Terminal, wireless communication method, and base station

Abstract

A terminal according to an aspect of the present disclosure includes: a reception unit that receives information pertaining to an ID of a model or functionality; and a control unit that controls the pairing of models on both sides on the basis of the information. The control unit uses the information as a basis for checking whether a model available on the terminal side is a model available on a base station side.

Description

Terminal, wireless communication method and base station

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

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

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

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

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

[0006] As a use case of utilizing an AI model, Channel State Information (CSI) compression using a two-sided AI model is being considered. Such a CSI compression method may be called AI-based CSI feedback and may be realized using, for example, an autoencoder.

[0007] In the use cases of utilizing the AI ​​models described above (e.g., CSI compression or CSI feedback using a two-sided AI model), model pairing for the two-sided models has been considered. However, specific details regarding the pairing have not yet been explored.

[0008] Unless these are properly defined, it may not be possible to achieve appropriate overhead reduction, highly accurate channel estimation, or highly efficient resource utilization, which may hinder improvements in communication throughput and communication quality.

[0009] Therefore, one of the objects of the present disclosure is to provide a terminal, a wireless communication method, and a base station that can achieve suitable overhead reduction / channel estimation / resource utilization.

[0010] A terminal according to one aspect of the present disclosure has a receiving unit that receives information regarding a model or functionality ID, and a control unit that controls pairing for two-sided models based on the information, and the control unit checks, based on the information, whether a model available on the terminal side is a model available on the base station side.

[0011] According to one aspect of the present disclosure, it is possible to achieve favorable overhead reduction / channel estimation / resource utilization.

[0012] FIG. 1 is a diagram illustrating an example of a framework for managing AI models. FIG. 2 is a diagram illustrating an example of specifying an AI model. FIG. 3 is a diagram illustrating an example of CSI feedback using an encoder / decoder. FIG. 4 is a diagram illustrating an example of AI-based CSI feedback. FIG. 5 is a diagram illustrating an example of quantization-aware training (QAT) and quantization-unaware training (QNT). FIG. 6A is a conceptual diagram illustrating an example of Type 2 training, FIG. 6B is a conceptual diagram illustrating an example of UE-first Type 3 training, and FIG. 6C is a conceptual diagram illustrating an example of NW-first Type 3 training. FIG. 7 is a diagram illustrating an example of CSI feedback using an encoder / decoder incorporating quantization. FIG. 8 is a diagram illustrating an example of AI model-related information. FIGS. 9A and 9B are diagrams illustrating an example of a model ID-based pairing procedure according to the 0th embodiment. FIGS. 10A and 10B are diagrams illustrating an example of a non-model ID-based pairing procedure with a dataset according to the 0th embodiment. FIGS. 11A and 11B are diagrams illustrating an example of a non-model ID-based pairing procedure with additional conditions according to the 0th embodiment. 12A to 12C are diagrams illustrating an example of scheme 1-0 of a pairing procedure according to the 0th embodiment. FIGS. 13A and 13B are diagrams illustrating an example of scheme 1-1 of a pairing procedure according to the 0th embodiment. FIGS. 14A and 14B are diagrams illustrating an example of scheme 1-2 of a pairing procedure according to the 0th embodiment. FIG. 15 is a diagram illustrating an example of a pairing procedure initiated by a NW according to the 0th embodiment. FIG. 16 is a diagram illustrating an example of a pairing procedure initiated by a UE according to the 0th embodiment. FIGS. 17A and 17B are diagrams illustrating an example of model and quantization pairing according to the 0th embodiment. FIG. 18 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment. FIG. 19 is a diagram illustrating an example of a configuration of a base station according to an embodiment. FIG. 20 is a diagram illustrating an example of a configuration of a user terminal according to an embodiment. FIG. 21 is a diagram illustrating an example of hardware configurations of a base station and a user terminal according to an embodiment. FIG. 22 is a diagram illustrating an example of a vehicle according to an embodiment.

[0013] (Application of Artificial Intelligence (AI) Technology to Wireless Communications) With regard to future wireless communications technologies, the use of AI technology such as machine learning (ML) for network / device control and management is being considered.

[0014] For example, it is being considered that terminals (user terminals, user equipment (UE)) / base stations (BSs) will utilize AI technology to improve Channel State Information (CSI) feedback (e.g., reduced overhead, improved accuracy, prediction), improve beam management (e.g., improved accuracy, prediction in the time / space domain), and improve position measurement (e.g., improved position estimation / prediction).

[0015] Based on the input information, the AI ​​model may output at least one information such as an estimate, a prediction, a selected action, a classification, etc. The UE / BS may input channel state information, reference signal measurements, etc. to the AI ​​model and output highly accurate channel state information / measurements / beam selection / location, future channel state information / radio link quality, etc.

[0016] In the present disclosure, AI may be interpreted as an object (also called a subject, object, data, function, program, etc.) that has (performs) at least one of the following characteristics: - Estimation based on observed or collected information; - Selection based on observed or collected information; - Prediction based on observed or collected information.

[0017] In the present disclosure, estimation, prediction, and inference may be used interchangeably. Also, in the present disclosure, estimate, predict, and infer may be used interchangeably.

[0018] In the present disclosure, an object may be, for example, an apparatus, device, etc., such as a UE or a BS. Also, in the present disclosure, an object may correspond to a program / model / entity that operates in the apparatus.

[0019] Also, in the present disclosure, an AI model may be interpreted as an object that has (performs) at least one of the following characteristics: - Generates an estimate by feeding information; - Predicts an estimate by feeding information; - Discovers features by feeding information; - Selects an action by feeding information.

[0020] Additionally, in this disclosure, an AI model may refer to a data-driven algorithm that applies AI techniques to generate a set of outputs based on a set of inputs.

[0021] In addition, in the present disclosure, the terms AI model, model, ML model, predictive analytics, predictive analysis model, tool, autoencoder, encoder, decoder, neural network model, AI algorithm, scheme, etc. may be interchangeable. The AI ​​model may be derived using at least one of regression analysis (e.g., linear regression analysis, multiple regression analysis, logistic regression analysis), support vector machine, random forest, neural network, deep learning, etc.

[0022] In this disclosure, the term "autoencoder" may be interchangeably referred to as any autoencoder, such as a stacked autoencoder, a convolutional autoencoder, etc. The encoder / decoder of this disclosure may employ a model such as a Residual Network (ResNet), a DenseNet, or a RefineNet.

[0023] Furthermore, in the present disclosure, the terms encoder, encoding, encode / encoded, modification / alteration / control by an encoder, compressing, compress / compressed, generating, generate / generated, etc. may be read interchangeably.

[0024] In addition, in the present disclosure, decoder, decoding, decode / decoded, modification / alteration / control by decoder, decompressing, decompress / decompressed, reconstructing, reconstruct / reconstructed, etc. may be read interchangeably.

[0025] In the present disclosure, a layer (of an AI model) may be interchangeably read as a layer (such as an input layer or an intermediate layer) used in the AI ​​model. The layer in the present disclosure may correspond to at least one of an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolutional layer, an activation layer, a dense layer, a normalization layer, a pooling layer, an attention layer, a dropout layer (which may include an ND layer described later), a fully connected layer, etc.

[0026] In this disclosure, methods for training an AI model may include supervised learning, unsupervised learning, reinforcement learning, federated learning, etc. Supervised learning may refer to the process of training a model from inputs and corresponding labels. Unsupervised learning may refer to the process of training a model without labeled data. Reinforcement learning may refer to the process of training a model from inputs (i.e., states) and feedback signals (i.e., rewards) resulting from the model's outputs (i.e., actions) in an environment with which the model interacts.

[0027] In the present disclosure, terms such as generate, calculate, derive, etc. may be interchangeable. In the present disclosure, terms such as implement, operate, operate, execute, etc. may be interchangeable. In the present disclosure, terms such as train, learn, update, retrain, etc. may be interchangeable. In the present disclosure, terms such as infer, after-training, live use, actual use, etc. may be interchangeable. In the present disclosure, signal may be interchangeable with signal / channel.

[0028] 1 is a diagram illustrating an example of a framework for managing an AI model. In this example, each stage related to an AI model is shown as a block. This example is also referred to as lifecycle management of an AI model.

[0029] The data collection stage corresponds to a stage of collecting data for generating / updating an AI model. The data collection stage may include data organization (e.g., determining which data to transfer for model training / model inference), data transfer (e.g., transferring data to an entity (e.g., UE, gNB) that performs model training / model inference), etc.

[0030] Note that data collection may refer to a process in which data is collected by a network node, a management entity, or a UE for the purpose of AI model training / data analysis / inference. In the present disclosure, the terms "process" and "procedure" may be interpreted interchangeably.

[0031] In the model training stage, model training is performed based on the data (training data) transferred from the collection stage. This stage may include data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model training / validation, model testing (e.g., verifying whether the trained model meets a performance threshold), model exchange (e.g., transferring the model for distributed learning), and model deployment / update (deploying / updating the model to the entity that will perform model inference).

[0032] It should be noted that AI model training may refer to a process for training an AI model in a data-driven manner and obtaining a trained AI model for inference.

[0033] AI model validation may also refer to a sub-process of training that evaluates the quality of an AI model using a dataset different from the dataset used to train the model, which helps select model parameters that generalize beyond the dataset used to train the model.

[0034] AI model testing may also refer to a sub-process of training for evaluating the performance of the final AI model using a dataset different from that used for model training / validation. Note that, unlike validation, testing does not necessarily require subsequent model tuning.

[0035] In the model inference stage, model inference is performed based on the data (inference data) transferred from the collection stage. This stage may include data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model inference, model monitoring (e.g., monitoring the performance of model inference), model performance feedback (feeding back model performance to the entity training the model), and output (providing model output to the actor).

[0036] Additionally, AI model inference may refer to the process of using a trained AI model to produce a set of outputs from a set of inputs.

[0037] Also, a UE side model may refer to an AI model whose inference is performed entirely in the UE, and a network side model may refer to an AI model whose inference is performed entirely in the network (e.g., gNB).

[0038] Also, a one-sided model may refer to a UE-side model or a network-side model. A two-sided model may refer to a pair of AI models in which joint inference is performed. Here, joint inference may include AI inference in which the inference is performed jointly across the UE and the network, e.g., a first part of the inference may be performed first by the UE and the remaining part by the gNB (or vice versa).

[0039] In addition, AI model monitoring may refer to a process for monitoring the inference performance of an AI model, and may be interchangeably read as model performance monitoring, performance monitoring, etc.

[0040] Note that model registration may refer to assigning a version identifier to a model and making the model executable by compiling it into the specific hardware used in the inference stage, and model deployment may refer to distributing (or activating) a fully developed and tested model runtime image (or execution environment image) to (or enabling) a target (e.g., UE / gNB) where inference will be performed.

[0041] An actor stage may include action triggers (e.g., deciding whether to trigger an action on another entity), feedback (e.g., feeding back information needed for training data / inference data / performance feedback), etc.

[0042] For example, training of a model for mobility optimization may be performed in, for example, Operation, Administration and Maintenance (Management) (OAM) / gNodeB (gNB) in a network (NW). In the former case, interoperability, large-capacity storage, operator manageability, and model flexibility (feature engineering, etc.) are advantageous. In the latter case, the latency of model updates and the need for data exchange for model deployment are advantageous. Inference of the above model may be performed in, for example, a gNB.

[0043] The entity that performs training / inference may vary depending on the use case (i.e., the function of the AI ​​model), which may include beam management, beam prediction, autoencoder (or information compression), CSI feedback, positioning, etc.

[0044] For example, for AI-assisted beam management based on measurement reports, the OAM / gNB may perform model training and the gNB may perform model inference.

[0045] For AI-assisted UE-assisted positioning, a Location Management Function (LMF) may perform model training and the LMF may perform model inference.

[0046] 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).

[0047] 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.

[0048] Note that model activation may mean activating an AI model for a specific function, model deactivation may mean disabling an AI model for a specific function, and model switching may mean deactivating a currently active AI model for a specific function and activating a different AI model.

[0049] Model transfer may also refer to distributing an AI model over the air interface. This distribution may include distributing parameters of a model structure already known at the receiving end, or a new model with parameters, or both. This distribution may include a complete model or a partial model. Model download may refer to transferring a model from the network to the UE. Model upload may refer to transferring a model from the UE to the network.

[0050] 2 is a diagram showing an example of specifying an AI model. In this example, a UE and a NW (e.g., a base station (BS)) can recognize models #1 and #2 (although they do not need to fully understand the details of the models). The UE may report, for example, the capabilities of model #1 and model #2 to the NW, and the NW may instruct the UE on the AI ​​model to use.

[0051] (AI-Based CSI Feedback) As a use case of utilizing an AI model, CSI compression using a two-sided AI model is being considered. Such a CSI compression method may be called AI-based CSI feedback and may be realized using, for example, an autoencoder.

[0052] 3 is a diagram illustrating an example of CSI feedback using an encoder / decoder. A UE inputs input information (e.g., CSI) to an encoder and transmits information (CSI feedback information) including encoded bits from an antenna. A BS inputs the received CSI feedback information bits to a corresponding decoder to obtain reconstructed input information (e.g., CSI) to be output.

[0053] The input CSI may include, for example, information on channel coefficients (elements of a channel matrix) or information on precoding coefficients (elements of a precoding matrix). In other words, the CSI may correspond to information on the channel state in the space-frequency domain. Note that the input may include information other than CSI.

[0054] The CSI output from the decoder may be reconstructed CSI corresponding to the input to the encoder, or may be CSI different from the input to the encoder (e.g., information on precoding coefficients if the input information is information on channel coefficients).

[0055] The encoder / decoder may also include pre-processing for the input and post-processing for the output.

[0056] The encoded bits are more compressed than the input information before encoding, and this is expected to reduce the communication overhead required for CSI feedback.

[0057] 4 is a diagram showing an example of AI-based CSI feedback. The UE performs pre-processing, AI / ML-based CSI generation, and post-processing on CSI measurement results, etc., and transmits encoded bits (CSI feedback information) to the NW (base station). The NW (base station) performs pre-processing, AI / ML-based CSI reconstruction, and post-processing on the received bits to obtain CSI (channel / precoding matrix).

[0058] (Quantization / Dequantization) In order to evaluate the use case of utilizing the AI ​​model described above (CSI compression using a two-sided AI model), it is being considered to report on the quantization method. As will be described in detail later, examples of the quantization method include scalar quantization and vector quantization.

[0059] Furthermore, the following are being considered regarding the quantization of CSI feedback: quantization non-aware training (QNT); quantization aware training (QAT); quantization methods, including scalar and vector quantization, uniform and non-uniform quantization, and associated parameters (such as quantization resolution); and methods of using the quantization methods.

[0060] [QAT and QNT] As described above, quantization-aware training (QAT) and quantization-non-aware training (QNT) are being considered. FIG. 5 is a diagram showing an example of QAT and QNT. In QAT, appropriate quantization may be performed during the training process. On the other hand, in QNT, CSI generation and CSI reconstruction may be performed without quantization during the training process (see, for example, FIG. 3).

[0061] In this disclosure, V, Enc ｒ , Z ｅ , Q, DQ, Z ｑ , Dec ｒ ,V',Loss,V Ｔａｒｇｅｔ , may respectively represent the input to the encoder, the encoder, the output from the encoder (e.g., the value before quantization), the quantizer, the dequantizer, the input to the decoder (e.g., the value after quantization), the decoder, the output from the decoder, the loss function, and the target value. Also, {e} represents an embedding vector, which may be a codebook representing all values ​​that can be quantized. This also applies to the following drawings.

[0062] As shown in FIG. 5, for example, in the QAT, a predetermined input value V (e.g., CSI) is input to the encoder, and a pre-quantization value Z is output. ｅ The Z ｅ is input to the quantizer / dequantizer, and the output is the quantized value Z ｑ The Z ｑ is input to a decoder after inverse quantization. A decoded value V' is obtained as the output. V' is input to a loss function. A reconstructed value (e.g., CSI) is obtained as the output. The reconstructed value may be re-input to the encoder / decoder for further training. A target value may also be input to the loss function. This makes it possible to further reduce communication overhead. Note that the above values ​​may be converted into bits.

[0063] On the other hand, for example, in QNT, the value Z ｅ may be input directly to the decoder without going through a quantizer / dequantizer. A decoded value V' may be obtained as the output. V' is input to a loss function. A reconstructed value (e.g., CSI) is obtained as the output. The reconstructed value may be re-input to the encoder for further training. Other operations common to QAT will be omitted.

[0064] Furthermore, QAT considers the following cases in which quantization is performed during the training process: <Case 1> A case in which a fixed / preset quantization method / quantization parameters are applied in the training process, and the same quantization codebook is also applied in the inference process. <Case 2> A case in which the quantization method / quantization parameters are updated along with the AI / ML model during training, and the final quantization codebook is applied to perform inference after training is completed.

[0065] [Types of Quantization Methods] The above-mentioned quantization methods can be classified as follows, for example: <Scalar Uniform Quantization> In this method, each unit in the latent space is independently quantized to a scalar value. The quantized value is used equally for all units, and each unit may have the same bit width. <Scalar Non-Uniform Quantization> In this method, each unit in the latent space is independently quantized to a one-dimensional value. A different quantization method may be applied to each unit, and each unit may have a different bit width. <Vector Quantization> In this method, units or subsets of units in the latent space are jointly quantized to a multidimensional vector (embedding vector). In this case, the subsets may be divided evenly or irregularly. The subsets may share the same set of embedding vectors or different sets of embedding vectors.

