Information (OSIs).

[0107] In this disclosure, physical layer signaling may include, for example, Downlink Control Information (DCI) and Uplink Control Information (UCI).

[0108] In this disclosure, terms such as index, identifier (ID), indicator, and resource ID may be interpreted interchangeably. In this disclosure, terms such as sequence, list, set, group, cluster, and subset may be interpreted interchangeably.

[0109] In this disclosure, CSI-RS, Non Zero Power (NZP) CSI-RS, Zero Power (ZP) CSI-RS, and CSI Interference Measurement (CSI-IM) may be interpreted interchangeably. Furthermore, CSI-RS may include other reference signals.

[0110] Furthermore, in this disclosure, terms such as encoder, encoding, encoding, and modification / change / control by an encoder may be interpreted interchangeably. Also, in this disclosure, terms such as decoder, decoding, decoding, and modification / change / control by a decoder may be interpreted interchangeably.

[0111] In this disclosure, UCI, CSI report, CSI feedback, feedback information, feedback bits, etc., may be interpreted interchangeably. Also, in this disclosure, bits, bit sequences, bit series, sequences, values, information, values ​​obtained from bits, information obtained from bits, etc., may be interpreted interchangeably.

[0112] In this disclosure, the term "layer" (referring to an encoder) may be interpreted interchangeably with the terms "input layer," "hidden layer," etc., used in an AI model. The layers in this disclosure may correspond to at least one of the following: an input layer, a hidden layer, an output layer, a batch normalization layer, a convolutional layer, a dropout layer, a fully connected layer, etc.

[0113] In this disclosure, the layer for the precoding matrix may be interpreted as a Multi Input Multi Output (MIMO) layer, stream, etc.

[0114] In the following embodiments, the relevant entities are the UE and BS to describe an AI model relating to communication between UE and BS, but the application of each embodiment of this disclosure is not limited thereto. For example, for communication between other entities (e.g., UE-UE communication), the UE and BS in the embodiments below may be replaced with a first UE and a second UE. In other words, the UE, BS, etc. in this disclosure may all be replaced with any UE / BS.

[0115] In this disclosure, rank and RI may be interpreted interchangeably. Network (NW), base station and gNB may be interpreted interchangeably. In this disclosure, input and input information may be interpreted interchangeably.

[0116] (Wireless communication method) <Embodiment 0> The UE determines the inputs (input information) for the AI / ML model for AI-based CSI feedback. It then applies the determined inputs to the AI / ML model and transmits information about the CSI (e.g., encoded bits in Figure 4) using the output (output information) of the AI / ML model to the base station. The following input options are available (Figure 8). p represents preprocessing, and q represents quantization. Each input (H, W, pH…) corresponds to one or more AI / ML models (AI / ML #1~N). • Channel matrix (H). • Precoding matrix / eigenvector (W). • Pretreatment / quantized H (pH, QH, Qp-H). • Preprocessing / quantized W (QW, pW, Qp-W).

[0117] The UE may determine an input according to specific rules defined in the specification or set by the NW (base station), and report the determined input. The said setting may apply the example of (information reception) described above. The reporting may apply the example of (information reporting) described above. For example, when the SINR < threshold, the UE may determine H (or pre - processed / post - processed / quantized H) as the input. For example, when the uplink resources are limited, the UE may determine Q - W (quantized W) as the input.

[0118] The UE may set the input from the NW (base station). The said setting may be transmitted to the UE according to the example of (information reception) described above.

[0119] <The first embodiment> As shown in Steps 1 - 3 below, as pre - processing, the UE may perform rank adaptation on the channel matrix (H). Figure 9A is a diagram showing the outline of pre - processing. Figure 9B is a diagram showing the details of rank adaptation. Step 1: The UE decomposes H to obtain the highest rank R and the singular matrix Λ(R). Step 2: The UE performs rank adaptation to obtain the adapted rank R a (<R) and the corresponding singular matrix Λ′(R a ) Step 3: The UE reconstructs the channel matrix (H a ) equivalent to H using Λ′(R a ).

[0120] According to this embodiment, the UE can obtain an appropriate channel matrix according to the rank by performing pre - processing using the rank.

[0121] <The second embodiment> When the UE selects the channel matrix (H) or the pre-processed / quantized H (p-H, Q-H, Qp-H) as the input to the AI / ML model, it may or may not transmit the RI information (rank information) corresponding to the input. The pre-processing may be the processing shown in the first embodiment.

[0122] FIG. 10 is a diagram showing an application example of the pre-processed H. In FIG. 10, the UE pre-processes H to obtain H a and inputs it into the AI / ML model to obtain the encoded bits. The rank R a corresponding to this H a corresponds to the RI of this embodiment.

[0123] [Option 1] The UE may transmit RI (=R a ) information. The UE may transmit the RI information in the same manner as the existing specifications. The UE may compress and transmit the RI information together with H a (or the pre-processed / quantized H a ).

[0124] [Option 2] The UE may not transmit RI (=R a ) information. In this case, the NW (base station) may infer the RI information from the input / output of the AI / ML model, or the selected / reported AI / ML model information (see the third embodiment described later), or the payload size of the reported AI-based CSI feedback (see the fourth embodiment described later).

[0125] According to this embodiment, it becomes clear whether there is RI information. By compressing the RI information or omitting the transmission, the communication capacity can be reduced. <00005​​​Figure 11 shows an example of the transmission of AI model-related information. In this example, the UE enters the cell shown in the diagram and performs initial access / handover to this cell (BS). The BS sends a message to the UE asking whether it has the capability to support AI model inference. The UE then sends capability information.

[0127] When a UE (User Environment) reports its ability to support AI model inference, the NW (Base Station (BS)) may select an appropriate AI model and transfer the selected AI model, along with information related to that AI model (hereinafter also referred to as AI model-related information, or simply related information, etc.), to the UE.

[0128] Figure 12 shows an example of AI model-related information. The related information may include at least one of the following: model ID, model function, model inputs / outputs, and scope of application.

[0129] As shown in Figure 12, the model ID may include an integer, a string, etc. The model function may include a description of the AI ​​model's function, for example, "Estimate the best CSI-RS." The model input may be the RSRP of SSB#1-#n. The model output may be the best CSI-RS index (CSI-RS resource ID). The scope may indicate, for example, that the cells corresponding to AI-assisted technology (cells that can be included in beam reports based on AI-assisted beam estimation, cells that may use AI-based prediction / estimation) are cells #1-#3. The scope may also be indicated by the physical cell ID, serving cell index, etc.

[0130] As shown in Figure 12, the AI ​​model-related information may include the model ID and other related information (e.g., model inputs / outputs), or it may include only the model ID.

[0131] The UE may associate model IDs with related information based on specific rules. For example, the UE may determine which AI / ML model to apply based on specific rules and the received model ID. In other words, the model ID may be mapped to other related information. Alternatively, the UE may receive information from the base station indicating what the related information corresponding to the model ID is.

