Network device and wireless communication method

Abstract

A network device according to one aspect of the present disclosure comprises: a reception unit that collects, from a plurality of entities, data sets for training of a model; and a control unit that executes the training of the model on the basis of the data sets for training. In each of the data sets for training, the number of samples is associated with a certain feature amount. The control unit executes the training of the model on the basis of different feature amounts of the data sets for training, which have been respectively collected for the plurality of entities.

Description

Network device and wireless communication method

[0001] The present disclosure relates to a network device and a wireless communication method 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] Life cycle management (LCM) using artificial intelligence / machine learning (AI / ML) technologies is being considered for wireless communication systems. LCM use cases include CSI feedback, beam management, and terminal positioning.

[0006] For specific use cases utilizing these AI / ML models (CSI feedback, beam management, and terminal positioning as mentioned above), further extensions (e.g., model training) for application to the RAN are required.

[0007] However, the rules for training the model in the RAN are not clear enough, and without these rules, it may not be possible to achieve a suitable LCM, which may affect the throughput / quality of communication.

[0008] Therefore, one of the objects of the present disclosure is to provide a network device and a wireless communication method that can improve communication throughput / quality.

[0009] A network device according to one aspect of the present disclosure includes a receiving unit that collects model training datasets from multiple entities, and a control unit that performs training of the model based on the training datasets, wherein the training datasets are associated with a number of samples for a certain feature, and the control unit performs training of the model based on different features of the training datasets collected for each of the multiple entities.

[0010] According to one aspect of the present disclosure, communication throughput / quality can be improved.

[0011] FIG. 1 is a diagram illustrating an example of processing using an AI model. FIG. 2 is a diagram illustrating an example of an AI model (AI / ML model). FIG. 3 is a diagram illustrating an example of a dataset for horizontal associative learning (HFL). FIG. 4 is a diagram illustrating an example of a dataset for vertical associative learning (VFL). FIG. 5 is a conceptual diagram illustrating an overall training procedure of the present disclosure. FIG. 6 is a diagram illustrating an example of a correspondence relationship between entities in the training procedure of the present disclosure. FIG. 7 is a sequence diagram illustrating an example of a training procedure (registration procedure) of the present disclosure. FIG. 8 is a sequence diagram illustrating an example of a training procedure (disclosure procedure) of the present disclosure. FIG. 9 is an example of a sequence diagram illustrating the overall flow of the training procedure of the present disclosure. FIG. 10 is an example of a sequence diagram illustrating the overall flow of the training procedure of the present disclosure. FIG. 11 is an example of a sequence diagram illustrating the overall flow of the training procedure of the present disclosure. FIG. 12 is an example of a sequence diagram illustrating the overall flow of the training procedure of the present disclosure. FIG. 13 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment. FIG. 14 is a diagram illustrating an example of the configuration of a base station according to an embodiment. Fig. 15 is a diagram illustrating an example of a configuration of a user terminal according to an embodiment. Fig. 16 is a diagram illustrating an example of a hardware configuration of a base station and a user terminal according to an embodiment. Fig. 17 is a diagram illustrating an example of a vehicle according to an embodiment.

[0012] (AI Model) With regard to future wireless communication technologies, the use of AI technologies such as machine learning (ML) for network / device control and management is being considered.

[0013] For example, for future wireless communication technologies, the use of AI techniques is being considered 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 positioning (e.g., improved position estimation / prediction).

[0014] In the present disclosure, AI model information used in AI technology may refer to information including at least one of the following: - Information on the input / output of the AI ​​model; - Pre-processing / post-processing information for the input / output of the AI ​​model; - Information on parameters of the AI ​​model; - Training information for the AI ​​model; - Inference information for the AI ​​model; - Performance information regarding the AI ​​model.

[0015] In the present disclosure, the AI ​​model and the AI / ML model may be read interchangeably.

[0016] 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); - Type of the input / output data (e.g., immutable value, floating-point number); - 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]).

[0017] 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).

[0018] 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 base station adjacent to (or serving) the UE (e.g., a base station / cell identifier (ID), a BS-UE distance, a direction / angle of the BS (UE) as seen from the UE (BS), coordinates of the BS (UE) as seen from the UE (BS) (e.g., coordinates on the X, Y, and Z axes), etc.), a specific address of the UE (e.g., an Internet Protocol (IP) address), etc. The location information of the UE is not limited to information based on the position of the BS, and may be information based on a specific point.

[0019] 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.).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] FIG. 1 is a diagram showing an example of processing using an AI model. For example, Z-score normalization (x) is performed as preprocessing for input information x (original input values). ｎｅｗ = (x - μ) / σ, where μ is the mean of x and σ is the standard deviation) ｎｅｗ (Normalized input values) may be input to the AI ​​model, and the output y ｏｕｔ The output values ​​may be post-processed to obtain the final output y (post-processed output values).

[0024] 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).

[0025] In addition, the weight information in the above 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; - 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))).

[0026] The structure of the AI ​​model may also include information about at least one of the following: number of layers, 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 (L2 regularization, dropout function, etc.), where (e.g., after which layer) to place this function).