[0066] The bit width of each unit may be predefined. The number of embedding vectors may be the size of the codebook, and the length of the embedding vector may be the number of subset units. For example, since the size of the codebook is 2^4=16, 4 bits are required to represent one codebook. Alternatively, one codebook may be fed back for each subset.

[0067] The embedding vectors described above can be trained. A predetermined loss function may be used for the training. For example, a loss function (Loss2) used to update the embedding vector (a codebook representing all possible quantized values) and a loss function (Loss3) used to update the encoder to generate the normalized output (value before quantization) of the encoder may be applied to the training. In other words, the embedding vector and the output before quantization of the encoder are required to calculate these loss functions.

[0068] It has also been proposed to use a nested dropout (ND) layer as the final layer of the encoder. During training, nodes with high indices are more likely to be dropped out by the ND layer. As a result, it is assumed that units with low indices are more important.

[0069] After training, the bit allocation for each unit may be performed using a validation dataset. As mentioned above, units with smaller indices are more important and therefore are allocated more bits. As a result, each unit will have a different bit width.

[0070] At each training step, if unit m appears (m represents the index of the unit), it may be guaranteed that units 1,...,m-1 are also kept. Units with low indices (e.g., m=1,2,3) may contain more information than units with large indices.

[0071] (Model Training) As a model training method between a UE and a NW (BS), for example, Type 1 / Type 2 / Type 3 training has been proposed. In Type 1 training, joint training is performed in either the UE or NW entity, and a model is provided to the other entity. Alternatively, joint training may be performed in both entities, and a model may be provided to each entity.

[0072] Fig. 6A is a conceptual diagram showing an example of Type 2 training, Fig. 6B is a conceptual diagram showing an example of UE-first Type 3 training, and Fig. 6C is a conceptual diagram showing an example of NW-first Type 3 training. In Type 2 training, the UE and NW sides jointly train through the exchange of gradients, activations, and target outputs (see Fig. 6A).

[0073] In Type 3 training, a first training is performed on one of the entities, UE or NW, and a second training is performed on the other entity. That is, training is performed separately on each entity. Type 3 training may also be called separate / sequential training.

[0074] As shown in Figure 6B, in UE-first Type 3 training, training is first performed on the UE-side model (Step 1). That is, the UE trains the encoder and decoder. Next, a training data set is shared between the UE and the NW (Step 2). That is, the UE delivers input and output data sets from the decoder to the NW. Then, training is performed on the NW-side model (Step 3). That is, the NW trains the decoder based on the received data set.

[0075] As shown in FIG. 6C , in NW-first Type 3 training, a target data set (e.g., CSI) is first shared between the UE and the NW (step 0). Then, training is performed on the NW-side model (step 1). That is, the NW trains the encoder and decoder. Next, the training data set is shared between the UE and the NW (step 2). That is, the NW delivers input and output data sets from the decoder to the UE. Then, training is performed on the UE-side model (step 3). That is, the UE trains the decoder based on the received data set.

[0076] In the above-described model training, an FP value indicating a value between two layers in forward propagation and a BP value indicating a value between two layers in backward propagation may be used.

[0077] Quantization and quantization alignment are necessary in such model training procedures.

[0078] (Quantization for CSI Feedback) Figure 7 is a diagram showing an example of CSI feedback using an encoder / decoder incorporating quantization. The UE performs specific quantization in encoding or on encoded values / bits, and transmits information including the quantized bits (CSI feedback) from an antenna. Note that in this disclosure, "AI model layer(s)" may refer to an encoder / decoder implemented using one or more layers.

[0079] The base station applies corresponding dequantization to the bits of the received CSI feedback, and inputs the dequantized values / bits to corresponding decoders to obtain the reconstructed input information to be output.

[0080] [Uniform Quantization] The specific quantization may be uniform quantization, which corresponds to quantization in which the quantization step width is the same (uniform) regardless of the value to be quantized (or the quantization index), and may also be called linear quantization.

[0081] For example, the quantized value = round(2 Ｂ－１ *x) / 2 Ｂ－１ Here, round(A) is a function for rounding A, x is an input value, and 2 Ｂ－１ may be a quantization level, and B may be the number of quantization bits. Note that round(A) may be interpreted as a ceiling function for A, a floor function for A, or the like.

[0082] The UE may determine the quantization level / sampling interval based on specific rules / UE capabilities, or may be notified using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), specific signals / channels, or a combination thereof.

[0083] The UE may also report the determined quantization level / sampling interval using physical layer signaling (e.g., UCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel, or a combination thereof. For example, the UE may report information about the determined quantization level / sampling interval using CSI Part 1 of a CSI report.

[0084] [Non-uniform quantization] The above specific quantization may be non-uniform quantization, which corresponds to quantization in which the quantization step width varies (is not uniform) depending on the value to be quantized (or the quantization index), and may also be called non-linear quantization.

[0085] For example, the quantized value may be obtained using a-law coding / μ-law coding (algorithm) for the input value x. These coding methods are effective when small values ​​occur more frequently than large values.

[0086] The UE may determine the parameters for determining the non-linear quantization function based on a specific rule / UE capability, or may be notified using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel, or a combination thereof.

[0087] Furthermore, the UE may report parameters for determining the determined non-linear quantization function using physical layer signaling (e.g., UCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel, or a combination thereof. For example, the UE may report information regarding parameters for determining the determined non-linear quantization function using CSI Part 1 of a CSI report.

[0088] The UE may determine the quantization level / sampling interval based on specific rules / UE capabilities, or may be notified using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), specific signals / channels, or a combination thereof.

[0089] The UE may also report the determined quantization level / sampling interval using physical layer signaling (e.g., UCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel, or a combination thereof. For example, the UE may report information about the determined quantization level / sampling interval using CSI Part 1 of a CSI report.

[0090] The UE may determine whether to control quantization according to the above-mentioned uniform quantization or non-uniform quantization (or whether to perform quantization) based on a specific rule / UE capability, or may be notified using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel, or a combination thereof, thereby appropriately controlling quantization into output bits of the encoder.

[0091] For example, the non-uniform quantization described above allows for smaller step sizes at low amplitudes than at high amplitudes, which is effective when low amplitudes are observed more frequently than high amplitudes.

[0092] (AI model transfer to support Beam Management (BM) optimization) If the UE supports AI model inference (if the UE reports a UE capability indicating that it supports AI model inference), the BS may select an appropriate AI model and transfer information related to the selected AI model (hereinafter referred to as AI model related information, simply related information, etc.) to the UE.

[0093] 8 is a diagram illustrating an example of AI model related information. The related information may include at least one of information such as a model ID, a model function, an input / output of the model, and an application range.

[0094] As shown in FIG. 8, the model ID may include an integer, a character string, etc. The model function may include, for example, a description of the function of the AI ​​model, such as "estimating the best CSI-RS." The input of the model may be the RSRP of SSBs #1-#n. The output of the model may be the best CSI-RS index (CSI-RS resource ID). The application range may indicate, for example, that cells compatible with AI-assisted technology (cells that can be the target of beam reports based on AI-assisted beam estimation, cells that may use AI-based prediction / estimation) are cells #1-#3. The application range may be indicated by a physical cell ID, a serving cell index, etc.

[0095] As shown in FIG. 7, the AI ​​model related information may include the model ID and other related information (e.g., model input / output, etc.), or may include only the model ID.

[0096] In addition, the UE may determine other related information from the notified model ID based on a specific rule, that is, the model ID may be mapped to other related information.

[0097] The UE may decide which ML model to apply (to use for BM optimization) based on the relevant information transferred from the BS.

[0098] (Quantization Information) Hereinafter, information regarding quantization and dequantization (which may be collectively referred to as quantization information or quantization-related information) will be described. In the present disclosure, quantization and dequantization may be interpreted as interchangeable.

[0099] The quantization information may include the following: (1) Information about whether quantizer / dequantizer is supported in training (QAT / QNT support), or whether quantizer / dequantizer is considered during training (QAT / QNT support), which may include index(es) of models trained by QAT with the corresponding quantizer / dequantizer. (2) Information about whether the quantization / dequantization is trainable (can be updated during training). This information may include information about the type of quantization / dequantization that can be trained (e.g., ND layer, VQ VAE). (3) Information about how to construct the quantization / dequantization. This information may include constructing the quantization / dequantization based on K-means bit allocation, etc. (4) Information about whether the quantization / dequantization is uniform or non-uniform. (5) Information about whether the number of bits after quantization is constant for all quantization bits. (6) Information about the resolution of uniform or non-uniform quantization. (7) Information about the number of units (m) before quantization or after dequantization. (8) Information about the number of bits (B) after quantization or before dequantization. (9) Information about whether the quantization / dequantization is a vector or a scalar. (9a-1) If the quantization / dequantization (quantization type) is a vector and the units are evenly divided into subsets, at least one of the following may be included: (i) the number of units in the subset (m), (ii) the embedding vector (e), (iii) the bit width (b). (9a-2) If the quantization / dequantization (quantization type) is a vector and the units are unevenly divided into subsets, at least one of the following may be included: (i) the number of units in each subset (m1, m2, ..., m n ), (ii) the embedding vectors of each subset ({e1}, {e2}, ..., {e n}), (iii) the bit width of each subset (b1, b2, ..., b n(9b-1) When the quantization / inverse quantization (quantization type) is scalar uniform quantization, the bit width of each unit (b). (9b-2) When the quantization / inverse quantization (quantization type) is scalar non-uniform quantization, the bit width sequence of each unit (b1, b2, ... b M ).

[0100] (Life Cycle Management (LCM)) In future wireless communication systems (for example, Rel. 18 and later), the introduction of multiple LCMs is being considered.

[0101] The plurality of LCMs may include a functionality-based LCM and a model-ID-based LCM. The functionality-based LCM may be referred to as a functionality-based LCM, and the model-ID-based LCM may be referred to as a model-ID-based LCM.

[0102] In functionality-based LCM, the network (e.g., a base station / network node) may instruct operations related to the functionality of the AI / ML (e.g., at least one of activation, deactivation, fallback operation, and switch).

[0103] The UE may perform model-level LCM (eg, model switching and / or model selection) among the indicated functionality.

[0104] Among other things, the functionality may be transparent as to which models are activated / deactivated.

[0105] UE Capability information reporting may be used to signal supported functionality.

[0106] In model-ID-based LCM, a network (e.g., a base station / network node) may instruct an operation (e.g., at least one of activation, deactivation, fallback operation, and switch) related to an individual AI / ML model by a model ID.

[0107] The UE may perform model-level LCM (e.g., at least one of model switching and model selection) based on instructions from the NW.

[0108] A model may be defined in the NW by a model identifier (ID).

[0109] (Cases Related to Model Delivery / Transfer) Regarding model delivery / transfer, when a model is trained by a network, three cases are defined in existing scenarios.

[0110] [Case y] First, the NW trains a model and distributes the model to the UE outside the 3GPP network through offline engineering by multiple vendors.

[0111] The UE then reports its support for the delivered model (this step may be called model identification).

[0112] [Case z2] First, the NW trains a model through offline engineering of multiple vendors and saves it in a proprietary format, which may mean a format defined for each vendor.

[0113] The UE then reports the use of the stored model in the 3GPP network (this step may be called model identification).

[0114] A model transport is then carried out.

[0115] The UE then reports support for the transferred model (this step may be called model identification).

[0116] [Case z4] First, the UE reports the supported model structure (this step may be called model identification).

[0117] The NW then forwards the model parameters of the supported model structures.

[0118] The UE then reports support for the transferred model (this step may be called model identification).

[0119] (Functionality Identification) As a procedure for identifying functionality, for example, the UE may report a specific condition in the UE capability (capability information). In this case, the NW may configure the corresponding functionality based on the reported condition.

[0120] Here, functionality may represent features / feature groups (FGs) available in the AI / ML that are enabled by a certain configuration, such as a set of RRC parameters / LPP parameters. The configuration may be supported based on conditions indicated by the UE capabilities. Furthermore, functionality may represent units that the NW can control on the UE side in the operation (activation / deactivation / switching) of LCM.

[0121] The operation of the LCM based on functionality may be controlled based on the configuration of features / feature groups available in the AI / ML described above, where signaling (signaling for activation / deactivation / switching) to support the operation of the LCM based on the functionality is considered.

[0122] The UE may also report applicable functionality updates, e.g., a mechanism for updating the applicable model after identifying the model needs to be considered.

[0123] (Model Identification) A model identified by a model ID may be associated with, for example, settings / conditions / additional conditions (specific scenarios, sites, data sets, etc.). A model may represent a unit that the NW can control on the UE side in the operation (activation / deactivation / switching) of LCM.

[0124] The operation of the Model ID-based LCM may be controlled based on the identified model, where the model may be associated with specific settings / conditions regarding UE capabilities of features / feature groups available in the AI / ML, and additional conditions determined / identified between the UE side and the NW side.

[0125] In addition, it is assumed that the identification process and control unit are different between the functionality-based LCM and the model ID-based LCM. Here, it is being considered to share the activation / deactivation / switching procedures between the functionality-based LCM and the model ID-based LCM.

[0126] The following types of model identification procedures are also considered: Type A: Model information and model IDs are associated without signaling. The UE reports supported model IDs to the NW. That is, the mapping between model IDs and model information is identified to the NW and UE without signaling. The NW and UE identify the corresponding model information based on the received model ID. Type B1: Model information is reported from the UE to the NW via the air interface (signaling). Model identification is initiated by the UE, and the NW assists (takes responsibility for) the remaining steps of model identification. During model identification, a model ID may be assigned to the model. Type B2: Model information is reported from the NW to the UE via the air interface (signaling). Model identification is initiated by the NW, and the UE responds to the remaining steps of model identification. During model identification, a model ID may be assigned to the model.

[0127] Type B1 can be further classified into Type B1-1 and Type B1-2.

[0128] <Type B1-1> Type B1-1 refers to a type of model identification in which an ID is assigned by the NW, and may include the following procedures: ・The UE (re)trains and deploys a new model. ・The UE reports the existence of the new model along with any associated additional conditions. ・The NW indicates the model ID assigned to the reported model.

[0129] <Type B1-2> Type B1-2 refers to a type of model identification where the ID is assigned by the UE, and may include the following steps: The UE (re)trains a new model. The UE assigns a model ID to the new model. The UE reports the existence of the new model with additional conditions and the associated model ID.

[0130] Type B2 can be further classified into Type B2-1 and Type B2-2.

[0131] <Type B2-1> Type B2-1 refers to a type of model identification corresponding to model transfer, and may include the following procedures: ・UE reports supportable model information. ・NW (re)trains a new model. ・NW transfers the new model (with the model ID assigned to the new model) together with the model ID to the UE.

[0132] <Type B2-2> Type B2-2 refers to a type of model identification corresponding to data collection initiation, and may include the following procedures: The NW initiates collection of a dataset for model training to the UE and assigns a model ID. The UE trains a model based on the collected dataset. The UE notifies that the model associated with the model ID has been deployed.

[0133] (Meta Information) The UE may receive at least one of the following as meta information.

[0134] Note that the term "meta information" in this disclosure is merely an example, and meta information may refer to at least one of specific setting information, scenario information, environmental information, support information, and model information. In this disclosure, meta information, setting information, scenario information, environmental information, support information, and model information may be read interchangeably.

[0135] The meta information used by the UE / NW may include at least one of information regarding NW configuration / deployment, information regarding the environment, information regarding the AL / ML model on the NW side, and information regarding the model required by the NW.

[0136] The information regarding network settings / deployment may include, for example, information regarding antenna settings.

[0137] The information regarding the antenna configuration may indicate, for example, at least one of the following: horizontal / vertical antenna element / panel number, port number, antenna spacing, antenna position, panel position, and transceiver unit (TxRU) mapping.

[0138] The information regarding network configuration / deployment may include, for example, information regarding beam configuration.

[0139] The information about the beam setting may include, for example, at least one of the beam width, the number of beams, and the beam direction.

[0140] The information regarding network configuration / deployment may include, for example, information regarding TRP.

[0141] The information regarding the beam setting may include, for example, at least one of the altitude of the TRP and the relative positions of the multi-TRP.

[0142] The information about the environment may include, for example, information about the deployment scenario.

[0143] The information about the deployment scenario may indicate, for example, at least one of Urban Macro (UMa), Urban Micro (Umi), and Indoor Hotspot (InH).

[0144] The information about the environment may include, for example, information about indoors or outdoors.

[0145] The information about indoor or outdoor may indicate, for example, an indoor / outdoor probability.

[0146] The information about the environment may for example be information about objects around the UE / base station.

[0147] The information about the objects around the UE / base station may, for example, indicate the location of the objects around the UE / base station.

[0148] The information about the environment may include, for example, the scenario setting format (meta information) described below.

[0149] Use cases using AI models may be associated with a scenario-setting format that consists of long-term features.

[0150] Note that the term "long-term features" may be interchangeably read as "short-term / mid-term / long-term features" or simply "features." Furthermore, the term "scenario setting format" may be interchangeably read as "meta information," "meta information format," "scenario and configuration format," "scenario configuration format," "scenario format," "setting format," "use case format," "environment format," "meta format," etc. Furthermore, the term "format" may be interchangeably read as "type," "mode," "data," "setting," etc.