[0132] <Third Embodiment> If the UE selects a channel matrix (H) or preprocessed / quantized H as the input to the AI / ML model, it may select an appropriate AI / ML model to use based on the RI.

[0133] [Option 1] The UE may determine (select) an AI / ML model according to specific rules defined in the specifications or set by the NW (base station), and report the determined AI / ML. The setting may be based on the example of (receiving information) described above. The reporting may be based on the example of (reporting information) described above. The report may include the AI ​​model-related information described above.

[0134] Figure 13 shows an example of AI / ML model selection. For example, if RSRP < threshold (limited to Rank 1 transmissions), the UE may decide on a 2D-CNN or a transformer as the AI / ML model. For example, if RSRP ≥ threshold (Rank 2 or higher), the UE may decide on a 3D-CNN or a 2D attention and transformer as the AI / ML model. For example, the UE may decide on an AI / ML model based on SINR.

[0135] [Option 2] The UE may configure an AI / ML model from the base station. The example of (receiving information) described above may apply to this configuration. For example, the UE may configure an AI / ML model corresponding to the RI and report the RI. The example of (reporting information) described above may apply to this report. The UE may configure a set of available AI / ML models (multiple AI / MLs), select one AI / ML from the set according to the RI value, and report the selected AI / ML model. The example of (reporting information) described above may apply to this report.

[0136] According to this embodiment, the UE can select an appropriate AI / ML model according to the RI.

[0137] <Fourth Embodiment> If the UE selects a channel matrix (H) or a preprocessed / quantized H as the input to the AI / ML model, it may determine a suitable payload size (output bits) for the output of the AI / ML model based on the rank (RI). For example, the UE may determine a larger payload size for higher ranks.

[0138] When applying an AI / ML model capable of outputting multiple bits with different payload sizes, the UE may determine which output bits to report based on rank information. The UE may also determine these output bits according to certain rules defined in the specification or rules set by the network.

[0139] Figure 14 shows examples of payload sizes for each RI (Radioisotope). In the example in Figure 14, when AI / ML #2 is selected and RI=2, the payload size is 175 bits; when RI=3, the payload size is 255 bits; and when RI=4, the payload size is 325 bits.

[0140] [Option 1] The UE may report information about which output bits are reported. This information may include, for example, the corresponding RI, payload size, or bit index.

[0141] [Option 2] The UE does not need to report information about which output bits are reported. The NW may perform blind decoding to determine which bits were reported based on the received bits.

[0142] According to this embodiment, the UE can determine an appropriate payload size (output bits) according to the RI.

[0143] <Fifth Embodiment> The UE may decide to use layer-by-layer or rank-by-rank mode for AI (AI / ML model)-based CSI feedback when a pre-coded matrix / eigenvector (W) or pre-processed / quantized W is selected as the input to the AI / ML model. The example in Figure 6 may be applied as layer-by-layer or rank-by-rank mode. The UE may decide on layer-by-layer or rank-by-rank mode based, for example, on its capabilities.

[0144] Layer-based mode refers to a mode that uses a separate AI / ML model for each layer (e.g., a different / independent AI / ML model for each layer). Similarly, rank-based mode refers to a mode that uses a separate AI / ML model for each rank (e.g., a different / independent AI / ML model for each rank).

[0145] [Option 1] The UE may determine the mode and report the decision according to specific rules defined in the specification or rules set by the NW (base station). The reporting may be subject to the examples of (reporting information) described above. For example, if the UE's computing power or memory capacity is below a threshold, a layer-based mode may be applied; otherwise, a rank-based mode may be applied.

[0146] [Option 2] The UE may configure layer-based or rank-based modes from the NW (base station). The example of (receiving information) described above may be applied to such configuration. The UE may report computing power and memory capacity to the NW.

[0147] According to this embodiment, it is possible to appropriately determine whether to use a layer-by-layer mode or a rank-by-rank mode for AI (AI / ML model) based CSI feedback.

[0148] <Sixth Embodiment> The UE may reuse the second to fourth embodiments if a precoding matrix / eigenvector (W) or a preprocessed / quantized precoding matrix / eigenvector (W) is selected as the input to the AI / ML model, and the rank-by-rank modes are determined. In other words, the channel matrix (H) in the second to fourth embodiments may be replaced with a precoding matrix / eigenvector (W).

[0149] The UE may or may not transmit RI information once W or preprocessed / quantized W is selected and the rank-by-rank mode is determined (reuse of the second embodiment).

[0150] If W or preprocessed / quantized W is selected and the rank-by-rank mode is determined, the UE may select an appropriate AI / ML model to use according to the RI (reuse of the third embodiment).

[0151] If W or preprocessed / quantized W is selected and the rank-by-rank mode is determined, the UE may select a payload size (output bits) suitable for the output of the AI / ML model according to the RI (reuse of the fourth embodiment).

[0152] <Seventh Embodiment> If a pre-coding matrix / eigenvector (W) or a pre-processed / quantized pre-coding matrix / eigenvector (W) is selected as the input to the AI / ML model, and the layer-specific mode is determined, the UE may determine (select) the AI / ML model for each layer. In this case, options 1 and 2 below may be applied.

[0153] [Option 1] The UE may determine (select) and report AI / ML models based on certain rules defined in the specifications and rules set by the NW. The above example of (receiving information) may be applied to such settings. The above example of (reporting information) may be applied to such reporting.

[0154] [Option 2] The UE may have an AI / ML model configured by the NW. The example of (receiving information) described above may apply to this configuration. The UE may have one AI / ML model configured. The UE may have a set of available AI / ML models (multiple AI / ML models) configured, select one AI / ML model from that set, and report it. The example of (reporting information) described above may apply to this report.

[0155] The UE may determine (select) the AI / ML model using one of the following methods (1) to (3): (1) The UE selects the same AI / ML model for all layers. (2) The UE selects an independent AI / ML model for each layer. (3) A combination of (1) and (2).

[0156] Figure 15 shows the determination of the AI / ML model in the seventh embodiment. In the example shown in Figure 15, the UE determines (selects) the precoding matrix / eigenvectors (W) as input to the AI / ML model and determines (selects) the AI / ML model for each layer. The same AI / ML model (AI / ML#1) may be determined for multiple layers, such as layers 1 and 2.

[0157] According to this embodiment, an appropriate AI / ML model can be determined (selected) for each layer.

[0158] <Supplement> At least one of the embodiments described above may apply only to a UE that has transmitted (reported) a particular UE capability or that supports such particular UE capability.

[0159] The particular UE capability may indicate that it supports or applies certain processing / operations / controls / information for at least one of the above embodiments / options.

[0160] Furthermore, the above-mentioned specific UE capabilities may be capabilities that apply across all frequencies (commonly regardless of frequency), capabilities per frequency (e.g., per cell, band, BWP, band combination), capabilities per frequency range (e.g., Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), capabilities per subcarrier spacing (SCS), or capabilities per feature set (FS) or feature set per component-carrier (FSPC).

[0161] Furthermore, the specific UE capabilities described above may be capabilities that apply across all duplexing schemes (common to all duplexing schemes), or they may be capabilities specific to each duplexing scheme (e.g., Time Division Duplex (TDD), Frequency Division Duplex (FDD)).

[0162] Furthermore, at least one of the embodiments described above may be applied when the UE is configured with specific information related to the embodiments described above by upper-layer signaling or physical layer signaling. For example, such specific information may be information indicating that CSI feedback using an AI / ML model is enabled, or arbitrary RRC parameters for a particular release (e.g., Rel.18).

[0163] If the UE does not support at least one of the above-mentioned specific UE capabilities or does not have the above-mentioned specific information configured, the behavior of, for example, Rel.15 / 16 may be applied.

[0164] (Note) The following invention is added with respect to one embodiment of this disclosure. [Note 1] A control unit that determines the input to an Artificial Intelligence (AI) / Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback and applies said input to the AI / ML model, A transmission unit that transmits information about CSI using the output of the AI / ML model, A terminal. [Note 2] The control unit selects a channel matrix that has undergone at least one of preprocessing and quantization as the input, The transmitting unit transmits Rank Indicator (RI) information corresponding to the channel matrix. The terminals listed in Appendix 1. [Note 3] If the control unit selects a channel matrix that has undergone at least one of preprocessing and quantization as the input, it determines the AI / ML model based on the Rank Indicator (RI). The terminals listed in Appendix 1 or Appendix 2. [Note 4] When the control unit selects a precoding matrix or eigenvector that has undergone at least one of preprocessing and quantization as the input, it decides to use either a mode that uses the AI / ML model per layer or a mode that uses the AI / ML model per rank. The terminals listed in any of the appendices 1 through 3.

[0165] (Wireless communication system) The configuration of a wireless communication system according to one embodiment of this disclosure will be described below. In this wireless communication system, communication is performed using any or a combination thereof of the wireless communication methods according to the above embodiments of this disclosure.

[0166] Figure 16 shows an example of a schematic configuration of a wireless communication system according to one embodiment. The wireless communication system 1 (which may also be simply called system 1) may be a system that realizes communication using Long Term Evolution (LTE), 5th generation mobile communication system New Radio (5G NR), etc., as specified by the Third Generation Partnership Project (3GPP).

[0167] Furthermore, the wireless communication system 1 may 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)), and so on.

[0168] 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.

[0169] The wireless communication system 1 may support dual connectivity between multiple base stations within the same RAT (for example, dual connectivity where both MN and SN are NR base stations (gNB) (NR-NR Dual Connectivity (NN-DC))).

[0170] The wireless communication system 1 may include a base station 11 that forms a macrocell C1 with relatively wide coverage, and base stations 12 (12a-12c) located within the macrocell C1 that form a small cell C2 that is narrower than the macrocell C1. User terminals 20 may be located within at least one cell. The arrangement and number of each cell and user terminal 20 are not limited to the configuration shown in the figure. Hereinafter, when base stations 11 and 12 are not distinguished, they will be collectively referred to as base station 10.

[0171] 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).

[0172] Each CC may be included in at least one of the first frequency band (Frequency Range 1 (FR1)) and the second frequency band (Frequency Range 2 (FR2)). A macrocell C1 may be included in FR1, and a small cell C2 may be included in FR2. For example, FR1 may be a frequency band of 6 GHz or less (sub-6 GHz), and FR2 may be a frequency band 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 fall in a frequency band higher than FR2.

[0173] Furthermore, the user terminal 20 may communicate using at least one of the following methods at each CC: Time Division Duplex (TDD) and Frequency Division Duplex (FDD).

[0174] Multiple base stations 10 may be connected by wire (e.g., optical fiber compliant with Common Public Radio Interface (CPRI), X2 interface, etc.) or wireless (e.g., NR communication). For example, if NR communication is used as a backhaul between base stations 11 and 12, base station 11, which is the upstream station, may be called an Integrated Access Backhaul (IAB) donor, and base station 12, which is the relay station, may be called an IAB node.

[0175] Base station 10 may be connected to the core network 30 via other base stations 10 or directly. The core network 30 may include at least one of the following: Evolved Packet Core (EPC), 5G Core Network (5GCN), Next Generation Core (NGC), etc.

[0176] The core network 30 may include network functions (NF) such as User Plane Function (UPF), Access and Mobility Management Function (AMF), Session Management Function (SMF), Unified Data Management (UDM), Application Function (AF), Data Network (DN), Location Management Function (LMF), and Operation, Administration and Maintenance (Management) (OAM). Multiple functions may be provided by a single network node. Furthermore, communication with an external network (e.g., the Internet) may occur via the DN.

[0177] The user terminal 20 may be a terminal that supports at least one of the following communication methods: LTE, LTE-A, 5G, etc.

[0178] In the wireless communication system 1, an orthogonal frequency division multiplexing (OFDM)-based wireless access scheme may be used. For example, Cyclic Prefix OFDM (CP-OFDM), Discrete Fourier Transform Spread OFDM (DFT-s-OFDM), Orthogonal Frequency Division Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-FDMA), etc., may be used in at least one of the downlink (DL) and uplink (UL).

[0179] The wireless access method may also be called a waveform. In wireless communication system 1, other wireless access methods (for example, other single-carrier transmission methods, other multi-carrier transmission methods) may be used for the UL and DL wireless access methods.

[0180] In the wireless communication system 1, a Physical Downlink Shared Channel (PDSCH), a Broadcast Channel (PBCH), or a Physical Downlink Control Channel (PDCCH) may be used as the downlink channel, shared by each user terminal 20.

[0181] Furthermore, in the wireless communication system 1, the uplink channel may include a Physical Uplink Shared Channel (PUSCH), a Physical Uplink Control Channel (PUCCH), a Physical Random Access Channel (PRACH), or the like, all of which are shared by each user terminal 20.

[0182] User data, higher-layer control information, and System Information Blocks (SIBs) are transmitted via PDSCH. User data and higher-layer control information may also be transmitted via PUSCH. Furthermore, Master Information Blocks (MIBs) may be transmitted via PBCH.

[0183] Lower-layer control information may be transmitted by PDCCH. The lower-layer control information may include, for example, Downlink Control Information (DCI) which includes scheduling information for at least one of PDSCH and PUSCH.

[0184] Furthermore, the DCI that schedules PDSCH may be called a DL assignment or DL ​​DCI, and the DCI that schedules PUSCH may be called a UL grant or UL DCI. Furthermore, PDSCH may be interpreted as DL data, and PUSCH may be interpreted as UL data.

[0185] PDCCH detection may utilize a Control Resource Set (CORESET) and a search space. A CORESET corresponds to the resources used to search for DCIs. A search space corresponds to the search area and search method for PDCCH candidates. A single CORESET may be associated with one or more search spaces. The UE may monitor CORESETs associated with a particular search space based on the search space configuration.