[0151] The above features may include one or more combinations of the following elements: - Scenario / model (Urban Macro (UMa), Urban Micro (Umi), indoor, outdoor, indoor hotspot (InH), etc.); - Frequency / frequency range; - Numerology (or subcarrier spacing); - Distribution / set of general channel parameters (e.g., inter-site distances (ISD), gNB height, delay spread, angle spread, Doppler spread, etc.) in a single scenario / model; - UE distribution; - UE speed; - UE orbit; - Number of transmit beams / receive beams; - UE rotation pattern; - gNB / UE antenna configuration (e.g., transmit and receive antenna vectors); - Number of cells / sectors; - Bandwidth; - UE payload; - Channel quality (e.g., RSRP, SINR); - Beam configuration ID; - Physical Cell ID (PCI). Global Cell ID (GCI) Absolute Radio Frequency Channel Number (ARFCN) Line Of Site (LOS) / Non-Line Of Site (NLOS) probability.

[0152] A UE may be expected to be configured / registered with a model whose associated scenario configuration format matches the UE's configuration / status.

[0153] The UE may also be expected to activate a model whose associated scenario configuration format matches the UE's configuration / status.

[0154] The correspondence between a use case and a scenario configuration format may be specified in a standard, and information on the correspondence may be notified to the UE. Also, the features included in the scenario configuration format corresponding to the use case may be specified in a standard, and information on the features may be notified to the UE.

[0155] The information on the AL / ML model on the NW side may include, for example, information on paired models available on the NW side.

[0156] The information about the paired model available on the NW side may indicate, for example, a paired decoder for CSI compression.

[0157] The information about the AL / ML model on the NW side may include, for example, information about pre-processing / post-processing available on the NW side.

[0158] The information regarding pre-processing / post-processing available on the NW side may include, for example, at least one of quantization / dequantization processing, DFT transformation, IDFT transformation, FFT transformation, and IFFT transformation.

[0159] UE Assistance Information The UE may report assistance information / metadata (meta-information) for the AI / ML model.

[0160] The assistance information may include, for example, at least one of the following information (user status): overheating assistance information; DRX parameter preference; priority regarding maximum aggregate bandwidth; preference for maximum number of MIMO layers.

[0161] The AI / ML model may be an AI / ML model that is registered / configured / compiled / activated in the UE.

[0162] The aiding information / metadata for the AI / ML model may be transmitted together with or instead of the beam information, which may be, for example, information about the UE's antennas / beams.

[0163] The supporting information / metadata of the AI / ML model may be at least one of the information described below.

[0164] The supporting information / metadata for the AI / ML model may be the ID of the AI / ML model.

[0165] The ID of the AI / ML model may be a global / local AI / ML model ID.

[0166] The supporting information / metadata for the AI / ML model may be information regarding the applicable bandwidth corresponding to the AI / ML model ID.

[0167] The bandwidth may be indicated as the applicable minimum / maximum bandwidth.

[0168] The information about the bandwidth may include, for example, information indicating a band indicator (e.g., "freqBandIndicatorNR"). The information indicating the band indicator may be represented by a specific number of bits (e.g., 10 bits).

[0169] The information about the bandwidth may include, for example, information indicating the bandwidth of the RS associated with the corresponding AI / ML model (eg, "supportedBandwidth").

[0170] The information indicating the bandwidth of the RS associated with the corresponding AI / ML model may indicate the frequency for each frequency range (for example, FR1 / FR2 (FR2-1 / FR2-2) / FR3 / FR4 / FR5).

[0171] The supporting information / metadata of the AI / ML model may be information about the applicable area corresponding to the AI / ML model ID.

[0172] The information regarding the applicable area corresponding to the AI / ML model may include at least one of the following information (list of information): Area ID. Cell global ID (in NR). Physical cell ID (Identifier) ​​(in NR). ARFCN (Absolute Radio Frequency Channel Number). Evolved Cell Global ID (ECGI).

[0173] The area ID may include at least one of the global ID of the NR cell, the physical cell ID of the NR, and the ARFCN.

[0174] The support information / metadata for the AI / ML model may be antenna setting / beam information corresponding to the AI / ML model ID.

[0175] (KPI) Common Key Performance Indicators (KPIs) are being considered for monitoring the performance of AI models.

[0176] Below is an initial list of common KPIs for evaluating the performance impact of AI / ML models: Performance, Intermediate KPIs, Link-level and system-level performance, Generalization performance, Over-the-air overhead, Assistance information overhead, Data collection overhead, Model delivery / transfer overhead, Other AI / ML model related signaling overhead, Inference complexity, Model inference computational complexity: floating point operations (FLOPs) (note that the s is lowercase), Pre- and post-processing computational complexity, Model complexity (number of parameters / data size (e.g., Mbyte), etc.), Training complexity, LCM related complexity. complexity) and storage overhead, and latency (e.g., inference latency). Note that the above KPIs are merely examples, and other KPIs may be added to the list (e.g., KPIs related to model training, use-case-specific KPIs considered for a given use case, etc.).

[0177] Of the above-mentioned KPIs, KPIs related to performance may be called performance KPIs.

[0178] Functionality Related Information A UE may report the functionality it supports (or is supported).

[0179] A functionality may be associated with, include, or belong to a particular piece of information.

[0180] In this disclosure, "be associated with," "include," "belong to," and "correspond to" may be used interchangeably.

[0181] The specific information may be at least one of the following: The specific information and the functionality-related information may be interchangeable.

[0182] The particular information may be information regarding applicable conditions / settings.

[0183] The specific information may be, for example, applicable NW configurations. For example, the applicable parameters related to the NW configurations may be at least one of parameters of system information and parameters of assistance information.

[0184] The specific information may for example be applicable UE configuration, for example applicable parameters of the UE configuration may be higher layer (e.g. RRC) parameters.

[0185] The specific information may be, for example, information about an applicable scenario, such as information indicating at least one of NLOS / LOS, UE distribution, SINR, RSRP, bandwidth, frequency, indoor / outdoor, and deployment scenario (e.g., at least one of Urban Macro (UMa), Urban Micro (Umi), and Indoor Hotspot (InH)).

[0186] The specific information may be, for example, information about an applicable arrangement, which may be information indicating at least one of an applicable antenna configuration (e.g., at least one of the number of antenna elements / panels in the horizontal / vertical directions, the number of ports, the antenna spacing, the antenna position, the panel position, and the transceiver unit (TxRU) mapping), a beam configuration (e.g., at least one of the beam width, the number of beams, and the beam direction), and TRP information (e.g., at least one of the altitude of the TRP and the relative positions of multiple TRPs).

[0187] The specific information may be, for example, information about an applicable site, or may be information indicating at least one of an area ID, an NR cell global ID, an NR physical cell ID, a specific frequency (e.g., ARFCN), and an ECGI.

[0188] The specific information may be, for example, information about an applicable paired model. The paired model may be, for example, two (two or more) network-side models (combinations of models). The specific information may be, for example, information indicating a paired model for CSI compression.

[0189] The specific information may be, for example, information about an applicable time. The information may be, for example, information indicating an applicable period / interval / duration. The period may be, for example, indicated using a specific time unit (e.g., slot / symbol / subslot / millisecond / second).

[0190] The applicable conditions / settings may be predefined in the specification or may be determined / identified using a specific ID / token. For example, the applicable settings / scenarios / conditions may be defined as specific parameters (e.g., test parameters) or may be represented using a specific ID / token.

[0191] The performance of a functionality (e.g., prediction accuracy, location error) may be assumed to be better (e.g., higher) than a particular threshold under applicable conditions / settings, which may be, for example, the conditions / settings of a particular test.

[0192] The thresholds / performance requirements may be pre-specified or may be determined / identified using a specific ID / token.

[0193] If the threshold / performance requirements are based on a specific ID / token, each vendor / operator can define and utilize the desired thresholds / performance.

[0194] (Analysis) <Model Pairing> Model pairing for two-sided models (e.g., CSI compression and CSI feedback) has been considered, but the detailed procedures and mechanisms for model pairing have not yet been clarified.

[0195] In the use case of CSI compression using a two-sided model, it is considered to enable the UE to select a CSI generation model that is compatible with the CSI reconstruction model used by the gNB (NW).

[0196] - The UE reports the supported features / feature groups (FGs) of AI / ML-based CSI feedback in the UE capability report. - Additional information to align the pairing information. For example, the UE reports the pairing information in the UE capability report for NW confirmation (in the case where the UE initiates model pairing). Alternatively, the NW indicates the pairing information supported in the cell for UE confirmation (in the case where the NW initiates model pairing). - The pairing information may be considered as a model ID. - The model ID may be expressed starting from the pairing information.

[0197] <Model Pairing with Quantization Alignment> There is also further discussion on quantization alignment, which may be particularly relevant to the model pairing procedure.

[0198] In the use case of CSI compression using two-sided models, quantization alignment for CSI feedback between the UE-side CSI generator and the NW-side CSI reconstructor should be supported by the specification based on standardized quantization methods, e.g., through a model pairing process.

[0199] <Model Pairing Regarding Consistency> Furthermore, the consistency (which may also be called consistency or consistency) between the model training and model inference on the UE side may also be related to the model pairing procedure, and further discussion is underway. For example, the following content is being discussed.

[0200] In the UE-side model inference, the following options are considered to ensure consistency between model training and model inference regarding additional conditions on the NW side.

[0201] (Opt1): Model identification to achieve alignment between the NW side and the UE side under additional conditions on the NW side. (Opt2): Model training on the NW side and model transfer to the UE, where the model may be trained under the additional conditions. (Opt3): Information / instructions regarding additional conditions on the NW side are provided to the UE. (Opt4): Consistency supported by monitoring. The monitoring may be monitoring by the UE / NW and may be performance monitoring of candidate models / functionality to select a model / functionality on the UE side.

[0202] <Conditions and Additional Conditions> In Functionality Identification and Functionality-Based LCM (UE-part of UE-side model / two-sided model), functionality may represent features / feature groups (FG) available in AI / ML that are enabled by a certain configuration, e.g., a set of RRC parameters / LPP parameters, which may be supported based on the conditions indicated by the UE capabilities.

[0203] In model identification and model ID based LCM (UE-part of UE-side model / two-sided model), the operation of model ID based LCM may be controlled based on the identified model, where the model may be associated with specific settings / conditions regarding UE capabilities of features / feature groups available in AI / ML and additional conditions determined / identified between the UE side and the NW side.

[0204] For features / feature groups available in AI / ML, additional conditions may represent any aspect that is assumed / considered for model training but is not part of the UE capabilities for the features / feature groups available in AI / ML. Note that the additional conditions are optional and do not necessarily imply that the additional conditions are specified.

[0205] The additional conditions may be classified into two types: additional conditions on the NW side and additional conditions on the UE side.

[0206] Note that both "conditions" and "additional conditions" may be used to reach a consensus between the UE-side model and the NW-side model. "Conditions" may refer to a set of candidate values / parameters of UE capabilities (associated with features / feature groups available in AI / ML). "Additional conditions" may be a separate concept that is assumed / considered for model training but is not included in "conditions."

[0207] The difference between "condition" and "additional condition" may be, for example, as follows: "condition" is reported through UE capability, while "additional condition" is not reported (not indicated / specified / specified) by UE capability. In this case, "additional condition" may be indicated / specified / specified by information / indication about the additional condition.

[0208] As mentioned above, detailed procedures and mechanisms for model pairing (especially for models and quantization) have not yet been clearly defined. Unless these are properly defined, it may be difficult to achieve appropriate overhead reduction, highly accurate channel estimation, and highly efficient resource utilization, which may hinder improvements in communication throughput and communication quality.

[0209] Therefore, the present inventors came up with an idea for a suitable method for realizing pairing.

[0210] (Various Replacements, etc.) Hereinafter, embodiments according to the present disclosure will be described in detail with reference to the drawings. Wireless communication methods according to the embodiments may be applied independently or in combination.

[0211] In the present disclosure, "A / B" and "at least one of A and B" may be interpreted interchangeably. Also, in the present disclosure, "A / B / C" may mean "at least one of A, B, and C."

[0212] In the present disclosure, terms such as notify, activate, deactivate, indicate (or indicate), select, configure, update, and determine may be read interchangeably. In the present disclosure, terms such as support, control, controllable, operate, and operate may be read interchangeably.

[0213] In the present disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, higher layer parameters, fields, information elements (IEs), settings, etc. may be interchangeable. In the present disclosure, Medium Access Control (MAC) control elements (CEs), update commands, activation / deactivation commands, etc. may be interchangeable.

[0214] In the present disclosure, the higher layer signaling may be, for example, any one of Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information, other messages (e.g., messages from the core network such as positioning protocol (e.g., NR Positioning Protocol A (NRPPa) / LTE Positioning Protocol (LPP)) messages), or a combination thereof.

[0215] In the present disclosure, MAC signaling may use, for example, a MAC Control Element (MAC CE), a MAC Protocol Data Unit (PDU), etc. Broadcast information may be, for example, a Master Information Block (MIB), a System Information Block (SIB), Remaining Minimum System Information (RMSI), Other System Information (OSI), etc.

[0216] In the present disclosure, physical layer signaling may be, for example, Downlink Control Information (DCI), Uplink Control Information (UCI), and the like.

[0217] In the present disclosure, the terms index, identifier (ID), indicator, resource ID, etc. may be interchangeable. In the present disclosure, the terms sequence, list, set, group, cluster, subset, etc. may be interchangeable.

[0218] In the present disclosure, the terms panel, UE panel, panel group, beam, beam group, precoder, Uplink (UL) transmitting entity, Transmission / Reception Point (TRP), base station, Spatial Relation Information (SRI), spatial relation, SRS Resource Indicator (SRI), Control Resource Set (CORESET), Physical Downlink Shared Channel (PDSCH), Codeword (CW), Transport Block (TB), Reference Signal (RS), antenna port (e.g., Demodulation Reference Signal (DMRS) port), antenna port group (e.g., DMRS port group), group (e.g., spatial relation group, Code Division Multiplexing (CDM) group, reference signal group, CORESET group, Physical Uplink Control Channel (PUCCH) group, PUCCH resource group), resource (e.g., reference signal resource, SRS resource), resource set (e.g., reference signal resource set), CORESET pool, downlink Transmission Configuration Indication state (TCI state) (DL TCI state), uplink TCI state (UL TCI state), unified TCI state, common TCI state, Quasi-Co-Location (QCL), QCL assumption, etc. may be read as interchangeable.

[0219] In the present disclosure, CSI-RS, non-zero power (NZP) CSI-RS, zero power (ZP) CSI-RS, and CSI interference measurement (CSI-IM) may be interchangeable. Furthermore, CSI-RS may include other reference signals.

[0220] In this disclosure, a measured / reported RS may refer to an RS that is measured / reported for a CSI report.

[0221] In the present disclosure, timing, time, duration, slot, subslot, symbol, subframe, etc. may be read interchangeably.

[0222] In the present disclosure, the terms direction, axis, dimension, domain, polarization, polarization component, etc. may be read interchangeably.

[0223] In the present disclosure, estimation, prediction, and inference may be used interchangeably. Also, in the present disclosure, estimate, predict, and infer may be used interchangeably.

[0224] In the present disclosure, the terms autoencoder, encoder, decoder, etc. may be replaced with at least one of a model, an ML model, a neural network model, an AI model, an AI algorithm, etc. Furthermore, the term autoencoder may be replaced with any autoencoder, such as a stacked autoencoder or a convolutional autoencoder. The encoder / decoder of the present disclosure may employ a model such as a Residual Network (ResNet), a DenseNet, or a RefineNet.

[0225] In the present disclosure, the terms bit, bit string, bit sequence, sequence, value, information, value obtained from a bit, information obtained from a bit, etc. may be read interchangeably.

[0226] In the present disclosure, a layer (for an encoder) may be interchangeably read as a layer (such as an input layer or an intermediate layer) used in an AI model. The layer in the present disclosure may correspond to at least one of an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolutional layer, an activation layer, a dense layer, a normalization layer, a pooling layer, an attention layer, a dropout layer, a fully connected layer, etc.

[0227] In the present disclosure, RSRP may be interchangeably read as any parameter related to received power / received quality, etc. (e.g., RSRQ, SINR, CSI), etc.

[0228] In the present disclosure, the RS may be, for example, a CSI-RS, an SS / PBCH block (SS block (SSB)), etc. Also, the RS index may be a CSI-RS Resource Indicator (CRI), an SS / PBCH Block Indicator (SSBRI), etc.

[0229] In the present disclosure, channel measurement / estimation may be performed using at least one of, for example, a Channel State Information Reference Signal (CSI-RS), a Synchronization Signal (SS), a Synchronization Signal / Physical Broadcast Channel (SS / PBCH) block, a Demodulation Reference Signal (DMRS), a Sounding Reference Signal (SRS), and the like.

[0230] In the present disclosure, the terms "receive beam assumption," "number of receive beams," "index of receive beam," "receive beam selection," "receive beam setting," and "receive beam instruction" may be interchangeable. In the present disclosure, the terms "receive beam," "transmit beam," "DL receive beam," "DL transmit beam," and "pair of transmit beam and receive beam" may be interchangeable. In the present disclosure, the terms "transmit / receive beam" may be interchangeable with the terms "transmit / receive beam for beam prediction" and "transmit / receive beam for CSI measurement / reporting for beam prediction."

[0231] In this disclosure, functionality may refer to the use of a model or the physical meaning of the model's input / output. Multiple models may have the same functionality. Monitoring (checking performance), activation, deactivation, switching, fallback, and updating may be instructed (controlled) based on the functionality (e.g., for each function).

[0232] In this disclosure, functionality may refer to features (requiring AI / ML capabilities) (e.g., CSI prediction / CSI compression / temporal beam prediction / reporting information based on spatial domain beam prediction).

[0233] A model ID may also refer to an identifier for a model (or a set of models). Multiple models may be assigned the same model ID in an actual deployment. In this case, these models may actually be different models (e.g., have different numbers of layers) but may be treated as the same model.