[0186] A single search space may correspond to one or more PDCCH candidates corresponding to aggregation levels. One or more search spaces may be referred to as a search space set. In this disclosure, "search space," "search space set," "search space configuration," "search space set configuration," "CORESET," and "CORESET configuration" may be interpreted interchangeably.

[0187] PUCCH may transmit uplink control information (UCI) which includes at least one of the following: channel state information (CSI), delivery acknowledgment (e.g., Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), ACK / NACK, etc.), and scheduling request (SR). PRACH may transmit a random access preamble for establishing a connection with the cell.

[0188] In this disclosure, downlinks, uplinks, etc., may be expressed without the prefix "link." Also, the prefix "physical" may be omitted when describing various channels.

[0189] 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 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.

[0190] 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 SS (PSS, SSS) and PBCH (and DMRS for PBCH) may be called an SS / PBCH block, SS Block (SSB), etc. SS, SSB, etc., may also be called reference signals.

[0191] Furthermore, in the wireless communication system 1, the Uplink Reference Signal (UL-RS) may transmit the Sounding Reference Signal (SRS), Demodulation Reference Signal (DMRS), etc. The DMRS may also be called the User-Specific Reference Signal (UE-specific Reference Signal).

[0192] (base station) Figure 17 shows an example of the configuration of a base station according to one embodiment. The base station 10 includes a control unit 110, a transceiver unit 120, a transceiver antenna 130, and a transmission line interface 140. Note that one or more of the control unit 110, transceiver unit 120, transceiver antenna 130, and transmission line interface 140 may be provided.

[0193] In this example, the functional blocks of the characteristic parts of this embodiment are mainly shown, and it may be assumed that the base station 10 also has other functional blocks necessary for wireless communication. Some of the processing of each part described below may be omitted.

[0194] The control unit 110 controls the entire base station 10. The control unit 110 can be composed of a controller, control circuit, etc., as described based on common understanding in the art relating to this disclosure.

[0195] The control unit 110 may control signal generation, scheduling (e.g., resource allocation, mapping), etc. The control unit 110 may also control transmission and reception, measurement, etc., using the transceiver unit 120, the transceiver antenna 130, and the transmission path interface 140. The control unit 110 may generate data to be transmitted as signals, control information, sequences, etc., and transfer them to the transceiver unit 120. The control unit 110 may also perform call processing of communication channels (setting, releasing, etc.), status management of the base station 10, management of radio resources, etc.

[0196] The transmitting / receiving 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 transmitting / receiving unit 120 can be composed of a transmitter / receiver, RF circuit, baseband circuit, filter, phase shifter, measurement circuit, transmitting / receiving circuit, etc., as described based on common understanding in the art relating to this disclosure.

[0197] The transmitting / receiving unit 120 may be configured as an integrated transmitting / receiving unit, or it may be composed of a transmitting unit and a receiving unit. The transmitting unit may consist of a transmitting processing unit 1211 and an RF unit 122. The receiving unit may consist of a receiving processing unit 1212, an RF unit 122 and a measuring unit 123.

[0198] The transmitting and receiving antenna 130 can be composed of an antenna described based on common understanding in the art relating to this disclosure, such as an array antenna.

[0199] The transmitting / receiving unit 120 may transmit the downlink channel, synchronization signal, downlink reference signal, etc. The transmitting / receiving unit 120 may also receive the uplink channel, uplink reference signal, etc.

[0200] The transmitting / receiving unit 120 may form at least one of the transmitting beam and the receiving beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.

[0201] The transmitting / receiving unit 120 (transmission processing unit 1211) may perform processing on data and control information acquired from the control unit 110, for example, at the Packet Data Convergence Protocol (PDCP) layer, the Radio Link Control (RLC) layer (e.g., RLC retransmission control), the Medium Access Control (MAC) layer (e.g., HARQ retransmission control), etc., to generate a bit sequence to be transmitted.

[0202] The transmitting / receiving unit 120 (transmission processing unit 1211) may perform transmission processing on the bit sequence to be transmitted, 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, and output a baseband signal.

[0203] The transmitting / receiving unit 120 (RF unit 122) may perform modulation, filtering, amplification, etc., of the baseband signal to the radio frequency band and transmit the signal in the radio frequency band via the transmitting / receiving antenna 130.

[0204] On the other hand, the transmitting / receiving unit 120 (RF unit 122) may perform amplification, filtering, demodulation to a baseband signal, etc., on the radio frequency band signal received by the transmitting / receiving antenna 130.

[0205] The transmitting / receiving unit 120 (receiving processing unit 1212) may apply reception processing to the acquired baseband signal, such as analog-to-digital conversion, Fast Fourier Transform (FFT) processing, Inverse Discrete Fourier Transform (IDFT) processing (if necessary), filtering, demapping, demodulation, decoding (may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing, to acquire user data, etc.

[0206] The transmitting / receiving unit 120 (measurement unit 123) may perform measurements related to 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 also measure received power (e.g., Reference Signal Received Power (RSRP)), reception 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.

[0207] The transmission path interface 140 may send and receive signals (backhaul signaling) with 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.

[0208] In this disclosure, the transmitting and receiving units of the base station 10 may consist of at least one of a transmitting / receiving unit 120, a transmitting / receiving antenna 130, and a transmission path interface 140.

[0209] The transmitting / receiving unit 120 may also transmit reference information (e.g., CSI-RS). The transmitting / receiving unit 120 may receive information regarding the CSI using the output of the AI / ML model when the input to the Artificial Intelligence (AI) / Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback corresponding to the reference signal is determined at the terminal and the input is applied to the AI / ML model.

[0210] (User terminal) Figure 18 shows an example of the configuration of a user terminal according to one embodiment. The user terminal 20 includes a control unit 210, a transmitting / receiving unit 220, and a transmitting / receiving antenna 230. Note that one or more of the control unit 210, the transmitting / receiving unit 220, and the transmitting / receiving antenna 230 may be provided.

[0211] In this example, the functional blocks of the characteristic parts of this embodiment are mainly shown, and it may be assumed that the user terminal 20 also has other functional blocks necessary for wireless communication. Some of the processing of each part described below may be omitted.

[0212] The control unit 210 controls the entire user terminal 20. The control unit 210 can be composed of a controller, control circuit, etc., as described based on common understanding in the technical field related to this disclosure.

[0213] The control unit 210 may control signal generation, mapping, etc. The control unit 210 may also control transmission and reception, measurement, etc., using the transmitting / receiving unit 220 and the transmitting / receiving antenna 230. The control unit 210 may generate data to be transmitted as signals, control information, sequences, etc., and transfer them to the transmitting / receiving unit 220.

[0214] The transmitting / receiving 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 transmitting / receiving unit 220 can be composed of a transmitter / receiver, RF circuit, baseband circuit, filter, phase shifter, measurement circuit, transmitting / receiving circuit, etc., as described based on common understanding in the art relating to this disclosure.

[0215] The transmitting / receiving unit 220 may be configured as an integrated transmitting / receiving unit, or it may be composed of a transmitting unit and a receiving unit. The transmitting unit may consist of a transmitting processing unit 2211 and an RF unit 222. The receiving unit may consist of a receiving processing unit 2212, an RF unit 222 and a measuring unit 223.

[0216] The transmitting and receiving antenna 230 can be composed of an antenna described based on common understanding in the art relating to this disclosure, such as an array antenna.

[0217] The transmitting / receiving unit 220 may receive the downlink channel, synchronization signal, downlink reference signal, etc. The transmitting / receiving unit 220 may also transmit the uplink channel, uplink reference signal, etc.

[0218] The transmitting / receiving unit 220 may form at least one of the transmitting beam and the receiving beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.

[0219] The transmitting / receiving unit 220 (transmission processing unit 2211) may perform PDCP layer processing, RLC layer processing (e.g., RLC retransmission control), MAC layer processing (e.g., HARQ retransmission control), etc., on data and control information acquired from the control unit 210, etc., to generate a bit sequence to be transmitted.

[0220] The transmitting / receiving unit 220 (transmission processing unit 2211) may perform transmission processing on the bit sequence to be transmitted, 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, and output a baseband signal.

[0221] Whether or not to apply DFT processing may be based on the transform precoding settings. The transmitting / receiving unit 220 (transmission processing unit 2211) may perform DFT processing as part of the transmission process to transmit a channel (for example, PUSCH) using a DFT-s-OFDM waveform if transform precoding is enabled for that channel, or it may not perform DFT processing as part of the transmission process if transform precoding is not enabled for that channel.

[0222] The transmitting / receiving unit 220 (RF unit 222) may perform modulation, filtering, amplification, etc., of the baseband signal to the radio frequency band and transmit the signal in the radio frequency band via the transmitting / receiving antenna 230.

[0223] On the other hand, the transmitting / receiving unit 220 (RF unit 222) may perform amplification, filtering, demodulation to a baseband signal, etc., on the radio frequency band signal received by the transmitting / receiving antenna 230.

[0224] The transmitting / receiving unit 220 (receiving processing unit 2212) may apply reception processing such as analog-to-digital conversion, FFT processing, IDFT processing (if necessary), filtering, demapping, demodulation, decoding (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.

[0225] The transmitting / receiving unit 220 (measuring unit 223) may perform measurements related to the received signal. For example, the measuring unit 223 may perform RRM measurement, CSI measurement, etc., based on the received signal. The measuring unit 223 may also 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.

[0226] In this disclosure, the transmitting and receiving units of the user terminal 20 may consist of at least one of a transmitting / receiving unit 220 and a transmitting / receiving antenna 230.

[0227] The control unit 210 may determine the input to the Artificial Intelligence (AI) / Machine Learning (ML) model for Channel State Information Reference Signal (CSI) feedback and apply the input to the AI / ML model. The transmitting / receiving unit 220 may transmit information about the CSI using the output of the AI / ML model.

[0228] The control unit 210 may select a channel matrix that has undergone at least one of preprocessing and quantization as the input. The transmitting / receiving unit 220 may transmit Rank Indicator (RI) information corresponding to the channel matrix.

[0229] If the control unit 210 selects a channel matrix that has undergone at least one of preprocessing and quantization as the input, it may determine the AI / ML model based on the Rank Indicator (RI).

[0230] If the control unit 210 selects a precoding matrix or eigenvector that has undergone at least one of preprocessing and quantization as the input, it may decide to use a mode that uses the AI / ML model per layer or a mode that uses the AI / ML model per rank.

[0231] (Hardware Configuration) Note that the block diagrams used in the description of the above embodiments show blocks of functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Also, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one physically or logically combined device, or two or more physically or logically separated devices may be directly or indirectly connected (for example, using wired, wireless, etc.), and realized using these multiple devices. The functional block may be realized by combining software with the above one device or the above multiple devices.

[0232] Here, functions include, but are not limited to, judgment, decision, determination, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, solution, selection, selection, establishment, comparison, assumption, expectation, regarded as, notification (broadcasting), notification (notifying), communication (communicating), forwarding (forwarding), configuration (configuring), reconfiguration (reconfiguring), allocation (allocating, mapping), assignment (assigning), etc. For example, a functional block (component) that functions as transmission may be referred to as a transmission unit, a transmitter, etc. In any case, as described above, the realization method is not particularly limited.

[0233] For example, a base station, a user terminal, etc. in an embodiment of the present disclosure may function as a computer that performs the processing of the wireless communication method of the present disclosure. FIG. 19 is a diagram showing an example of the hardware configuration of a base station and a user terminal according to an embodiment. The above base station 10 and user terminal 20 may physically be 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.

[0234] In the present disclosure, terms such as apparatus, circuit, device, section, unit, etc. can be read interchangeably with each other. The hardware configurations of the base station 10 and the user terminal 20 may be configured to include one or more of each device shown in the figure, or may be configured without including some devices.

[0235] For example, although only one processor 1001 is illustrated, there may be a plurality of processors. Also, the processing may be executed by one processor, or the processing may be executed by two or more processors simultaneously, sequentially, or using other methods. Note that the processor 1001 may be implemented by one or more chips.

[0236] Each function in the base station 10 and the user terminal 20 is realized, for example, by causing a predetermined software (program) to be loaded onto hardware such as the processor 1001 and the memory 1002, so that the processor 1001 performs calculations and controls communication via the communication device 1004, or controls at least one of reading and writing data in the memory 1002 and the storage 1003.

[0237] The processor 1001 controls the entire computer by operating, for example, an operating system. The processor 1001 may be constituted by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic device, a register, etc. For example, at least a part of the above-described control unit 110 (210), transmission / reception unit 120 (220), etc. may be realized by the processor 1001.

[0238] Furthermore, the processor 1001 reads programs (program code), 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 accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. For example, the control unit 110 (210) may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and other functional blocks may be implemented similarly.

[0239] Memory 1002 is a computer-readable recording medium and may consist of at least one of the following: Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically EPROM (EEPROM), Random Access Memory (RAM), or other suitable storage medium. Memory 1002 may also be called a register, cache, or main memory. Memory 1002 can store executable programs (program code), software modules, etc., for carrying out a wireless communication method according to one embodiment of this disclosure.

[0240] Storage 1003 is a computer-readable recording medium and may consist of at least one of the following: a flexible disk, a floppy disk, a magneto-optical disk (e.g., a compact disk (Compact Disc ROM (CD-ROM)), a digital multipurpose disk, a Blu-ray disk), a removable disk, a hard disk drive, a smart card, a flash memory device (e.g., a card, stick, key drive), a magnetic stripe, a database, a server, or other suitable storage medium. Storage 1003 may also be called an auxiliary storage device.

[0241] 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 referred to as a network device, network controller, network card, communication module, etc. The communication device 1004 may be configured to include, for example, a high-frequency switch, duplexer, filter, frequency synthesizer, etc., in order to implement at least one of 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 implemented by the communication device 1004. The transmitting / receiving unit 120 (220) may be implemented with physically or logically separated implementations of a transmitting unit 120a (220a) and a receiving unit 120b (220b).