[0234] In the present disclosure, the model ID may be interchangeably read as a meta-information (or a set of meta-information) ID. The meta-information (or meta-information ID) may be associated with information about the applicability of the model / functionality, the environment, the UE / gNB settings, etc.

[0235] In the present disclosure, functionality may be simply read as "function."

[0236] In the present disclosure, functionality, function, functionality ID, model, and model ID may be read interchangeably.

[0237] In the present disclosure, applicable update, updating applicable models / functionality, updating applicability of models / functionality, and applicable update may be read interchangeably.

[0238] In the present disclosure, update, report, and send may be read interchangeably.

[0239] In the present disclosure, applicability report and applicability report may be read interchangeably.

[0240] In the present disclosure, meta information, assistance information, sensing information, KPI, performance KPI, UE status, and status may be read interchangeably.

[0241] In the present disclosure, the terms drop, abort, cancel, puncture, rate match, postpone, do not transmit, etc. may be read interchangeably.

[0242] In the present disclosure, consistency, coherence, and consistency may be read interchangeably.

[0243] In the present disclosure, consistent and matching may be read interchangeably.

[0244] In the present disclosure, an ID may represent an ID corresponding to (for identifying) at least one of a dataset, a model, a channel / RS property, a quantization, and a consistency. That is, in the present disclosure, an ID, a dataset ID, a model ID, a channel / RS property ID, a quantization ID, and a consistency ID may be read as interchangeable.

[0245] In the present disclosure, the terms "report initiation" and "report trigger" may be read interchangeably.

[0246] In the present disclosure, model pairing and pairing may be read interchangeably.

[0247] In the present disclosure, pairing is not necessarily limited to pairing of models (model IDs). That is, in the present disclosure, the pairing target is not limited to models (model IDs), but may be any element (e.g., quantization, consistency, etc.) associated by a specific ID.

[0248] In the present disclosure, pairing may mean having (making) a common understanding between a UE and a NW for a certain pairing target, or sharing the common understanding. For example, in the case of model pairing, it may mean that the UE and the NW share the model (ID) to be paired. Furthermore, pairing information may mean information necessary to realize the pairing.

[0249] In this disclosure, fetch may mean to search for and obtain (take out / extract) specific content from a collection (e.g., information).

[0250] In the present disclosure, mapping, table, mapping table, association, and correspondence may be read interchangeably.

[0251] (Wireless Communication Method) The embodiments of the present disclosure can be broadly categorized as follows: - 0th embodiment: Basic principle for pairing - 1st embodiment: Model ID-based pairing - 2nd embodiment: Non-Model ID-based pairing Each embodiment will be described below based on these.

[0252] In the present disclosure, each embodiment / option may be applied alone or in combination with other embodiments / options.

[0253] <Tenth Embodiment> The tenth embodiment relates to the basic principle for pairing.

[0254] The 0th embodiment can be further categorized as follows: - Embodiment 0-0: Overview of pairing procedure. - Embodiment 0-1: Model ID based pairing. - Embodiment 0-2: Non-model ID based pairing. - Embodiment 0-3: Quantization alignment.

[0255] [Embodiment 0-0] An overview of the pairing procedure will be described with reference to Fig. 9 to Fig. 11. Figs. 9A and 9B are diagrams showing an example of a model ID-based pairing procedure according to the 0th embodiment. Figs. 10A and 10B are diagrams showing an example of a non-model ID-based pairing procedure involving a dataset according to the 0th embodiment. Figs. 11A and 11B are diagrams showing an example of a non-model ID-based pairing procedure involving an additional condition according to the 0th embodiment.

[0256] The pairing procedures can be further classified as follows: (Opt1): Model ID-based pairing procedure initiated by the UE (see Fig. 9A); (Opt2): Model ID-based pairing procedure initiated by the NW (see Fig. 9B); (Opt3): Non-model ID-based pairing procedure with data set initiated by the UE (see Fig. 10A); (Opt4): Non-model ID-based pairing procedure with data set initiated by the NW (see Fig. 10B); (Opt5): Non-model ID-based pairing procedure with additional conditions initiated by the NW (see Fig. 11A); (Opt6): Non-model ID-based pairing procedure with additional conditions initiated by the UE (see Fig. 11B).

[0257] (Opt1) In FIG. 9A, the UE first reports the supported model ID(s) to the NW.

[0258] The NW performs a pairing operation (implements pairing) based on the reported model ID.

[0259] Then, the LCM procedure is performed, for example, the NW performs activation / indication of the model associated with the specific model ID ((paired model ID)) to the UE.

[0260] (Opt2) In FIG. 9B, first, the NW notifies the UE of a specific ID (one or more IDs).

[0261] Based on the notified ID, the UE performs a pairing operation or supports pairing for a specific ID (performs pairing).

[0262] The UE reports the supported ID(s) (which may be, for example, paired model IDs) to the NW.

[0263] Then, the LCM procedure is performed, for example, the NW performs activation / indication of the model associated with the specific model ID (paired model ID) to the UE.

[0264] (Opt3) As shown in FIG. 10A, first, the UE transmits information of data set(s) related to model pairing to the NW.

[0265] The NW executes a pairing operation (performs pairing) based on the received data set (information).

[0266] After that, the LCM procedure is performed, for example, the NW performs activation / indication to the UE of functionality associated with the specific data set information (paired data set).

[0267] (Opt4) In FIG. 10B, first, the NW transmits information of data set(s) related to model pairing to the UE.

[0268] The UE performs a pairing operation (implements pairing) based on the received data set (information).

[0269] The UE reports the supported data set(s) information (which may be paired data sets) to the NW.

[0270] After that, the LCM procedure is performed, for example, the NW performs activation / indication to the UE of functionality associated with the specific data set information (paired data set).

[0271] (Opt5) In Fig. 11A, first, the NW transmits information about the model pairing and additional conditions to the UE. The NW may transmit the conditions using the same / different signaling. The NW may or may not transmit the additional conditions.

[0272] The UE fetches the conditions / additional conditions from the received information.

[0273] Furthermore, the UE performs a pairing operation (implements pairing) based on the received information (extracted conditions / additional conditions).

[0274] The UE reports the supported data set(s) information (which may be paired data sets) to the NW.

[0275] After that, the LCM procedure is performed, for example, the NW performs functionality activation / indication to the UE.

[0276] (Opt6) As shown in Fig. 11B, first, the UE transmits information about the model pairing and the additional condition to the NW. The UE may or may not transmit the additional condition.

[0277] The NW fetches the conditions / additional conditions from the received information.

[0278] Furthermore, the NW executes a pairing operation (performs pairing) based on the received information (extracted condition / additional condition).

[0279] After that, the LCM procedure is performed, for example, the NW performs activation / indication to the UE of functionality associated with the specific data set information (paired data set).

[0280] [Embodiment 0-1] In embodiment 0-1, model ID-based pairing will be described. Figures 12A to 12C are diagrams showing an example of scheme 1-0 of a pairing procedure according to the 0th embodiment. Figures 13A and 13B are diagrams showing an example of scheme 1-1 of a pairing procedure according to the 0th embodiment. Figures 14A and 14B are diagrams showing an example of scheme 1-2 of a pairing procedure according to the 0th embodiment.

[0281] Pairing schemes can be classified into a model ID-based pairing procedure (Scheme 1) and a non-model ID-based pairing procedure (Scheme 2). In embodiment 0-1, scheme 1 will be described, and scheme 2 will be described later in embodiment 0-2.

[0282] Scheme 1 can be further classified into the following three schemes (Schemes 1-0 to 1-2).

[0283] (Scheme 1-0) In Scheme 1-0, a straightforward method based on a mapping table (association) of model IDs is described.

[0284] Scheme 1-0 is a method in which the UE side and the NW side have a common recognition of a certain model ID (the model ID is shared between the UE and the NW).

[0285] Specifically, as shown in Fig. 12A, a model ID #1 may be associated with a certain CSI generation model on the UE side, while as shown in Fig. 12B, a model ID #2 may be associated with a certain CSI reconstruction model on the NW side.

[0286] Here, there may be a compatible correspondence between the CSI generation model and the CSI reconstruction model.

[0287] 12C , a correspondence relationship between the UE side (e.g., side A) and the NW side (e.g., side B) may be defined. Specifically, a model ID obtainable on the UE side (side A) may be associated with (a list of) model IDs that can be paired with a model on the NW side (side B). The UE and the NW may share the mapping table shown in FIG. 12C .

[0288] In scheme 1-0, accurate and arbitrary pairing of two models is possible based on the association of model IDs on the UE side / NW side. Note that in this case, generation of a mapping table by offline engineering is required.

[0289] (Scheme 1-1) In scheme 1-1, a method of pairing using part of the model ID will be described.

[0290] For example, as shown in FIG. 13A, on the UE side, a portion of model ID #1 may be associated with a certain CSI generation model.

[0291] More specifically, assume that model ID #1 is configured, for example, with an 8-bit field, the first 4 bits of which contain pairing ID #1. Here, a 4-bit field extracted from the 8-bit model ID #1 using a pairing mask (which may also be called a model ID mask) may be designated as pairing ID #1.

[0292] That is, the UE may use a pairing mask to extract predetermined bits (for example, the first 4 bits) from the model ID #1 and set them as the pairing ID #1 (perform pairing). Here, the pairing mask may mean information for indicating the pairing ID #1.

[0293] Note that the pairing mask does not necessarily have to be used. In this case, the UE may assume that a predetermined number of bits in the model ID corresponds to the pairing ID. The predetermined number of bits may be predetermined by the specifications.

[0294] For example, as shown in FIG. 13A, on the UE side, a portion of model ID #1 may be associated with a certain CSI generation model.

[0295] More specifically, assume that model ID #1 is configured, for example, with an 8-bit field, the first 4 bits of which include (correspond to) pairing ID #1. Here, a 4-bit field extracted from the 8-bit model ID #1 using a pairing mask (which may also be called a model ID mask) may be designated as pairing ID #1.

[0296] That is, the UE may use a pairing mask to extract predetermined bits (for example, the first 4 bits) from the model ID #1 and set them as the pairing ID #1 (perform pairing). Here, the pairing mask may mean information for indicating the pairing ID #1.

[0297] Note that the pairing mask does not necessarily have to be used. In this case, the UE may assume that a predetermined number of bits in the model ID corresponds to the pairing ID. The predetermined number of bits may be predetermined by the specifications.

[0298] Also, as shown in FIG. 13B, on the NW side, a part of model ID #2 may be associated with a certain CSI reconstruction model.

[0299] More specifically, assume that model ID #2 is configured, for example, with an 8-bit field, the first 4 bits of which include (correspond to) pairing ID #2. Here, a 4-bit field extracted from the 8-bit model ID #2 using a pairing mask (which may also be called a model ID mask) may be designated as pairing ID #2.

[0300] That is, the NW may use the pairing mask to extract predetermined bits (for example, the first 4 bits) from the model ID #2 and set them as the pairing ID #2 (perform pairing). Here, the pairing mask may mean information for indicating the pairing ID #2.

[0301] Note that the pairing mask does not necessarily have to be used. In this case, the network may assume that a predetermined number of bits in the model ID corresponds to the pairing ID. The predetermined number of bits may be predetermined by the specifications.

[0302] If the pairing IDs indicated (extracted) by each UE / NW match (i.e., pairing ID #1 = pairing ID #2), the UE / NW may determine that the two models (both models) can be paired.

[0303] In scheme 1-1, two models can be paired based on a pairing ID indicated by a part of the model ID on the UE side / NW side, respectively. In this case, cooperation through offline engineering is not required.

[0304] (Scheme 1-2) In scheme 1-2, a method of using the same model ID for both models (both UE / NW models) will be described.

[0305] Specifically, as shown in Fig. 14A, a certain CSI generation model may be associated with model ID #1 on the UE side. Also, as shown in Fig. 14B, a certain CSI reconstruction model may be associated with the same model ID #1 as on the UE side (the same model ID as model ID #1) on the NW side.

[0306] In scheme 1-2, pairing of two models is possible based on a common model ID on the UE side / NW side. In this case, additional signaling is not required.

[0307] According to embodiment 0-1, the UE / NW can appropriately control model ID-based pairing.

[0308] [Embodiment 0-2] In embodiment 0-2, non-model ID-based pairing will be described. Fig. 15 is a diagram showing an example of scheme 2-1 of a pairing procedure initiated by a NW according to the 0th embodiment. Fig. 16 is a diagram showing an example of scheme 2-1 of a pairing procedure initiated by a UE according to the 0th embodiment.

[0309] Scheme 2 can be further classified into the following two schemes (Schemes 2-1 and 2-2).

[0310] (Scheme 2-1) In Scheme 2-1, a pairing method that uses a pairing data set will be described.

[0311] Scheme 2-1 can be classified into a NW-initiated scheme (see FIG. 15) and a UE-initiated scheme (see FIG. 16).

[0312] 15, the NW transmits the reconstructed model (CSI reconstruction model) to the UE as a pairing data set. This pairing data set may include information on the input and reference output of the CSI generation model corresponding to the CSI reconstruction model on the NW side.

[0313] The UE generates a CSI generation model based on the pairing data set transmitted / instructed by the NW.

[0314] The UE may then perform performance evaluation (performance monitoring) on ​​the generated CSI generation model. For example, the UE may evaluate the performance of the CSI generation model using an error indicator for a reference output. The error indicator may be interchangeable with an index (performance metric) for evaluating the performance of the CSI generation model or for evaluating the compatibility between the CSI generation model and a CSI reconstruction model.

[0315] The UE may determine whether pairing is possible based on the result of the performance evaluation (Alt1). If the UE determines that pairing is possible, the UE may perform the pairing operation. On the other hand, if the UE determines that pairing is not possible, the UE may or may not report this to the NW. If the NW receives a notification from the UE that pairing is not possible, the NW may not perform the pairing operation. Alternatively, if there is no report from the UE for a predetermined time, the NW may determine that pairing is possible / not possible.

[0316] Alternatively, the NW may determine whether pairing is possible (Alt2). In this case, the NW may receive the result of the performance evaluation from the UE, or the NW may perform the performance evaluation.

[0317] 16 , the UE transmits the generated model (CSI generation model) to the UE as a pairing data set. This pairing data set may include information on the input and reference output of the CSI generation model corresponding to the CSI reconstruction model on the NW side, or information on the input and reference output of the CSI reconstruction model corresponding to the CSI generation model on the UE side.

[0318] The NW generates / reconstructs a CSI reconstruction model based on the pairing data set transmitted / instructed by the UE.

[0319] Then, the NW may perform performance evaluation (performance monitoring) on ​​the generated / reconstructed CSI reconstruction model. For example, the NW may evaluate the performance of the CSI reconstruction model using an error indicator for a reference output. The error indicator may be interchangeably read as an index (performance metric) for performance evaluation of the CSI reconstruction model.

[0320] The NW may determine whether pairing is possible based on the result of the performance evaluation. If the NW determines that pairing is possible, the NW may perform a pairing operation.

[0321] In Scheme 2-1, the pairing operation can be flexibly controlled by adjusting / selecting the model used by either the UE or the NW. The UE / NW can also appropriately control the pairing operation based on the pairing data set. Furthermore, the pairing operation can be performed even if the model ID function is not enabled.

[0322] (Scheme 2-2) In scheme 2-2, the UE may sense (detect) / obtain conditions / additional conditions for model pairing and report the pairing result to the NW.

[0323] According to embodiment 0-2, the UE / NW can appropriately control non-model ID-based pairing.

[0324] [Embodiment 0-3] In embodiment 0-3, pairing of a model and quantization (quantization alignment) will be described. Figures 17A and 17B are diagrams showing an example of pairing of a model and quantization according to the 0th embodiment.

[0325] Quantized alignment may be integrated into model pairing in the following ways (Alt0-Alt3):

[0326] (Alt0) In all pairing schemes (Schemes 1-2 above), quantization may be treated as part of the model and may be aligned with the model pairing.

[0327] (Alt1) In schemes 1-0 and 1-2, quantization may be handled individually for quantization ID / quantization setting, i.e., quantization may be paired independently.

[0328] (Alt2) Following the same principle as the model pairing in Scheme 1-1, the model ID may include a bit portion that indicates / specifies quantization using a mask (which may be called, for example, a quantization mask).

[0329] (Alt3) Following the same principles as in Scheme 2, quantization pairing may be performed.

[0330] (Modification) As shown in Fig. 17A, a model and quantization may be paired individually. Also, as shown in Fig. 17B, when a quantization is in an inclusive relationship with a certain model (when the model and quantization are associated, or when part of the model supports quantization), pairing may be performed only on the model. In this case, since the quantization is included in the model, even if the quantization itself is not paired, the quantization is automatically paired.

[0331] In the present disclosure, the term "model" may be interchangeably read as "quantization / quantizer." The quantization may refer to an AI / ML model or a specified quantization. Furthermore, the term "functionality" may be interchangeably read as an AI / ML function or a quantization function. In other words, the pairing of the present disclosure is also applicable to quantization (alignment) pairing.

[0332] According to the embodiment 0-3, pairing is possible by applying the above scheme to quantization as well.

[0333] First Embodiment The first embodiment relates to model ID-based pairing. The above-described pairing scheme 1 is applicable to this embodiment.