[0242] The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, light-emitting diode (LED) lamp, etc.). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel).

[0243] 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 different buses may be configured for each device.

[0244] 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), and a field programmable gate array (FPGA), and some or all of each functional block may be implemented using such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0245] (modified version) In addition, terms used in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, channel, symbol, and signal (signal or signaling) may be used interchangeably. Also, a signal may be a message. A reference signal may be abbreviated as RS and may be called a pilot, pilot signal, etc., depending on the applicable standard. Also, a component carrier (CC) may be called a cell, frequency carrier, carrier frequency, etc.

[0246] A wireless frame may consist of one or more periods (frames) in the time domain. Each of these periods (frames) constituting a wireless frame may be called a subframe. Furthermore, a subframe may consist 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.

[0247] Here, the neuralelogy may be communication parameters applied to at least one of the transmission and reception of a signal or channel. The neuralelogy may be, 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, specific filtering processes performed by the transceiver in the frequency domain, or specific windowing processes performed by the transceiver in the time domain.

[0248] A slot may consist of one or more symbols in the time domain (such as Orthogonal Frequency Division Multiplexing (OFDM) symbols or Single Carrier Frequency Division Multiple Access (SC-FDMA) symbols). Alternatively, a slot may be a time unit based on neurology.

[0249] A slot may include multiple mini-slots. Each mini-slot may consist of one or more symbols in the time domain. Mini-slots may also be called sub-slots. Mini-slots may consist of fewer symbols than a slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a mini-slot may be called a PDSCH (PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using a mini-slot may be called a PDSCH (PUSCH) mapping type B.

[0250] Wireless frames, subframes, slots, minislots, and symbols all represent units of time when transmitting a signal. Wireless frames, subframes, slots, minislots, and symbols may each be referred to by different names. Furthermore, the units of time such as frames, subframes, slots, minislots, and symbols in this disclosure may be interpreted as interchangeable.

[0251] For example, one subframe may be called TTI, multiple consecutive subframes may be called TTI, or one slot or one mini-slot may be called TTI. In other words, at least one of the subframe and TTI may be a subframe (1ms) in existing LTE, a period shorter than 1ms (e.g., 1-13 symbols), or a period longer than 1ms. Note that the unit representing TTI may be called a slot, mini-slot, etc., instead of a subframe.

[0252] Here, TTI refers to, for example, the smallest unit of time for scheduling in wireless communication. For example, in an LTE system, the base station schedules each user terminal to allocate wireless resources (such as the frequency bandwidth and transmission power available to each user terminal) in TTI units. However, the definition of TTI is not limited to this.

[0253] TTI may be a transmission time unit for channel-encoded data packets (transport blocks), code blocks, code words, etc., or it may be a processing unit for scheduling, link adaptation, etc. Given a TTI, the actual time interval (e.g., number of symbols) to which the transport block, code block, code word, etc. are mapped may be shorter than the given TTI.

[0254] In addition, when one slot or one mini-slot is called a TTI, one or more TTIs (i.e., one or more slots or one or more mini-slots) may be the minimum time unit for scheduling. Also, the number of slots (number of mini-slots) constituting the minimum time unit for the scheduling may be controlled.

[0255] A TTI having a time length of 1 ms may be referred to as a normal TTI (TTI in 3GPP Rel.8-12), a normal TTI, a long TTI, a normal subframe, a normal subframe, a long subframe, a slot, etc. A TTI shorter than a normal TTI may be referred to as a shortened TTI, a short TTI, a partial TTI (partial or fractional TTI), a shortened subframe, a short subframe, a mini-slot, a sub-slot, a slot, etc.

[0256] Note that a long TTI (e.g., a normal TTI, a subframe, etc.) may be read as a TTI having a time length exceeding 1 ms, and a short TTI (e.g., a shortened TTI, etc.) may be read as a TTI having a TTI length less than that of a long TTI and not less than 1 ms.

[0257] A resource block (Resource Block (RB)) is a resource allocation unit in the time domain and the frequency domain, and in the frequency domain, it may include one or a plurality of consecutive subcarriers (subcarriers). The number of subcarriers included in an RB may be the same regardless of the numerology, and may be, for example, 12. The number of subcarriers included in an RB may be determined based on the numerology.

[0258] Also, an RB may include one or a plurality of symbols in the time domain, and may have a length of one slot, one mini-slot, one subframe, or one TTI. One TTI, one subframe, etc. may each be constituted by one or a plurality of resource blocks.

[0259] One or more RBs may also be called Physical RBs (PRBs), Sub-Carrier Groups (SCGs), Resource Element Groups (REGs), PRB pairs, RB pairs, etc.

[0260] Furthermore, a resource block may consist of one or more resource elements (REs). For example, one RE may be a radio resource area comprising one subcarrier and one symbol.

[0261] A Bandwidth Part (BWP) (also called a partial bandwidth) may represent a subset of consecutive common resource blocks (RBs) for a given neurology in a given carrier. Here, the common RBs may be identified by an index of the RBs relative to the carrier's common reference point. PRBs may be defined and numbered within a BWP.

[0262] A BWP may include UL BWPs (BWPs for UL) and DL BWPs (BWPs for DL). One or more BWPs may be configured within a single carrier for a UE.

[0263] At least one of the configured BWPs may be active, and the UE does not need to assume that it will send or receive a given signal / channel outside of the active BWP. In this disclosure, terms such as "cell" and "carrier" may be read as "BWP".

[0264] The structures described above, such as wireless frames, subframes, slots, minislots, and symbols, are merely illustrative examples. For instance, the number of subframes included in a wireless frame, the number of slots per subframe or wireless frame, the number of minislots within a slot, the number of symbols and RBs included in a slot or minislot, the number of subcarriers included in an RB, and the number of symbols, symbol length, and cyclic prefix (CP) length within a TTI can be varied in various ways.

[0265] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a predetermined value, or corresponding other information. For example, wireless resources may be indicated by a predetermined index.

[0266] The names used for parameters and other elements in this disclosure are not restrictive in any way. Furthermore, mathematical formulas and other elements that use these parameters may differ from those expressly disclosed in this disclosure. Various channels (PUCCH, PDCCH, etc.) and information elements can be identified by any suitable name, and therefore, the various names assigned to these various channels and information elements are not restrictive in any way.

[0267] The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. 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 voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0268] Furthermore, information, signals, etc., can be output from upper layers to lower layers and from lower layers to upper layers, or to at least one of the two. Information, signals, etc., may also be input and output via multiple network nodes.

[0269] Input and output information and signals may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information and signals may be overwritten, updated, or appended to. Output information and signals may be deleted. Input information and signals may be transmitted to other devices.

[0270] Information notification is not limited to the embodiments described herein and may be carried out by other means. For example, information notification in this disclosure may be carried out 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)), Medium Access Control (MAC) signaling), other signals, or a combination thereof).

[0271] Physical layer signaling may also be called Layer 1 / Layer 2 (L1 / L2) control information (L1 / L2 control signals), L1 control information (L1 control signals), etc. RRC signaling may also be called RRC messages, for example, RRC Connection Setup messages, RRC Connection Reconfiguration messages, etc. MAC signaling may also be communicated using, for example, MAC Control Element (CE).

[0272] Furthermore, notification of the specified information (for example, notification that "X is the case") is not limited to explicit notification, but may also be made implicitly (for example, by not notifying the specified information or by notifying other information).

[0273] The determination may be made by a value represented by 1 bit (0 or 1), by a boolean value represented as true or false, or by a numerical comparison (for example, a comparison with a predetermined value).

[0274] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name.

[0275] Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technology (such as coaxial cable, fiber optic cable, twisted pair, or Digital Subscriber Line (DSL)) and wireless technology (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0276] The terms “system” and “network” as used in this disclosure may be used interchangeably. “Network” may also mean the equipment included in the network (e.g., base stations).

[0277] 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," "antenna port group," "layer," "number of layers," "rank," "resource," "resource set," "resource group," "beam," "beam width," "beam angle," "antenna," "antenna element," and "panel" may be used interchangeably.

[0278] In this disclosure, terms such as "Base Station (BS)", "wireless 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", and "component carrier" may be used interchangeably. Base stations may also be referred to by terms such as macrocell, small cell, femtocell, and picocell.

[0279] A base station can house one or more (e.g., three) cells. If a base station houses multiple cells, the entire coverage area of ​​the base station can be divided into several smaller areas, each of which may also be provided with communication services by a base station subsystem (e.g., a small indoor base station (Remote Radio Head (RRH))). The terms “cell” or “sector” refer to part or all of the coverage area of ​​at least one of the base station and / or base station subsystems that provide communication services in that coverage.

[0280] In this disclosure, the transmission of information by a base station to a terminal may be interpreted as the base station instructing the terminal to perform a control / operation based on said information.

[0281] In this disclosure, terms such as "Mobile Station (MS)," "user terminal," "User Equipment (UE)," and "terminal" may be used interchangeably.

[0282] A mobile station may also be called 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 appropriate term.

[0283] At least one of the base station and the mobile station may be called a transmitting device, a receiving device, a wireless communication device, etc. At least one of the base station and the mobile station may also be a device mounted on a moving object, the moving object itself, etc.

[0284] The term "mobile object" refers to any movable object, regardless of its speed, and naturally includes cases where the mobile object is stationary. Examples of such mobile objects include, but are 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, satellites, drones, multicopters, quadcopters, balloons, and items carried on them. Furthermore, such mobile objects may be autonomously driven objects operating based on operational commands.

[0285] The mobile entity may be a vehicle (e.g., a car, an airplane), an unmanned mobile entity (e.g., a drone, an autonomous vehicle), or a robot (manned or unmanned). At least one of the base station and the mobile station may be a device that does 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.

[0286] Figure 20 shows an example of a vehicle according to one 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, a pneumatic 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.

[0287] The drive unit 41 consists of, for example, at least one of an engine, a motor, or an engine-motor hybrid. The steering unit 42 includes at least a steering wheel (also called a handle) and is configured to steer at least one of the front wheels 46 and the rear wheels 47 based on the operation of the steering wheel operated by the user.

[0288] The electronic control unit 49 consists of a microprocessor 61, memory (ROM, RAM) 62, and communication ports (e.g., input / output (IO) ports) 63. Signals from various sensors 50-58 installed in the vehicle are input to the electronic control unit 49. The electronic control unit 49 may also be called an Electronic Control Unit (ECU).

[0289] Signals from various sensors 50-58 include current signals from current sensor 50 for sensing motor current, rotational speed signals of front wheels 46 / rear wheels 47 acquired by rotational speed sensor 51, air pressure signals of front wheels 46 / rear wheels 47 acquired by air pressure sensor 52, vehicle speed signals acquired by vehicle speed sensor 53, acceleration signals acquired by acceleration sensor 54, accelerator pedal depression signal of accelerator pedal 43 acquired by accelerator pedal sensor 55, brake pedal depression signal of brake pedal 44 acquired by brake pedal sensor 56, operation signals of shift lever 45 acquired by shift lever sensor 57, and detection signals for detecting obstacles, vehicles, pedestrians, etc., acquired by object detection sensor 58.

[0290] The information service unit 59 consists of various devices for providing (outputting) various types of information such as driving information, traffic information, and entertainment information, including a car navigation system, audio system, speakers, displays, television, and radio, and one or more ECUs that control these devices. The information service unit 59 uses information acquired from external devices via a communication module 60 or the like to provide various types of information / services (e.g., multimedia information / multimedia services) to the occupants of the vehicle 40.

[0291] The information service unit 59 may include input devices that accept input from the outside (e.g., keyboard, mouse, microphone, switch, button, sensor, touch panel, etc.) and output devices that perform output to the outside (e.g., display, speaker, LED lamp, touch panel, etc.).

[0292] The driver assistance system unit 64 consists of various devices that provide functions to prevent accidents or reduce the driver's workload, such as millimeter-wave radar, Light Detection and Ranging (LiDAR), cameras, positioning locators (e.g., Global Navigation Satellite System (GNSS)), map information (e.g., High Definition (HD) maps, Autonomous Vehicle (AV) maps), gyro systems (e.g., Inertial Measurement Unit (IMU), Inertial Navigation System (INS)), artificial intelligence (AI) chips, and AI processors, as well as one or more ECUs that control these devices. The driver assistance system unit 64 also transmits and receives various information via the communication module 60 to realize driver assistance functions or autonomous driving functions.

[0293] 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 sends and receives data (information) via the communication port 63 to 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, axle 48, the microprocessor 61 and memory (ROM, RAM) 62 in the electronic control unit 49, and various sensors 50-58 provided in the vehicle 40.

[0294] 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 external devices. For example, it can send and receive various types of information to and from external devices via wireless communication. The communication module 60 may be located either inside or outside the electronic control unit 49. The external device may be, for example, the base station 10 or the user terminal 20 described above. Alternatively, the communication module 60 may be, for example, at least one of the base station 10 and the user terminal 20 (it may function as at least one of the base station 10 and the user terminal 20).

[0295] The communication module 60 may transmit at least one of the following to an external device via wireless communication: signals from the various sensors 50-58 input to the electronic control unit 49, information obtained based on said signals, and information based on input from an external source (user) obtained via the information service unit 59. The electronic control unit 49, the various sensors 50-58, the information service unit 59, etc., may also be called input units that accept input. For example, the PUSCH transmitted by the communication module 60 may include information based on the above input.

[0296] The communication module 60 receives various information (traffic information, signal information, inter-vehicle information, etc.) transmitted from an external device and displays it on the information service unit 59 installed in the vehicle. The information service unit 59 may also be called an output unit, which outputs information (for example, it outputs information to devices such as displays and speakers based on the PDSCH (or data / information decoded from the PDSCH) received by the communication module 60).