[0334] The UE may receive a list of IDs (ID list) corresponding to functionality through higher layer signaling / physical layer signaling. Having received the ID list, the UE may perform the following three operations: (Operation 1): The UE may check (confirm) whether a model available on the UE side is available on the NW side based on the received ID list (including multiple received IDs). (Operation 2): The UE may report IDs that have passed the check through higher layer signaling / physical layer signaling. The UE may include (associate) the UE side ID with the corresponding received ID (ID that has passed the check) and report it. (Operation 3): If there is no ID that has passed the check (available ID), the UE may report a message notifying that fact (check result) through higher layer signaling / physical layer signaling. The message may include information about IDs that did not pass the check (unavailable IDs).

[0335] The above-mentioned operation 1 can be further classified into the following Alt1 to Alt3 depending on the type of scheme.

[0336] (Alt1) In the case of scheme 1-0, the UE may check the availability of a model based on configured / instructed pairing information. Here, the pairing information may include a list of UE-side IDs that can be paired with the received ID. The pairing information may be configured / instructed to the UE using higher layer signaling / physical layer signaling, or a method other than signaling via the air interface of the network (e.g., operator's configurations, preconfigured messages, etc.).

[0337] (Alt2) For Scheme 1-1, the UE may check the availability of a model based on the received ID and a portion of the available UE-side ID. For example, the UE may apply a pairing mask or a truncate operation to the UE-side ID. The output of the operation / operation (pairing mask / truncate) may be a portion of the UE-side ID bits. The UE may then compare the output with the received ID to check the availability of a model.

[0338] Here, the pairing mask calculation for the ID may be performed by applying an exclusive OR (XOR) calculation between two binary bit sequences.

[0339] Part of the ID / mask may be configured / indicated to the UE.

[0340] (Alt3) In the case of scheme 1-2, the UE may check the availability of the model by comparing the received ID and the UE side ID.

[0341] In the present disclosure, the ID (received ID) provided from the NW may include only a portion related to pairing (ID).

[0342] According to the first embodiment described above, the UE can appropriately control the model ID-based pairing for each applied scheme.

[0343] Second Embodiment The second embodiment relates to non-model ID-based pairing. The above-described pairing scheme 2 is applicable to this embodiment.

[0344] The second embodiment can be classified into embodiments 2-1 to 2-3 according to the type of scheme.

[0345] [Embodiment 2-1] In embodiment 2-1, pairing corresponding to scheme 2-1 / 2-2 initiated by the UE will be described.

[0346] When the UE is instructed via higher layer signaling / physical layer signaling to report information corresponding to functionality / features / capabilities, the UE may perform at least one of the following operations:

[0347] (Operation 1) The UE may report information / instructions regarding the conditions or additional conditions via higher layer signaling / physical layer signaling, where the additional conditions may include information regarding a unique device ID, a vendor / vendor ID, a scenario, an ID representing an MNO, etc.

[0348] (Operation 2) In the case of scheme 2-1, the UE may report a list of data sets, a list of IDs, a list of addresses / locations, etc., by using higher layer signaling / physical layer signaling, or a method other than signaling via the air interface of the NW (e.g., operator's configurations, pre-configured messages, etc.). Entries in each of these lists may correspond to one or more models available on the UE side.

[0349] (Operation 3) After reporting, the UE may receive, via higher layer signaling / physical layer signaling, an indication corresponding to one or more entries of the reported list, in which case the UE may activate one of the models corresponding to the indicated entries.

[0350] (Operation 4) After reporting, the UE may receive an indication of failure via higher layer signaling / physical layer signaling. The indication of failure may include an indication that pairing is not possible, an indication of functionality that is not possible to pair, etc. After the indication, the UE may not expect to receive signaling that activates functionality corresponding to the indication.

[0351] [Embodiment 2-2] In embodiment 2-2, pairing corresponding to scheme 2-1 initiated by the NW will be described.

[0352] The UE may receive or be configured with a list of datasets, a list of IDs, a list of addresses / locations, etc. via higher layer signaling / physical layer signaling. In this case, the UE may perform at least one of the following operations:

[0353] (Operation 1) When the UE receives the list of IDs / addresses / locations, it may fetch a data set based on the IDs / addresses / locations.

[0354] (Operation 2) The UE may select K entries from the dataset. The value of K may be set / indicated by higher layer signaling / physical layer signaling, may be predefined by a specification, or may be determined based on UE capabilities. Alternatively, the selection method may be to select the first K entries of the dataset, or to select K entries randomly.

[0355] (Operation 3) The UE may apply input data from the dataset to its own models, and may calculate KPIs based on the model outputs and the outputs from each model's dataset.

[0356] (Action 4) The UE may be configured / instructed to use the dataset to train / tune the UE-side model and calculate the KPIs after training / tuning.

[0357] (Operation 5) The UE may report whether the KPI is below or above a configured / instructed threshold (indicating target performance) via higher layer signaling / physical layer signaling. For example, the UE may start reporting only when the KPI is below / above the configured / instructed threshold.

[0358] (Operation 6) The UE may report the KPIs corresponding to each received list entry via higher layer / physical layer signaling, and may start reporting only if the KPIs fall below / above a configured / instructed threshold.

[0359] In the present disclosure, thresholds for performance evaluation (KPI) may be determined based on the ID (e.g., model ID / functionality ID) associated with the threshold.

[0360] [Embodiment 2-3] In embodiment 2-3, pairing corresponding to scheme 2-2 initiated by the NW will be described.

[0361] The UE may receive configuration / instructions regarding conditions, information / instructions regarding additional conditions, where the additional conditions may be, for example, at least one of regional information, location / map information, operator ID / operator name, network ID / network name, scenario, and channel condition.

[0362] The UE may perform at least one of the following actions upon indication of the condition / additional condition:

[0363] (Alt1) The UE may fetch / identify datasets based on conditions / additional conditions.

[0364] The UE may select K entries from the dataset. The value of K may be set / indicated by higher layer / physical layer signaling, predefined by a specification, or determined based on UE capabilities. The selection method may be to select the first K entries of the dataset, or to randomly select K entries.

[0365] The UE may apply input data from the dataset to its models, and may calculate KPIs based on the outputs of the models and the outputs from each model's dataset.

[0366] The UE may be configured / instructed to use the dataset to train / tune the UE-side model and to calculate the KPIs after training / tuning.

[0367] The UE may report via higher layer / physical layer signaling whether the KPI is below or above a configured / instructed threshold (indicating target performance), for example, the UE may start reporting only if the KPI is below / above a configured / instructed threshold.

[0368] (Alt2) The UE may check (verify) whether the model available on the UE side is available on the NW side based on conditions / additional conditions.

[0369] The UE may fetch a UE-side model based on the obtained conditions / additional conditions.

[0370] The UE may collect data to train / fine-tune the UE-side model for use (pairing) with the NW-side model.

[0371] The UE may report any available UE side model along with the NW side model via higher layer signaling / physical layer signaling.

[0372] The UE may report KPIs of the UE-side model via higher layer / physical layer signaling. The KPIs may be for each UE-side model identified by the UE, or may be the best (optimal) KPI for all UE-side models. The UE may start reporting only if the KPI falls below / exceeds a configured / instructed threshold.

[0373] In Alt1-2 above, the UE may report via higher layer signaling / physical layer signaling whether the KPI is below or above a set / instructed threshold (indicating target performance).

[0374] In the present disclosure, the KPI may be squared generalized cosine similarity (SGCS), mean square error (MSE), binary status, etc.

[0375] In the present disclosure, which KPIs to use / report may be determined by set / specified parameters or predefined rules.

[0376] According to the second embodiment described above, the UE can appropriately control non-model ID-based pairing for each applied scheme.

[0377] <Supplementary Notes> [Supplementary Note 1: AI Model Information] In the present disclosure, AI model information may refer to information including at least one of the following: - Input / output information of the AI ​​model. - Pre-processing / post-processing information for the input / output of the AI ​​model. - Parameter information of the AI ​​model. - Training information for the AI ​​model. - Inference information for the AI ​​model. - Performance information regarding the AI ​​model.

[0378] Here, the input / output information of the AI ​​model may include information on at least one of the following: - Contents of the input / output data (e.g., RSRP, SINR, amplitude / phase information in the channel matrix (or precoding matrix), information on the angle of arrival (Angle of Arrival (AoA)), information on the angle of departure (Angle of Departure (AoD)), location information); - Auxiliary information of the data (which may be called meta-information); - Type of the input / output data (e.g., immutable value, floating-point number); - Bit width of the input / output data (e.g., 64 bits for each input value); - Quantization interval (quantization step size) of the input / output data (e.g., 1 dBm for L1-RSRP); - Range that the input / output data can take (e.g., [0, 1]).

[0379] In the present disclosure, the information on AoA may include information on at least one of an azimuth angle of arrival and a zenith angle of arrival (ZoA). The information on AoD may include information on at least one of an azimuth angle of departure and a zenith angle of departure (ZoD).

[0380] In the present disclosure, location information may be location information related to a UE / NW. The location information may include at least one of information (e.g., latitude, longitude, altitude) obtained using a positioning system (e.g., a satellite positioning system (Global Navigation Satellite System (GNSS), Global Positioning System (GPS), etc.)), information about a BS neighboring (or serving) the UE (e.g., a BS / cell identifier (ID), a BS-UE distance, a direction / angle of the BS (UE) as seen from the UE (BS), coordinates of the BS (UE) as seen from the UE (BS) (e.g., X / Y / Z axis coordinates), etc.), a specific address of the UE (e.g., an Internet Protocol (IP) address), etc. The location information of the UE is not limited to information based on the position of the BS, and may be information based on a specific point.

[0381] The location information may include information about its implementation (e.g., location / position / orientation of antennas, location / orientation of antenna panels, number of antennas, number of antenna panels, etc.).

[0382] The location information may include mobility information, which may include information indicating at least one of information indicating a mobility type, a moving speed of the UE, an acceleration of the UE, and a moving direction of the UE.

[0383] Here, the mobility type may correspond to at least one of a fixed location UE, a movable / moving UE, a no mobility UE, a low mobility UE, a middle mobility UE, a high mobility UE, a cell-edge UE, a not-cell-edge UE, etc.

[0384] In the present disclosure, environmental information (for data) may be information about the environment in which the data is acquired / used, and may correspond to, for example, frequency information (such as a band ID), environmental type information (information indicating at least one of indoor, outdoor, Urban Macro (UMa), Urban Micro (Umi), etc.), information indicating Line Of Site (LOS) / Non-Line Of Site (NLOS), etc.

[0385] Here, LOS may mean that the UE and the BS are in an environment where they can see each other (or there is no obstruction), and NLOS may mean that the UE and the BS are not in an environment where they can see each other (or there is an obstruction). The information indicating LOS / NLOS may indicate a soft value (e.g., the probability of LOS / NLOS) or a hard value (e.g., either LOS or NLOS).

[0386] In the present disclosure, meta-information may mean, for example, information regarding input / output information suitable for an AI model, information regarding acquired / acquirable data, etc. Specifically, meta-information may include information regarding beams of RS (e.g., CSI-RS / SRS / SSB, etc.) (e.g., the pointing angle of each beam, the 3 dB beam width, the shape of the pointed beam, the number of beams), layout information of the gNB / UE antenna, frequency information, environmental information, meta-information ID, etc. Note that meta-information may be used as input / output of the AI ​​model.

[0387] The pre-processing / post-processing information for the input / output of the AI ​​model may include information on at least one of the following: Whether to apply normalization (e.g., Z-score normalization (standardization), min-max normalization); Parameters for normalization (e.g., mean / variance for Z-score normalization, min / max for min-max normalization); Whether to apply a specific numerical conversion method (e.g., one hot encoding, label encoding, etc.); Selection rules for whether to use as training data.

[0388] For example, Z-score normalization (x) is performed as a preprocessing step for input information x. ｎｅｗ = (x - μ) / σ, where μ is the mean of x and σ is the standard deviation) ｎｅｗ may be input to the AI ​​model, and the output y ｏｕｔ may be subjected to post-processing to obtain the final output y.

[0389] The information on the parameters of the AI ​​model may include information on at least one of the following: - Information on weights in the AI ​​model (e.g., neuron coefficients (connection coefficients)); - Structure of the AI ​​model; - Type of the AI ​​model as a model component (e.g., Residual Network (ResNet), DenseNet, RefineNet, Transformer model, CRBlock, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)); - Function of the AI ​​model as a model component (e.g., decoder, encoder).

[0390] Note that the weight information in the AI ​​model may include information on at least one of the following: - Bit width (size) of the weight information; - Quantization interval of the weight information; - Granularity of the weight information; - Range that the weight information can take; - Weight parameters in the AI ​​model; - Information on the difference from the AI ​​model before update (if updating); - Weight initialization method (e.g., zero initialization, random initialization (based on normal distribution / uniform distribution / truncated normal distribution), Xavier initialization (for sigmoid function), He initialization (for rectified linear units (ReLU))).

[0391] The structure of the AI ​​model may also include information about at least one of the following: the number of layers, the type of layer (e.g., convolutional layer, activation layer, dense layer, normalization layer, pooling layer, attention layer), layer information, time series specific parameters (e.g., bidirectionality, time step), parameters for training (e.g., type of function (e.g., L2 regularization, dropout function, etc.), where (e.g., after which layer) to place this function).

[0392] The layer information may include information about at least one of the following: Number of neurons in each layer; Kernel size; Stride for pooling / convolutional layers; Pooling method (MaxPooling, AveragePooling, etc.); Residual block information; Number of heads; Normalization method (Batch normalization, instance normalization, layer normalization, etc.); Activation function (Sigmoid, tanh function, ReLU, leaky ReLU information, Maxout, Softmax).

[0393] An AI model may be included as a component of another AI model, for example, an AI model that includes model component #1, ResNet, model component #2, a Transformer model, a dense layer, and a normalization layer in that order.

[0394] The training information for the AI ​​model may include information about at least one of the following: - Information for the optimization algorithm (e.g., type of optimization (Stochastic Gradient Descent (SGD)), AdaGrad, Adam, etc.), parameters of the optimization (learning rate, momentum information, etc.); - Information on the loss function (e.g., information on metrics of the loss function (Mean Absolute Error (MAE)), Mean Square Error (MSE), Cross Entropy Loss, NLL Loss, Kullback-Leibler (KL) Divergence, etc.)); - Parameters to be frozen for training (e.g., layers, weights); - Parameters to be updated (e.g., layers, weights); - Parameters to be (used as) initial parameters for training (e.g., layers, weights); - Method of training / updating the AI ​​model (e.g., (recommended) number of epochs, batch size, number of data to use for training).

[0395] The inference information for the AI ​​model may include information regarding decision tree branch pruning, parameter quantization, and functions of the AI ​​model, etc. Here, the functions of the AI ​​model may correspond to at least one of, for example, time domain beam prediction, spatial domain beam prediction, an autoencoder for CSI feedback, and an autoencoder for beam management.

[0396] An autoencoder for CSI feedback may be used as follows: The UE inputs the CSI / channel matrix / precoding matrix into the AI ​​model of the encoder and transmits the encoded bits as CSI feedback (CSI report). The BS inputs the received encoded bits into the AI ​​model of the decoder to reconstruct the CSI / channel matrix / precoding matrix, which is the output.

[0397] In spatial domain beam prediction, the UE / BS may input measurement results (beam quality, e.g., RSRP) based on sparse (or thick) beams into an AI model and output dense (or thin) beam quality.

[0398] In time domain beam prediction, the UE / BS may input time series (past, present, etc.) measurement results (beam quality, e.g., RSRP) into an AI model and output future beam quality.

[0399] The performance information regarding the AI ​​model may include information regarding the expected value of a loss function defined for the AI ​​model.

[0400] The AI ​​model information in the present disclosure may include information regarding the application range (applicable range) of the AI ​​model. The application range may be indicated by a physical cell ID, a serving cell index, etc. The information regarding the application range may be included in the above-mentioned environment information.

[0401] AI model information regarding a specific AI model may be predetermined in a standard or may be notified to a UE from a network (NW). An AI model defined in a standard may be referred to as a reference AI model. AI model information regarding a reference AI model may be referred to as reference AI model information.

[0402] Note that the AI ​​model information in the present disclosure may include an index for identifying the AI ​​model (which may be referred to as, for example, an AI model index, an AI model ID, a model ID, etc.). The AI ​​model information in the present disclosure may include an AI model index in addition to / instead of the input / output information of the AI ​​model described above. The association between the AI ​​model index and the AI ​​model information (for example, input / output information of the AI ​​model) may be predetermined in a standard, or may be notified to the UE from the NW.

[0403] The AI ​​model information in the present disclosure may be associated with an AI model and may be referred to as AI model relevant information, simply relevant information, etc. The AI ​​model relevant information does not need to explicitly include information for identifying the AI ​​model. The AI ​​model relevant information may be information that includes only meta information, for example.

[0404] In the present disclosure, the model ID may be interchangeably read as an ID (model set ID) corresponding to a set of AI models. Furthermore, in the present disclosure, the model ID may be interchangeably read as a meta information ID. The meta information (or the meta information ID) may be associated with information about a beam (beam setting) as described above. For example, the meta information (or the meta information ID) may be used by the UE to select an AI model taking into account which beam the BS is using, or may be used to notify the BS of which beam to use to apply the AI ​​model deployed by the UE. Furthermore, in the present disclosure, the meta information ID may be interchangeably read as an ID (meta information set ID) corresponding to a set of meta information.

[0405] [Supplementary Note 2: Notification of Information to UE] In the above-described embodiments, any information may be notified (from the NW) to the UE (in other words, reception of any information from the BS by the UE) using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel (e.g., PDCCH, PDSCH, reference signal), or a combination thereof.

[0406] When the notification is performed by a MAC CE, the MAC CE may be identified by including a new Logical Channel ID (LCID) in the MAC subheader, which is not defined in existing standards.

[0407] When the notification is made by DCI, the notification may be made by a specific field of the DCI, a Radio Network Temporary Identifier (RNTI) used to scramble Cyclic Redundancy Check (CRC) bits assigned to the DCI, the format of the DCI, etc.

[0408] Furthermore, notification of any information to the UE in the above embodiments may be performed periodically, semi-persistently, or aperiodically.

[0409] [Supplementary Note 3: Notification of Information from UE] In the above-described embodiments, notification of any information from the UE (to the NW) (in other words, transmission / report of any information from the UE to the BS) may be performed using physical layer signaling (e.g., UCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel (e.g., PUCCH, PUSCH, reference signal), or a combination thereof.

[0410] When the notification is performed by a MAC CE, the MAC CE may be identified by including a new LCID, which is not defined in existing standards, in the MAC subheader.

[0411] If the notification is made by UCI, the notification may be transmitted using PUCCH or PUSCH.

[0412] Furthermore, any information in the above-described embodiments may be notified from the UE periodically, semi-persistently, or aperiodically.

[0413] [Supplementary Note 4] In this disclosure, functionality may refer to a set of parameters (e.g., a set of parameters for CSI prediction / beam prediction / CSI compression) that can be supported based on conditions indicated by UE capabilities.

[0414] The UE may report parameter values ​​related to the functionality / model as conditions to the NW via higher layer signaling (e.g., RRC, MAC CE) / physical layer signaling (e.g., DCI). For example, the UE may report the conditions using UE capability / feature / feature group reporting.

[0415] The UE may report or instruct the parameter values ​​related to the functionality / model as additional conditions to the NW using higher layer signaling / physical layer signaling or methods other than signaling via the air interface of the NW (e.g., operator's configurations, pre-configured messages, etc.).

[0416] The UE may report or be instructed on information / instructions about the parameters corresponding to these additional conditions (e.g., parameter names) as information / instructions about the additional conditions using higher layer signaling / physical layer signaling or methods other than signaling via the air interface of the network (e.g., operator's configurations, pre-configured messages, etc.).

[0417] For example, the UE may report a device ID, a vendor ID, etc. as additional information. The UE may also be notified of a cell ID as an additional condition. The UE may also report or be instructed to report information such as a cell ID / UE ID instead of a parameter name.

[0418] Methods other than signaling via the air interface of the network may be methods related to pre-configuration of the UE (for example, configuration by the UE vendor) or operator configuration provided by the network operator.

[0419] [Application of Each Embodiment] At least one of the above-described embodiments may be applied when a specific condition is met. The specific condition may be defined in a standard or may be notified to a UE / BS using higher layer signaling / physical layer signaling.

[0420] At least one of the above embodiments may be applied only to UEs that have reported or support certain UE capabilities, for example (by way of example only): Supporting functionality-based LCM; Supporting model ID-based LCM; Supporting model training within a certain duration; Time required to train / update a model; Number of data samples (number of measurements / measurement result samples) required to train / update a model.

[0421] The particular UE capability may indicate support for particular processes / operations / controls / information for at least one of the above embodiments / options / choices.

[0422] Furthermore, the above-mentioned specific UE capability may be a capability that is applied across all frequencies (commonly regardless of frequency), or may be a capability for each frequency (e.g., one or a combination of a cell, band, band combination, BWP, component carrier, etc.), or may be a capability for each frequency range (e.g., Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or may be a capability for each subcarrier spacing (SubCarrier Spacing (SCS)), or may be a capability for each Feature Set (FS) or Feature Set Per Component-carrier (FSPC) (CSI prediction / CSI compression / time beam prediction / spatial domain beam prediction / AI-based positioning, etc.).

[0423] Furthermore, the specific UE capability may be a capability that is applied to all duplexing methods (commonly regardless of the duplexing method), or may be a capability for each duplexing method (e.g., Time Division Duplex (TDD) or Frequency Division Duplex (FDD)).

[0424] Furthermore, at least one of the above-described embodiments may be applied when the UE configures / activates / triggers specific information related to the above-described embodiments (or performs the operations of the above-described embodiments) through higher layer signaling / physical layer signaling. For example, the specific information may be information indicating that LCM based on a model / functionality ID is enabled, any RRC parameter for a specific release (e.g., Rel. 18 / 19), etc.

[0425] If the UE does not support at least one of the specific UE capabilities or is not configured with the specific information, the UE may apply, for example, the behavior of Rel. 15 / 16 / 17.

[0426] (Supplementary Notes) The following inventions are supplemented with respect to one embodiment (0th / 1st embodiment) of the present disclosure. [Supplementary Note 1] A terminal comprising: a receiving unit that receives information regarding an ID of a model or functionality; and a control unit that controls pairing for two-sided models based on the information, wherein the control unit checks whether a model available on the terminal side is a model available on the base station side based on the information. [Supplementary Note 2] The terminal according to Supplementary Note 1, wherein the control unit controls the pairing according to at least one of a scheme in which an ID is shared between the terminal side and the base station side, a scheme in which a part of an ID is used, and a scheme in which an ID common to the terminal side and the base station side is used. [Supplementary Note 3] The terminal according to Supplementary Note 1 or Supplementary Note 2, wherein the control unit controls reporting of an available ID or reporting of an unavailable ID based on a result of the check. [Supplementary Note 4] The terminal according to any one of Supplementary Notes 1 to 3, wherein the control unit checks the availability of a model based on at least one of a received ID, pairing information including a list of IDs available for pairing, and a part of IDs available on the terminal side.

[0427] (Supplementary Notes) The following inventions are supplemented with respect to one embodiment (0th / 2nd embodiment) of the present disclosure. [Supplementary Note 1] A terminal comprising: a receiving unit that receives at least one information of a dataset, a condition, and an additional condition related to the pairing of models or functionalities; and a control unit that controls pairing for two-sided models based on the information, wherein the control unit determines whether pairing of the models or functionalities is possible on the terminal side and the base station side based on the information. [Supplementary Note 2] The terminal described in Supplementary Note 1, wherein the control unit controls the pairing according to at least one of a scheme that evaluates performance of a model generated or reconstructed based on the dataset, and a scheme based on the condition or the additional condition. [Supplementary Note 3] The terminal described in Supplementary Note 1 or Supplementary Note 2, wherein the control unit controls transmission and reception of at least one of a reporting of an additional condition including information about a specific ID, a reporting of a list of the dataset, an instruction corresponding to a specific entry in the reported list, and an instruction indicating a pairing failure. [Supplementary Note 4] The terminal described in any of Supplements 1 to 3, wherein the control unit calculates a corresponding key performance indicator (KPI) based on the received list of datasets.

[0428] (Wireless Communication System) The configuration of a wireless communication system according to an embodiment of the present disclosure will be described below. In this wireless communication system, communication is performed using any one of the wireless communication methods according to the above embodiments of the present disclosure or a combination thereof.

[0429] 18 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment. The wireless communication system 1 (which may be simply referred to as system 1) may be a system that realizes communication using Long Term Evolution (LTE) or 5th generation mobile communication system New Radio (5G NR) specified by the Third Generation Partnership Project (3GPP).

[0430] The wireless communication system 1 may also support dual connectivity between multiple Radio Access Technologies (RATs) (Multi-RAT Dual Connectivity (MR-DC)). MR-DC may include dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (EN-DC)), dual connectivity between NR and LTE (NR-E-UTRA Dual Connectivity (NE-DC)), etc.

[0431] In EN-DC, the LTE (E-UTRA) base station (eNB) is the master node (Master Node (MN)), and the NR base station (gNB) is the secondary node (Secondary Node (SN)). In NE-DC, the NR base station (gNB) is the MN, and the LTE (E-UTRA) base station (eNB) is the SN.

[0432] The wireless communication system 1 may support dual connectivity between multiple base stations within the same RAT (for example, dual connectivity in which both the MN and SN are NR base stations (gNBs) (NR-NR Dual Connectivity (NN-DC))).

[0433] The wireless communication system 1 may include a base station 11 that forms a macrocell C1 with a relatively wide coverage, and base stations 12 (12a-12c) that are located within the macrocell C1 and form small cells C2 that are smaller than the macrocell C1. A user terminal 20 may be located within at least one of the cells. The locations and numbers of the cells and user terminals 20 are not limited to the embodiment shown in the figure. Hereinafter, when there is no need to distinguish between the base stations 11 and 12, they will be collectively referred to as base station 10.

[0434] The user terminal 20 may be connected to at least one of the multiple base stations 10. The user terminal 20 may utilize at least one of carrier aggregation (CA) using multiple component carriers (CCs) and dual connectivity (DC).

[0435] Each CC may be included in at least one of a first frequency band (Frequency Range 1 (FR1)) and a second frequency band (Frequency Range 2 (FR2)). The macro cell C1 may be included in FR1, and the small cell C2 may be included in FR2. For example, FR1 may be a frequency band of 6 GHz or less (sub-6 GHz), and FR2 may be a frequency band higher than 24 GHz (above-24 GHz). Note that the frequency bands and definitions of FR1 and FR2 are not limited to these, and for example, FR1 may correspond to a higher frequency band than FR2.

[0436] Furthermore, the user terminal 20 may perform communication using at least one of time division duplex (TDD) and frequency division duplex (FDD) in each CC.

[0437] The multiple base stations 10 may be connected by wire (e.g., optical fiber compliant with the Common Public Radio Interface (CPRI), an X2 interface, etc.) or wirelessly (e.g., NR communication). For example, when NR communication is used as a backhaul between the base stations 11 and 12, the base station 11 corresponding to the upper station may be called an Integrated Access Backhaul (IAB) donor, and the base station 12 corresponding to the relay station (relay) may be called an IAB node.

[0438] The base station 10 may be connected to the core network 30 directly or via another base station 10. The core network 30 may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), a Next Generation Core (NGC), and the like.

[0439] The core network 30 may include network functions (Network Functions (NF)) such as a User Plane Function (UPF), an Access and Mobility management Function (AMF), a Session Management Function (SMF), a Unified Data Management (UDM), an Application Function (AF), a Data Network (DN), a Location Management Function (LMF), and Operation, Administration and Maintenance (Management) (OAM). A single network node may provide multiple functions. Communication with an external network (e.g., the Internet) may also be performed via the DN.

[0440] The user terminal 20 may be a terminal that supports at least one of communication methods such as LTE, LTE-A, and 5G.

[0441] An Orthogonal Frequency Division Multiplexing (OFDM)-based radio access scheme may be used in the wireless communication system 1. For example, Cyclic Prefix OFDM (CP-OFDM), Discrete Fourier Transform Spread OFDM (DFT-s-OFDM), Orthogonal Frequency Division Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-FDMA), or the like may be used in at least one of the downlink (DL) and uplink (UL).

[0442] The radio access scheme may also be called a waveform. Note that in the wireless communication system 1, other radio access schemes (e.g., other single-carrier transmission schemes, other multi-carrier transmission schemes) may be used as the UL and DL radio access schemes.

[0443] In the wireless communication system 1, a downlink shared channel (Physical Downlink Shared Channel (PDSCH)) shared by each user terminal 20, a broadcast channel (Physical Broadcast Channel (PBCH)), a downlink control channel (Physical Downlink Control Channel (PDCCH)), etc. may be used as the downlink channel.

[0444] Furthermore, in the wireless communication system 1, an uplink shared channel (Physical Uplink Shared Channel (PUSCH)) shared by each user terminal 20, an uplink control channel (Physical Uplink Control Channel (PUCCH)), a random access channel (Physical Random Access Channel (PRACH)), or the like may be used as an uplink channel.

[0445] The PDSCH transmits user data, higher layer control information, a System Information Block (SIB), etc. The PUSCH may transmit user data, higher layer control information, etc. Furthermore, the PBCH may transmit a Master Information Block (MIB).

[0446] Lower layer control information may be transmitted by the PDCCH. The lower layer control information may include, for example, Downlink Control Information (DCI) including scheduling information for at least one of the PDSCH and the PUSCH.

[0447] Note that the DCI for scheduling the PDSCH may be referred to as a DL assignment, a DL DCI, etc., and the DCI for scheduling the PUSCH may be referred to as a UL grant, a UL DCI, etc. Note that the PDSCH may be replaced with DL data, and the PUSCH may be replaced with UL data.

[0448] A control resource set (CORESET) and a search space may be used to detect the PDCCH. The CORESET corresponds to resources for searching for DCI. The search space corresponds to a search region and a search method for PDCCH candidates. One CORESET may be associated with one or more search spaces. The UE may monitor the CORESET associated with a certain search space based on the search space configuration.

[0449] One search space may correspond to PDCCH candidates corresponding to one or more aggregation levels. One or more search spaces may be referred to as a search space set. Note that the terms "search space," "search space set," "search space configuration," "search space set configuration," "CORESET," "CORESET configuration," and the like in the present disclosure may be read interchangeably.

[0450] The PUCCH may transmit uplink control information (UCI) including at least one of channel state information (CSI), delivery confirmation information (which may be called, for example, Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), ACK / NACK, etc.), and scheduling request (SR). The PRACH may transmit a random access preamble for establishing a connection with a cell.

[0451] In the present disclosure, downlink, uplink, etc. may be expressed without adding "link." Also, various channels may be expressed without adding "Physical" to the beginning.

[0452] In the wireless communication system 1, a synchronization signal (SS), a downlink reference signal (DL-RS), etc. may be transmitted. In the wireless communication system 1, as the DL-RS, a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS), a demodulation reference signal (DMRS), a positioning reference signal (PRS), a phase tracking reference signal (PTRS), etc. may be transmitted.

[0453] The synchronization signal may be, for example, at least one of a Primary Synchronization Signal (PSS) and a Secondary Synchronization Signal (SSS). A signal block including an SS (PSS, SSS) and a PBCH (and a DMRS for the PBCH) may be referred to as an SS / PBCH block, an SS Block (SSB), or the like. Note that the SS, SSB, and the like may also be referred to as a reference signal.

[0454] Furthermore, in the wireless communication system 1, a sounding reference signal (SRS), a demodulation reference signal (DMRS), or the like may be transmitted as an uplink reference signal (UL-RS). Note that the DMRS may also be called a user equipment-specific reference signal (UE-specific reference signal).

[0455] 19 is a diagram showing an example of the configuration of a base station according to an embodiment. The base station 10 includes a control unit 110, a transceiver unit 120, a transceiver antenna 130, and a transmission line interface 140. Note that the base station may include one or more of each of the control unit 110, the transceiver unit 120, the transceiver antenna 130, and the transmission line interface 140.

[0456] In this example, the functional blocks of the characteristic parts of the present embodiment are mainly shown, and it may be assumed that the base station 10 also has other functional blocks necessary for wireless communication. Some of the processing of each unit described below may be omitted.

[0457] The control unit 110 performs overall control of the base station 10. The control unit 110 can be configured from a controller, a control circuit, and the like that are explained based on common understanding in the technical field to which the present disclosure relates.

[0458] The control unit 110 may control signal generation, scheduling (e.g., resource allocation, mapping), etc. The control unit 110 may control transmission and reception using the transceiver unit 120, the transceiver antenna 130, and the transmission path interface 140, measurement, etc. The control unit 110 may generate data, control information, sequences, etc. to be transmitted as signals, and transfer them to the transceiver unit 120. The control unit 110 may perform call processing (setting up, releasing, etc.) of communication channels, status management of the base station 10, management of radio resources, etc.

[0459] The transceiver unit 120 may include a baseband unit 121, a radio frequency (RF) unit 122, and a measurement unit 123. The baseband unit 121 may include a transmission processing unit 1211 and a reception processing unit 1212. The transceiver unit 120 may be configured with a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transceiver circuit, etc., which are described based on common understanding in the technical field related to the present disclosure.

[0460] The transmitting / receiving unit 120 may be configured as an integrated transmitting / receiving unit, or may be configured from a transmitting unit and a receiving unit. The transmitting unit may be configured from a transmission processing unit 1211 and an RF unit 122. The receiving unit may be configured from a reception processing unit 1212, the RF unit 122, and a measurement unit 123.

[0461] The transmitting and receiving antenna 130 can be configured from an antenna described based on common understanding in the technical field to which the present disclosure relates, such as an array antenna.

[0462] The transceiver 120 may transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transceiver 120 may receive the above-mentioned uplink channel, uplink reference signal, etc.

[0463] The transceiver 120 may form at least one of the transmit beam and the receive beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.

[0464] The transmitter / receiver unit 120 (transmission processing unit 1211) may perform Packet Data Convergence Protocol (PDCP) layer processing, Radio Link Control (RLC) layer processing (e.g., RLC retransmission control), Medium Access Control (MAC) layer processing (e.g., HARQ retransmission control), etc. on data, control information, etc. obtained from the control unit 110, and generate a bit string to be transmitted.

[0465] The transmitter / receiver unit 120 (transmission processing unit 1211) may perform transmission processing such as channel coding (which may include error correction coding), modulation, mapping, filtering, Discrete Fourier Transform (DFT) processing (if necessary), Inverse Fast Fourier Transform (IFFT) processing, precoding, and digital-to-analog conversion on the bit string to be transmitted, and output a baseband signal.

[0466] The transceiver unit 120 (RF unit 122) may perform modulation, filtering, amplification, etc. on the baseband signal to a radio frequency band, and transmit the radio frequency band signal via the transceiver antenna 130.

[0467] On the other hand, the transceiver unit 120 (RF unit 122) may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency band signal received by the transceiver antenna 130.