[0297] Furthermore, the communication module 60 stores various information received from external devices in a memory 62 that can be used by the microprocessor 61. Based on the information stored in the memory 62, the microprocessor 61 may control the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axle 48, various sensors 50-58, etc., which are provided in the vehicle 40.

[0298] Furthermore, the term "base station" in this disclosure may be interpreted as "user terminal." For example, the various aspects / embodiments of this 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)). In this case, the user terminal 20 may have the functions that the base station 10 has. Also, terms such as "uplink" and "downlink" may be interpreted as terms corresponding to terminal-to-terminal communication (for example, "sidelink"). For example, uplink channel and downlink channel may be interpreted as sidelink channel.

[0299] Similarly, the term "user terminal" in this disclosure may be replaced with "base station." In this case, the base station 10 may be configured to have the same functions as the user terminal 20 described above.

[0300] In this disclosure, operations performed by a base station may, in some cases, be performed by its upper node. In a network including one or more network nodes with 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 (for example, a Mobility Management Entity (MME), a Serving Gateway (S-GW), etc., but not limited to these), or a combination thereof.

[0301] Each aspect / embodiment described in this disclosure may be used individually, in combination, or switched between during execution. Furthermore, the processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described in this disclosure may be rearranged in order, provided they are consistent. For example, the methods described in this disclosure present various step elements in an exemplary order and are not limited to that specific order.

[0302] 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 (where x is, for example, an integer or decimal)), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM®), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi®), IEEE 802.16 (WiMAX®), and IEEE This may apply to systems utilizing 802.20, Ultra-WideBand (UWB), Bluetooth®, or other appropriate wireless communication methods, as well as next-generation systems that are extended, modified, created, or defined based on these. It may also apply to combinations of multiple systems (e.g., a combination of LTE or LTE-A and 5G).

[0303] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on."

[0304] Any reference to elements using the designations “first,” “second,” etc., as 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 way to distinguish between two or more elements. Accordingly, the references to the first and second elements do not imply that only two elements may be employed or that the first element must precede the second element in any way.

[0305] The term “determining” as used in this disclosure may encompass a wide variety of actions. For example, “determining” may be considered to include judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiry (e.g., searching in tables, databases, or other data structures), ascertaining, etc.

[0306] Furthermore, "judgment (decision)" may be considered as "judging (deciding)" things like receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory).

[0307] Furthermore, "judgment (decision)" can be considered as "judging (deciding)" something like resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment (decision)" can be considered as "judging (deciding)" something about an action.

[0308] Furthermore, "judgment (decision)" can be replaced with "assuming," "expecting," or "considering."

[0309] The term "maximum transmit power" as used in this disclosure may mean the maximum transmit power, the nominal UE maximum transmit power, or the rated UE maximum transmit power.

[0310] As used in this disclosure, the terms “connected,” “coupled,” and any variations thereof mean any direct or indirect connection or coupling between two or more elements, and may include one or more intermediate elements between two elements that are “connected” or “coupled” with each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, “connection” may be replaced with “access.”

[0311] In this disclosure, when two elements are connected, they can be considered to be “connected” or “coupled” to each other using one or more wires, cables, printed electrical connections, etc., and, in some non-exclusive and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain, or optical domain (both visible and invisible).

[0312] 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 "combine" may be interpreted similarly to "different."

[0313] Where the terms “include,” “including,” and variations thereof are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to mean exclusive OR.

[0314] In this disclosure, if articles are added by translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0315] In this disclosure, terms such as "less than or equal to," "less than," "greater than or equal to," "more than," and "equal to" may be interpreted interchangeably. In addition, in this disclosure, terms meaning "good," "bad," "big," "small," "high," "low," "early," "slow," "wide," and "narrow" may be interpreted interchangeably, not limited to the positive, comparative, and superlative degrees. Furthermore, in this disclosure, terms meaning "good," "bad," "big," "small," "high," "low," "early," "slow," "wide," and "narrow" may be interpreted interchangeably, not limited to the positive, comparative, and superlative degrees, by adding "i-th" (where i is any integer) to the expression (for example, "highest" may be interpreted interchangeably with "i-th highest").

[0316] In this disclosure, "of," "for," "regarding," "related to," and "associated with" may be interpreted as being interchangeable.

[0317] Although the invention described herein has been explained in detail above, it will be clear to those skilled in the art that the invention described herein is not limited to the embodiments described herein. The invention described herein can be implemented in modified and altered forms without departing from the spirit and scope of the invention as defined in the claims. Therefore, the descriptions herein are for illustrative purposes only and do not imply any limitation on the invention described herein.

Claims

1. A control unit that determines the input to an Artificial Intelligence (AI) / Machine Learning (ML) model for Channel State Information (CSI) feedback and applies said input to the AI / ML model, A transmission unit that transmits information about CSI using the output of the AI / ML model, It has, The control unit, when it selects a precoding matrix or eigenvector that has undergone at least one of preprocessing and quantization as the input, determines that the larger the Rank Indicator (RI), the larger the payload size of the output of the AI / ML model.

2. When the control unit selects a precoding matrix or eigenvector that has undergone at least one of preprocessing and quantization as the input, it decides to use either a mode that uses the AI / ML model per layer or a mode that uses the AI / ML model per rank. The terminal according to claim 1.

3. The process involves determining the input to an Artificial Intelligence (AI) / Machine Learning (ML) model for Channel State Information (CSI) feedback, and applying said input to the AI / ML model. A step of transmitting information about CSI using the output of the AI / ML model, If a precoding matrix or eigenvector that has undergone at least one of preprocessing and quantization is selected as the input, the larger the Rank Indicator (RI), the larger the payload size of the output of the AI / ML model is determined. A wireless communication method for a terminal having [a certain feature].

4. A transmitting unit that transmits a reference signal, When the input to the Artificial Intelligence (AI) / Machine Learning (ML) model for Channel State Information (CSI) feedback corresponding to the aforementioned reference signal is determined at the terminal, and when said input is applied to the AI / ML model, a receiving unit receives information about the CSI using the output of the AI / ML model. It has, When a precoding matrix or eigenvector that has undergone at least one of preprocessing and quantization is selected as the input, the larger the Rank Indicator (RI), the larger the payload size of the output of the AI / ML model is determined to be for the base station.

5. A system having a base station and a terminal, The base station has a transmitting unit that transmits a reference signal, The aforementioned terminal is A control unit that determines the input to an Artificial Intelligence (AI) / Machine Learning (ML) model for Channel State Information (CSI) feedback corresponding to the aforementioned reference signal, and applies the said input to the AI / ML model, A transmission unit that transmits information about CSI using the output of the AI / ML model, It has, The control unit, when it selects a precoding matrix or eigenvector that has undergone at least one of preprocessing and quantization as the input, determines that the larger the Rank Indicator (RI), the larger the payload size of the output of the AI / ML model.

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