[0468] The transceiver 120 (reception processing unit 1212) may apply reception processing such as analog-to-digital conversion, Fast Fourier Transform (FFT) processing, Inverse Discrete Fourier Transform (IDFT) processing (if necessary), filtering, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal, thereby acquiring user data, etc.

[0469] The transceiver 120 (measurement unit 123) may perform measurements on the received signal. For example, the measurement unit 123 may perform Radio Resource Management (RRM) measurements, Channel State Information (CSI) measurements, etc. based on the received signal. The measurement unit 123 may measure received power (e.g., Reference Signal Received Power (RSRP)), received quality (e.g., Reference Signal Received Quality (RSRQ), Signal to Interference plus Noise Ratio (SINR), Signal to Noise Ratio (SNR)), signal strength (e.g., Received Signal Strength Indicator (RSSI)), propagation path information (e.g., CSI), etc. The measurement results may be output to the control unit 110.

[0470] The transmission path interface 140 may transmit and receive signals (backhaul signaling) between devices included in the core network 30 (e.g., network nodes that provide NF), other base stations 10, etc., and may acquire and transmit user data (user plane data), control plane data, etc. for the user terminal 20.

[0471] The transmitting section and receiving section of the base station 10 in the present disclosure may be configured by at least one of the transmitting / receiving section 120, the transmitting / receiving antenna 130, and the transmission path interface 140.

[0472] The transceiver 120 may transmit and receive information related to the ID of a model or functionality. The controller 110 may control pairing of the two-sided models based on the information. The controller 110 may check whether a model available on the terminal side is a model available on the base station side based on the information.

[0473] The transceiver 120 may transmit and receive information on at least one of a data set, a condition, and an additional condition related to pairing of models or functionality. The controller 110 may control pairing of the two-sided models based on the information. The controller 110 may determine whether pairing of the models or functionality is possible between the terminal side and the base station side based on the information.

[0474] (User Terminal) Fig. 20 is a diagram showing an example of the configuration of a user terminal according to one embodiment. The user terminal 20 includes a control unit 210, a transceiver unit 220, and a transceiver antenna 230. Note that the user terminal 20 may include one or more of each of the control unit 210, the transceiver unit 220, and the transceiver antenna 230.

[0475] In this example, the functional blocks of the characteristic parts of the present embodiment are mainly shown, and it may be assumed that the user terminal 20 also has other functional blocks necessary for wireless communication. Some of the processing of each unit described below may be omitted.

[0476] The control unit 210 performs overall control of the user terminal 20. The control unit 210 can be configured from a controller, a control circuit, etc., which are described based on common understanding in the technical field to which the present disclosure relates.

[0477] The control unit 210 may control signal generation, mapping, etc. The control unit 210 may control transmission and reception, measurement, etc. using the transceiver unit 220 and the transceiver antenna 230. The control unit 210 may generate data, control information, sequences, etc. to be transmitted as signals and transfer them to the transceiver unit 220.

[0478] The transceiver unit 220 may include a baseband unit 221, an RF unit 222, and a measurement unit 223. The baseband unit 221 may include a transmission processing unit 2211 and a reception processing unit 2212. The transceiver unit 220 may be configured with a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transceiver circuit, etc., which are described based on common understanding in the technical field related to the present disclosure.

[0479] The transmitting / receiving unit 220 may be configured as an integrated transmitting / receiving unit, or may be composed of a transmitting unit and a receiving unit. The transmitting unit may be composed of a transmission processing unit 2211 and an RF unit 222. The receiving unit may be composed of a reception processing unit 2212, an RF unit 222, and a measurement unit 223.

[0480] The transmitting / receiving antenna 230 can be configured from an antenna described based on common understanding in the technical field to which the present disclosure relates, such as an array antenna.

[0481] The transceiver 220 may receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transceiver 220 may transmit the above-mentioned uplink channel, uplink reference signal, etc.

[0482] The transceiver unit 220 may form at least one of the transmit beam and the receive beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.

[0483] The transceiver unit 220 (transmission processing unit 2211) may perform PDCP layer processing, RLC layer processing (e.g., RLC retransmission control), MAC layer processing (e.g., HARQ retransmission control), etc. on data, control information, etc. obtained from the control unit 210, and generate a bit string to be transmitted.

[0484] The transmitter / receiver unit 220 (transmission processing unit 2211) may perform transmission processing such as channel coding (which may include error correction coding), modulation, mapping, filtering, DFT processing (if necessary), IFFT processing, precoding, and digital-to-analog conversion on the bit string to be transmitted, and output a baseband signal.

[0485] Whether or not to apply DFT processing may be based on the setting of transform precoding. When transform precoding is enabled for a certain channel (e.g., PUSCH), the transceiver unit 220 (transmission processing unit 2211) may perform DFT processing as the transmission processing to transmit the channel using a DFT-s-OFDM waveform, and if not, it may not be necessary to perform DFT processing as the transmission processing.

[0486] The transceiver unit 220 (RF unit 222) may perform modulation, filtering, amplification, etc. on the baseband signal to a radio frequency band, and transmit the radio frequency band signal via the transceiver antenna 230.

[0487] On the other hand, the transceiver unit 220 (RF unit 222) may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency band signal received by the transceiver antenna 230.

[0488] The transceiver unit 220 (reception processing unit 2212) may apply reception processing such as analog-to-digital conversion, FFT processing, IDFT processing (if necessary), filtering, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal, and acquire user data, etc.

[0489] The transceiver 220 (measurement unit 223) may perform measurements on the received signal. For example, the measurement unit 223 may perform RRM measurements, CSI measurements, etc. based on the received signal. The measurement unit 223 may measure received power (e.g., RSRP), received quality (e.g., RSRQ, SINR, SNR), signal strength (e.g., RSSI), propagation path information (e.g., CSI), etc. The measurement results may be output to the control unit 210.

[0490] The measurement unit 223 may derive channel measurements for CSI calculation based on the channel measurement resources. The channel measurement resources may be, for example, non-zero power (NZP) CSI-RS resources. The measurement unit 223 may also derive interference measurements for CSI calculation based on the interference measurement resources. The interference measurement resources may be at least one of an NZP CSI-RS resource for interference measurement, a CSI-Interference Measurement (IM) resource, etc. Note that CSI-IM may be referred to as CSI-Interference Management (IM) or may be interchangeably read as Zero Power (ZP) CSI-RS. Note that in the present disclosure, CSI-RS, NZP CSI-RS, ZP CSI-RS, CSI-IM, CSI-SSB, etc. may be interchangeably read as interchangeable.

[0491] The transmitting unit and receiving unit of the user terminal 20 in the present disclosure may be configured by at least one of the transmitting / receiving unit 220 and the transmitting / receiving antenna 230.

[0492] The transceiver 220 may transmit and receive information regarding the ID of a model or functionality. The controller 210 may control pairing for both-side models based on the information. The controller 210 may check whether a model available on the terminal side is also available on the base station side based on the information. The controller 210 may control the pairing according to at least one of a scheme in which an ID is shared between the terminal side and the base station side, a scheme in which a portion of an ID is used, and a scheme in which a common ID is used between the terminal side and the base station side. The controller 210 may control reporting of available IDs or reporting of unavailable IDs based on the result of the check. The controller 210 may check the availability of a model based on at least one of the received ID, pairing information including a list of IDs available for pairing, and a portion of IDs available on the terminal side.

[0493] The transceiver 210 may transmit and receive information on at least one of a dataset, conditions, and additional conditions related to the pairing of models or functionality. The controller 210 may control pairing for a two-sided model based on the information. The controller 210 may determine whether pairing of the models or functionality is possible on the terminal side and the base station side based on the information. The controller 210 may control the pairing according to at least one of a scheme for evaluating performance of a model generated or reconstructed based on the dataset, a scheme based on the conditions, or the additional conditions. The controller 210 may control transmission and reception of at least one of a report of additional conditions including information about a specific ID, a report of a list of the datasets, an indication corresponding to a specific entry in the reported list, and an indication indicating a pairing failure. The controller 210 may calculate a corresponding key performance indicator (KPI) based on the received list of datasets.

[0494] (Hardware Configuration) Note that the block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wires, wirelessly, etc.) and these multiple devices. The functional block may be realized by combining software with the single device or the multiple devices.

[0495] Here, the functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, deeming, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission may be called a transmitting unit, transmitter, etc. As described above, the implementation method of each is not particularly limited.

[0496] For example, a base station, a user terminal, etc. according to an embodiment of the present disclosure may function as a computer that performs processing of the wireless communication method of the present disclosure. Fig. 21 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment. The above-described base station 10 and user terminal 20 may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0497] In the present disclosure, the terms apparatus, circuit, device, section, unit, etc. may be used interchangeably. The hardware configurations of the base station 10 and the user terminal 20 may be configured to include one or more of the devices shown in the drawings, or may be configured to exclude some of the devices.

[0498] For example, although only one processor 1001 is shown, there may be multiple processors. Furthermore, processing may be performed by one processor, or processing may be performed by two or more processors simultaneously, serially, or in other ways. Furthermore, processor 1001 may be implemented by one or more chips.

[0499] Each function in the base station 10 and the user terminal 20 is realized, for example, by loading specified software (programs) onto hardware such as a processor 1001 and a memory 1002, causing the processor 1001 to perform calculations, control communication via the communication device 1004, and control at least one of reading and writing data in the memory 1002 and the storage 1003.

[0500] The processor 1001, for example, runs an operating system to control the entire computer. The processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, at least a part of the above-mentioned control unit 110 (210), transceiver unit 120 (220), etc. may be realized by the processor 1001.

[0501] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the control unit 110 (210) may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and the other functional blocks may be implemented in a similar manner.

[0502] The memory 1002 is a computer-readable recording medium and may be configured by at least one of, for example, Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically EEPROM (EEPROM), Random Access Memory (RAM), or other suitable storage medium. The memory 1002 may also be referred to as a register, cache, main memory, etc. The memory 1002 may store executable programs (program codes), software modules, etc. for implementing a wireless communication method according to an embodiment of the present disclosure.

[0503] Storage 1003 is a computer-readable recording medium and may be composed of at least one of, for example, a flexible disk, a floppy disk, a magneto-optical disk (e.g., a compact disc (e.g., a Compact Disc ROM (CD-ROM)), a digital versatile disc, a Blu-ray disc), a removable disk, a hard disk drive, a smart card, a flash memory device (e.g., a card, a stick, a key drive), a magnetic stripe, a database, a server, or other suitable storage medium. Storage 1003 may also be referred to as an auxiliary storage device.

[0504] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the above-mentioned transmission / reception unit 120 (220), transmission / reception antenna 130 (230), etc. may be realized by the communication device 1004. The transmission / reception unit 120 (220) may be implemented as a transmission unit 120a (220a) and a reception unit 120b (220b) that are physically or logically separated.

[0505] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, a light emitting diode (LED) lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0506] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0507] Furthermore, the base station 10 and the user terminal 20 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized using this hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0508] (Modifications) Note that terms described in the present disclosure and terms necessary for understanding the present disclosure may be replaced with terms having the same or similar meanings. For example, a channel, a symbol, and a signal (signal or signaling) may be interchangeable. A signal may also be a message. A reference signal may be abbreviated as RS, and may also be called a pilot, pilot signal, etc. depending on the applicable standard. A component carrier (CC) may also be called a cell, frequency carrier, carrier frequency, etc.

[0509] A radio frame may be composed of one or more periods (frames) in the time domain. Each of the one or more periods (frames) constituting a radio frame may be called a subframe. Furthermore, a subframe may be composed of one or more slots in the time domain. A subframe may have a fixed time length (e.g., 1 ms) that is independent of numerology.

[0510] Here, the numerology may be a communication parameter applied to at least one of transmission and reception of a signal or channel, and may indicate at least one of, for example, Subcarrier Spacing (SCS), bandwidth, symbol length, cyclic prefix length, Transmission Time Interval (TTI), number of symbols per TTI, radio frame structure, specific filtering performed by a transceiver in the frequency domain, and specific windowing performed by a transceiver in the time domain.

[0511] A slot may be composed of one or more symbols (such as an Orthogonal Frequency Division Multiplexing (OFDM) symbol or a Single Carrier Frequency Division Multiple Access (SC-FDMA) symbol) in the time domain. A slot may also be a time unit based on numerology.

[0512] A slot may include multiple minislots. Each minislot may consist of one or multiple symbols in the time domain. A minislot may also be called a subslot. A minislot may consist of fewer symbols than a slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a minislot may be called PDSCH (PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using a minislot may be called PDSCH (PUSCH) mapping type B.

[0513] A radio frame, a subframe, a slot, a minislot, and a symbol all represent time units for transmitting signals. The radio frame, the subframe, the slot, the minislot, and the symbol may be referred to by other names corresponding to the radio frame, the subframe, the slot, the minislot, and the symbol. Note that the time units such as a frame, a subframe, a slot, a minislot, and a symbol in the present disclosure may be interchangeable.

[0514] For example, one subframe may be referred to as a TTI, or multiple consecutive subframes may be referred to as a TTI, or one slot or one minislot may be referred to as a TTI. That is, at least one of the subframe and the TTI may be a subframe (1 ms) in existing LTE, a period shorter than 1 ms (for example, 1-13 symbols), or a period longer than 1 ms. Note that the unit representing the TTI may be called a slot, minislot, etc. instead of a subframe.

[0515] Here, TTI refers to, for example, the smallest time unit for scheduling in wireless communication. For example, in an LTE system, a base station performs scheduling to allocate radio resources (such as frequency bandwidth and transmission power that can be used by each user terminal) to each user terminal in TTI units. Note that the definition of TTI is not limited to this.

[0516] The TTI may be a transmission time unit for a channel-encoded data packet (transport block), a code block, a code word, etc., or may be a processing unit for scheduling, link adaptation, etc. When a TTI is given, the time interval (e.g., the number of symbols) to which a transport block, a code block, a code word, etc. is actually mapped may be shorter than the TTI.

[0517] When one slot or one minislot is called a TTI, one or more TTIs (i.e., one or more slots or one or more minislots) may be the minimum time unit for scheduling. Also, the number of slots (minislots) constituting the minimum time unit for scheduling may be controlled.

[0518] A TTI having a time length of 1 ms may be called a regular TTI (TTI in 3GPP Rel. 8-12), normal TTI, long TTI, regular subframe, normal subframe, long subframe, slot, etc. A TTI shorter than a regular TTI may be called a shortened TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, minislot, subslot, slot, etc.

[0519] In addition, a long TTI (e.g., a normal TTI, a subframe, etc.) may be interpreted as a TTI having a time length of more than 1 ms, and a short TTI (e.g., a shortened TTI, etc.) may be interpreted as a TTI having a TTI length shorter than the TTI length of a long TTI and greater than or equal to 1 ms.

[0520] A resource block (RB) is a resource allocation unit in the time domain and the frequency domain, and may include one or more consecutive subcarriers in the frequency domain. The number of subcarriers included in an RB may be the same regardless of numerology, for example, 12. The number of subcarriers included in an RB may be determined based on numerology.

[0521] In addition, an RB may include one or more symbols in the time domain and may have a length of one slot, one minislot, one subframe, or one TTI, each of which may be composed of one or more resource blocks.

[0522] In addition, one or more RBs may be referred to as a physical resource block (PRB), a sub-carrier group (SCG), a resource element group (REG), a PRB pair, an RB pair, etc.

[0523] Furthermore, a resource block may be composed of one or more resource elements (REs). For example, one RE may be a radio resource region of one subcarrier and one symbol.

[0524] A Bandwidth Part (BWP), which may also be referred to as a partial bandwidth, may represent a subset of contiguous common resource blocks (RBs) for a given numerology on a given carrier, where the common RBs may be identified by their index relative to a Common Reference Point of the carrier. PRBs may be defined in a BWP and numbered within the BWP.

[0525] The BWP may include a UL BWP (BWP for UL) and a DL BWP (BWP for DL). One or more BWPs may be configured for a UE within one carrier.

[0526] At least one of the configured BWPs may be active, and the UE may not expect to transmit or receive a given signal / channel outside the active BWP. Note that the terms "cell," "carrier," etc. in this disclosure may be read as "BWP."

[0527] The above-described structures of radio frames, subframes, slots, minislots, symbols, etc. are merely examples. For example, the number of subframes included in a radio frame, the number of slots per subframe or radio frame, the number of minislots included in a slot, the number of symbols and RBs included in a slot or minislot, the number of subcarriers included in an RB, the number of symbols in a TTI, the symbol length, the cyclic prefix (CP) length, etc. may be changed in various ways.

[0528] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by a predetermined index.

[0529] The names used for parameters and the like in this disclosure are not intended to be limiting in any way. Furthermore, the mathematical expressions and the like using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0530] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0531] Furthermore, information, signals, etc. may be output from a higher layer to a lower layer and / or from a lower layer to a higher layer. Information, signals, etc. may be input / output via multiple network nodes.

[0532] Input and output information, signals, etc. may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information, signals, etc. may be overwritten, updated, or added. Output information, signals, etc. may be deleted. Input information, signals, etc. may be transmitted to another device.

[0533] The notification of information is not limited to the aspects / embodiments described in the present disclosure, and may be performed using other methods. For example, the notification of information in the present disclosure may be performed by physical layer signaling (e.g., Downlink Control Information (DCI) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB) and System Information Block (SIB)), Medium Access Control (MAC) signaling), other signals, or a combination thereof.

[0534] Note that the physical layer signaling may be referred to as Layer 1 / Layer 2 (L1 / L2) control information (L1 / L2 control signal), L1 control information (L1 control signal), etc. Furthermore, the RRC signaling may be referred to as an RRC message, such as an RRC Connection Setup message or an RRC Connection Reconfiguration message. Furthermore, the MAC signaling may be notified using, for example, a MAC Control Element (CE).

[0535] Furthermore, notification of specified information (e.g., notification that "it is X") is not limited to explicit notification, but may be made implicitly (e.g., by not notifying the specified information or by notifying other information).

[0536] The determination may be made by a value represented by one bit (0 or 1), by a Boolean value represented by true or false, or by a comparison of numerical values ​​(e.g., comparison with a predetermined value).

[0537] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0538] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), these wired and / or wireless technologies are included within the definition of transmission media.

[0539] As used in this disclosure, the terms "system" and "network" may be used interchangeably. A "network" may refer to devices included in the network (e.g., base stations).

[0540] In this disclosure, terms such as "precoding," "precoder," "weight (precoding weight)," "Quasi-Co-Location (QCL)," "Transmission Configuration Indication state (TCI state)," "spatial relation," "spatial domain filter," "transmit power," "phase rotation," "antenna port," "layer," "number of layers," "rank," "resource," "resource set," "beam," "beam width," "beam angle," "antenna," "antenna element," "panel," "UE panel," "transmitting entity," "receiving entity," etc. may be used interchangeably.

[0541] In the present disclosure, the term "antenna port" may be interchangeably read as an antenna port for any signal / channel (e.g., a demodulation reference signal (DMRS) port). In the present disclosure, the term "resource" may be interchangeably read as a resource for any signal / channel (e.g., a reference signal resource, an SRS resource, etc.). The resource may include time / frequency / code / space / power resources. Furthermore, the spatial domain transmission filter may include at least one of a spatial domain transmission filter and a spatial domain reception filter.

[0542] The group may include, for example, at least one of a spatial relationship group, a Code Division Multiplexing (CDM) group, a Reference Signal (RS) group, a Control Resource Set (CORESET) group, a PUCCH group, an antenna port group (e.g., a DMRS port group), a layer group, a resource group, a beam group, an antenna group, a panel group, and the like.

[0543] In addition, in the present disclosure, beam, SRS Resource Indicator (SRI), CORESET, CORESET pool, PDSCH, PUSCH, codeword (CW), transport block (TB), RS, etc. may be read as interchangeable terms.

[0544] In addition, in the present disclosure, the terms TCI state, downlink TCI state (DL TCI state), uplink TCI state (UL TCI state), unified TCI state, common TCI state, joint TCI state, etc. may be read interchangeably.

[0545] Furthermore, in the present disclosure, terms such as "QCL," "QCL assumption," "QCL relationship," "QCL type information," "QCL property / properties," "specific QCL type (e.g., Type A, Type D) property," and "specific QCL type (e.g., Type A, Type D)" may be interchangeable.

[0546] In the present disclosure, terms such as index, identifier (ID), indicator, indication, and resource ID may be interchangeable. In the present disclosure, terms such as sequence, list, set, group, cluster, and subset may be interchangeable.

[0547] Furthermore, the spatial relationship information identifier (ID) (TCI state ID) and the spatial relationship information (TCI state) may be interchangeable. The "spatial relationship information (TCI state)" may be interchangeable with "set of spatial relationship information (TCI state)", "one or more pieces of spatial relationship information", etc. The TCI state and the TCI may be interchangeable. The spatial relationship information and the spatial relationship may be interchangeable.

[0548] In the present disclosure, terms such as "base station (BS)," "radio base station," "fixed station," "NodeB," "eNB (eNodeB)," "gNB (gNodeB)," "access point," "transmission point (TP)," "reception point (RP)," "transmission / reception point (TRP)," "panel," "cell," "sector," "cell group," "carrier," "component carrier," etc. may be used interchangeably. Base stations may also be referred to by terms such as macrocell, small cell, femtocell, picocell, etc.

[0549] A base station can accommodate one or more (e.g., three) cells. When a base station accommodates multiple cells, the overall coverage area of ​​the base station can be partitioned into multiple smaller areas, and each smaller area can be provided with communication service by a base station subsystem (e.g., a small indoor base station (Remote Radio Head (RRH))). The terms "cell" or "sector" refer to part or all of the coverage area of ​​a base station and / or base station subsystem that provides communication service within that coverage.

[0550] In the present disclosure, a base station transmitting information to a terminal may be interpreted as the base station instructing the terminal to control / operate based on the information.

[0551] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," etc. may be used interchangeably.

[0552] A mobile station may also be referred to as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

[0553] At least one of the base station and the mobile station may be called a transmitting device, a receiving device, a wireless communication device, etc. Note that at least one of the base station and the mobile station may be a device mounted on a moving object, the moving object itself, etc.

[0554] The mobile body is a movable object that can move at any speed and naturally includes cases where the mobile body is stationary. Examples of the mobile body include, but are not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcars, rickshaws, ships and other watercraft, airplanes, rockets, satellites, drones, multicopters, quadcopters, balloons, and objects mounted thereon. The mobile body may also be a mobile body that moves autonomously based on an operation command.

[0555] The mobile object may be a vehicle (e.g., a car, an airplane, etc.), an unmanned mobile object (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned). Note that at least one of the base station and the mobile station may also include devices that do not necessarily move during communication operations. For example, at least one of the base station and the mobile station may be an Internet of Things (IoT) device such as a sensor.

[0556] 22 is a diagram showing an example of a vehicle according to an embodiment. The vehicle 40 includes a drive unit 41, a steering unit 42, an accelerator pedal 43, a brake pedal 44, a shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, an electronic control unit 49, various sensors (including a current sensor 50, an RPM sensor 51, an air pressure sensor 52, a vehicle speed sensor 53, an acceleration sensor 54, an accelerator pedal sensor 55, a brake pedal sensor 56, a shift lever sensor 57, and an object detection sensor 58), an information service unit 59, and a communication module 60.

[0557] The drive unit 41 is configured with at least one of an engine, a motor, and a hybrid of an engine and a motor, for example. The steering unit 42 includes at least a steering wheel (also called a handle) and is configured to steer at least one of the front wheels 46 and the rear wheels 47 based on the operation of the steering wheel operated by a user.

[0558] The electronic control unit 49 is composed of a microprocessor 61, memory (ROM, RAM) 62, and a communication port (for example, an input / output (IO) port) 63. Signals are input to the electronic control unit 49 from various sensors 50-58 provided in the vehicle. The electronic control unit 49 may also be called an Electronic Control Unit (ECU).

[0559] The signals from the various sensors 50-58 include a current signal from a current sensor 50 that senses the current of the motor, a rotation speed signal of the front wheels 46 / rear wheels 47 obtained by a rotation speed sensor 51, an air pressure signal of the front wheels 46 / rear wheels 47 obtained by an air pressure sensor 52, a vehicle speed signal obtained by a vehicle speed sensor 53, an acceleration signal obtained by an acceleration sensor 54, a depression amount signal of the accelerator pedal 43 obtained by an accelerator pedal sensor 55, a depression amount signal of the brake pedal 44 obtained by a brake pedal sensor 56, an operation signal of the shift lever 45 obtained by a shift lever sensor 57, and a detection signal for detecting obstacles, vehicles, pedestrians, etc. obtained by an object detection sensor 58.

[0560] The information service unit 59 is composed of various devices, such as a car navigation system, an audio system, speakers, a display, a television, and a radio, for providing (outputting) various information such as driving information, traffic information, and entertainment information, and one or more ECUs for controlling these devices. The information service unit 59 uses information acquired from external devices via the communication module 60 or the like to provide various information / services (e.g., multimedia information / multimedia services) to the occupants of the vehicle 40.

[0561] The information service unit 59 may include input devices (e.g., keyboards, mice, microphones, switches, buttons, sensors, touch panels, etc.) that accept input from the outside, and may also include output devices (e.g., displays, speakers, LED lamps, touch panels, etc.) that output to the outside.

[0562] The driving assistance system unit 64 includes various devices for providing functions to prevent accidents and reduce the driver's driving burden, such as millimeter-wave radar, Light Detection and Ranging (LiDAR), cameras, positioning locators (e.g., Global Navigation Satellite System (GNSS)), map information (e.g., High Definition (HD) maps, Autonomous Vehicle (AV) maps), gyro systems (e.g., Inertial Measurement Units (IMUs), Inertial Navigation Systems (INSs)), artificial intelligence (AI) chips, and AI processors, as well as one or more ECUs that control these devices. The driving assistance system unit 64 also transmits and receives various information via the communication module 60 to realize driving assistance functions or autonomous driving functions.

[0563] The communication module 60 can communicate with the microprocessor 61 and components of the vehicle 40 via the communication port 63. For example, the communication module 60 transmits and receives data (information) via the communication port 63 to and from the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, the microprocessor 61 and memory (ROM, RAM) 62 in the electronic control unit 49, and the various sensors 50-58, which are provided in the vehicle 40.

[0564] The communication module 60 is a communication device that can be controlled by the microprocessor 61 of the electronic control unit 49 and can communicate with an external device. For example, it transmits and receives various information to and from the external device via wireless communication. The communication module 60 may be located either inside or outside the electronic control unit 49. The external device may be, for example, the base station 10 or the user terminal 20 described above. Furthermore, the communication module 60 may be, for example, at least one of the base station 10 and the user terminal 20 described above (or may function as at least one of the base station 10 and the user terminal 20).

[0565] The communication module 60 may transmit at least one of signals from the above-mentioned various sensors 50-58 input to the electronic control unit 49, information obtained based on the signals, and information based on input from the outside (user) obtained via the information service unit 59 to an external device via wireless communication. The electronic control unit 49, the various sensors 50-58, the information service unit 59, etc. may be referred to as input units that accept input. For example, the PUSCH transmitted by the communication module 60 may include information based on the above-mentioned input.

[0566] The communication module 60 receives various information (traffic information, traffic signal information, vehicle distance information, etc.) transmitted from an external device and displays it on an information service unit 59 provided in the vehicle. The information service unit 59 may also be called an output unit that outputs information (for example, outputs information to a device such as a display or speaker based on the PDSCH received by the communication module 60 (or data / information decoded from the PDSCH)).

[0567] Furthermore, the communication module 60 stores various information received from external devices in a memory 62 that can be used by the microprocessor 61. Based on the information stored in the memory 62, the microprocessor 61 may control the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, various sensors 50-58, and the like provided in the vehicle 40.

[0568] Furthermore, a base station in the present disclosure may be read as a user terminal. For example, the aspects / embodiments of the present disclosure may be applied to a configuration in which communication between a base station and a user terminal is replaced with communication between multiple user terminals (which may be called, for example, Device-to-Device (D2D) or Vehicle-to-Everything (V2X)). In this case, the user terminal 20 may be configured to have the functions of the base station 10 described above. Furthermore, terms such as "uplink" and "downlink" may be read as terms corresponding to terminal-to-terminal communication (for example, "sidelink"). For example, terms such as an uplink channel and a downlink channel may be read as a sidelink channel.

[0569] Similarly, the user terminal in the present disclosure may be read as a base station, in which case the base station 10 may be configured to have the functions of the user terminal 20 described above.

[0570] In the present disclosure, an operation described as being performed by a base station may be performed by its upper node in some cases. It is apparent that in a network including one or more network nodes having a base station, various operations performed for communication with a terminal may be performed by the base station, one or more network nodes other than the base station (such as, but not limited to, a Mobility Management Entity (MME), a Serving-Gateway (S-GW), etc.), or a combination thereof.

[0571] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Furthermore, the order of the processing procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0572] Each aspect / embodiment described in the present disclosure may be a technology other than Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 6th generation mobile communication system (6G), xth generation mobile communication system (xG (x is, for example, an integer or decimal number)), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.17 (WiMAX (registered trademark)), IEEE 802.19 (WiMAX (registered trademark)), IEEE 802.20 (WiMAX (registered trademark)), IEEE 802.21 (Wi-Fi (registered trademark)), IEEE 802.22 (WiMAX (registered trademark)), IEEE 802.23 (WiMAX (registered trademark)), IEEE 802.24 (WiMAX (registered trademark)), IEEE 802.25 (WiMAX (registered trademark)), IEEE 802.26 (WiMAX (registered trademark)), IEEE 802.27 (WiMAX (registered trademark)), IEEE 802.28 (WiMAX (registered trademark)), IEEE 802.29 (WiMAX (registered trademark)), IEEE 802.30 (WiMAX (registered trademark)), IEEE 802.31 (Wi-Fi (registered trademark)), IEEE 802.32 (WiMAX (registered trademark)), IEEE 802.33 (WiMAX (registered trademark)), IEEE 802. The present invention may be applied to systems that use IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), or other suitable wireless communication methods, or to next-generation systems that are expanded, modified, created, or defined based on these. Furthermore, the present invention may be applied to a combination of multiple systems (e.g., a combination of LTE or LTE-A and 5G).

[0573] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0574] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0575] The term "determining" as used in this disclosure may encompass a wide variety of actions. For example, "determining" may be considered to be judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry (e.g., looking up in a table, database, or another data structure), ascertaining, etc.

[0576] Additionally, "determining" may be considered to be "determining" receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), etc.

[0577] Furthermore, "determination" may be considered to be "determining" resolving, selecting, choosing, establishing, comparing, etc. In other words, "determination" may be considered to be "determining" some kind of action. In the present disclosure, "determination" may be read interchangeably with the above-mentioned actions.

[0578] Furthermore, in this disclosure, "determine / determining" may be interchangeably read as "assume / assuming," "expect / expecting," "consider / considering," etc. Furthermore, in this disclosure, "does not expect to do..." may be interchangeably read as "assumes not to do...."

[0579] In the present disclosure, "expect" may be interchangeably read as "be expected." For example, "expect(s) ..." ("..." may be expressed, for example, as a that clause, a to-infinitive, etc.) may be interchangeably read as "be expected ...." "does not expect ..." may be interchangeably read as "be not expected ...." Furthermore, "An apparatus A is not expected ..." may be interchangeably read as "an apparatus B other than apparatus A does not expect ... from apparatus A" (e.g., if apparatus A is a UE, apparatus B may be a base station).

[0580] The "maximum transmit power" in this disclosure may mean the maximum value of transmit power, the nominal UE maximum transmit power, or the rated UE maximum transmit power.

[0581] As used in this disclosure, the terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access."

[0582] In this disclosure, when two elements are connected, they may be considered to be "connected" or "coupled" to one another using one or more wires, cables, printed electrical connections, etc., as well as using electromagnetic energy having wavelengths in the radio frequency range, microwave range, light (both visible and invisible) range, etc., as some non-limiting and non-exhaustive examples.

[0583] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0584] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0585] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0586] In the present disclosure, terms such as "less than or equal to," "less than," "greater than," "more than," "equal to," etc. may be interchangeable. Furthermore, in the present disclosure, terms meaning "good," "bad," "big," "small," "high," "low," "fast," "slow," "wide," "narrow," etc. may be interchangeable, not limited to the positive, comparative, and superlative. Furthermore, in the present disclosure, terms meaning "good," "bad," "big," "small," "high," "low," "fast," "slow," "wide," "narrow," etc. may be interchangeable, not limited to the positive, comparative, and superlative, as expressions with "i-th" (i is an arbitrary integer) attached (for example, "highest" may be interchangeable with "i-th highest").

[0587] In this disclosure, the terms "of," "for," "regarding," "related to," "associated with," etc. may be read interchangeably.

[0588] In the present disclosure, terms such as "when A, B," "if A, (then) B," "B upon A," "B in response to A," "B based on A," "B during / while A," "B before A," "B at (the same time as) / on A," "B after A," "B since A," and "B until A" may be interchangeable. Note that A, B, and the like herein may be replaced with appropriate expressions such as nouns, gerunds, and regular sentences, depending on the context. Note that the time difference between A and B may be approximately zero (immediately after or immediately before). A time offset may also be applied to the time at which A occurs. For example, "A" may be interchangeable with "before / after a time offset at which A occurs." The time offset (eg, one or more symbols / slots) may be predefined or may be specified by the UE based on signaled information.

[0589] In the present disclosure, timing, time, duration, time instance, any time unit (e.g., slot, subslot, symbol, subframe), occasion, resource, etc. may be read interchangeably.

[0590] Although the invention according to the present disclosure has been described in detail above, it is clear to those skilled in the art that the invention according to the present disclosure is not limited to the embodiments described in the present disclosure. The description of the present disclosure is for illustrative purposes only and does not impose any limiting meaning on the invention according to the present disclosure.

Claims

1. A terminal having a receiving unit that receives information regarding a model or functionality ID, and a control unit that controls pairing for both side models based on the information, wherein the control unit checks whether a model available on the terminal side is a model available on the base station side based on the information.

2. The terminal according to claim 1, wherein the control unit controls the pairing in accordance with at least one of a scheme in which an ID is shared between the terminal side and the base station side, a scheme in which a part of an ID is used, and a scheme in which a common ID is used between the terminal side and the base station side.

3. The terminal according to claim 1, wherein the control unit controls the reporting of available IDs or unavailable IDs based on the result of the check.

4. The terminal according to claim 1, wherein the control unit checks availability of the model based on at least one of the received ID, pairing information including a list of IDs available for pairing, and a portion of IDs available on the terminal side.

5. A wireless communication method for a terminal, comprising the steps of: receiving information regarding a model or functionality ID; controlling pairing for both side models based on the information; and checking whether a model available on the terminal side is a model available on the base station side based on the information.

6. A base station comprising: a receiving unit that receives information regarding a model or functionality ID; and a control unit that controls pairing for both side models based on the information, wherein the control unit checks whether a model available on the terminal side is a model available on the base station side based on the information.

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