[0027] The layer information may include information about at least one of the following: the number of neurons in each layer, the kernel size, the stride for pooling / convolutional layers, the pooling method (MaxPooling, AveragePooling, etc.), the residual block information, the number of heads, the normalization method (Batch normalization, instance normalization, layer normalization, etc.), the activation function (Sigmoid, tanh function, ReLU, leaky ReLU information, Maxout, Softmax).

[0028] FIG. 2 is a diagram illustrating an example of an AI model (AI / ML model). This example illustrates an AI model including a ResNet model component #1, a Transformer model component #2, a dense layer, and a normalization layer. In this manner, one AI model may be included as a component of another AI model. Note that FIG. 2 may also illustrate an AI model in which processing proceeds from left to right.

[0029] 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, 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).

[0030] The inference information for the AI ​​model may include information regarding decision tree branch pruning, parameter quantization, etc.

[0031] The performance information regarding the AI ​​model may include information regarding the expected value of a loss function defined for the AI ​​model.

[0032] 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.

[0033] 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 an AI model index, for example). The AI ​​model information in the present disclosure may include the 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.

[0034] (Model Training) For example, the following methods are being considered for training AI / ML models.

[0035] Data (input information) for training (for training) may be collected from any entity (UE, gNB, or other NF with corresponding functionality). Any entity that provides data for training may be called a data source.

[0036] The data for training (which may be called data sample Xi, etc.) may consist of at least one of the following: Features / measurements x_i1, x_i2, ..., x_in; Labels y_i, where i denotes a sample index (which may be, for example, any integer) and n denotes the number of samples (which may be, for example, any integer).

[0037] Data containing one or more of the elements described above may be called a training dataset (data sample set).

[0038] An entity performing the training may have access to the training dataset and perform the training using the training dataset.

[0039] The entity that performs the training may be at least one of the following entities: A training server (e.g., a UE vendor / UE-side [model-specific] training server); A function in the core network (i.e., any NF); An OAM; or Other NFs.

[0040] (Analysis) Life cycle management (LCM) using artificial intelligence / machine learning (AI / ML) technology is being considered for wireless communication systems. LCM use cases include CSI feedback, beam management, and terminal positioning.

[0041] Further extensions to the RAN are required for specific use cases utilizing these AI / ML models (CSI feedback, beam management, and terminal positioning, as mentioned above).

[0042] For example, model training requires data (input information) from UE / NW (e.g., gNB). However, it is not desirable (preferable) for various vendors to disclose this data to third parties from the viewpoint of disclosure of know-how (such as leaking of design / ideas).

[0043] More specifically, training data (input information) and models are hardware-dependent, and therefore it is not required (or desirable) for measurement data (training input information) to be shared among training entities owned / operated by different vendors / operators.

[0044] Furthermore, each vendor has its own training server for its own UEs and uses its own methods to train the models. In this case, the training data is limited. That is, each vendor can only collect data (input information) for its own UEs, which may limit the improvement of the model performance.

[0045] It is also conceivable to share a minimal training data set for standard models.

[0046] Furthermore, use cases can be envisioned in which data from both the UE and the NW (gNB) is used for training.

[0047] In other words, it is necessary to train a suitable model (to improve model performance) without disclosing vendor-specific information.

[0048] However, the rules for such training are not sufficiently clear, and if they are not clear, it may be difficult to achieve a suitable LCM, which may affect the throughput / quality of communication.

[0049] Therefore, the inventors have conceived a method for properly performing model training.

[0050] 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.

[0051] (Various Reinterpretations) In the present disclosure, a word enclosed in "( )" in a sentence may indicate an explanation of the word immediately preceding it (for example, an explanation of spelling), a paraphrase, a specific example, a supplementary explanation, etc. Furthermore, in the present disclosure, a word enclosed in "[ ]" in a sentence may be interpreted including the word in the meaning of the entire sentence, or may be interpreted excluding (ignoring) the word in the meaning of the entire sentence. Note that "( )" and "[ ]" may also be used for purposes / meanings other than those mentioned above.

[0052] 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."

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] In the present disclosure, physical layer signaling may be, for example, Downlink Control Information (DCI), Uplink Control Information (UCI), and the like.

[0058] In the present disclosure, the terms drop, abort, cancel, puncture, rate match, postpone, do not transmit, etc. may be read interchangeably.

[0059] 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.

[0060] In the present disclosure, positioning may be interchangeably read as position determination, position estimation, position prediction, etc. In the present disclosure, KPI (Key Performance Indicator) and performance metrics may be interchangeably read as KPI (Key Performance Indicator) and performance metrics (Performance Metrics Calculation), model monitoring, and performance monitoring may be interchangeably read as KPI (Key Performance Indicator), model monitoring, and performance monitoring.

[0061] In the following embodiments, to explain the AI ​​model for communication between UEs, gNBs, and other NFs, the relevant entities are UEs and gNBs, but the application of each embodiment of the present disclosure is not limited to this. For example, for communication between other entities (e.g., communication between UEs), the UEs and gNBs in the following embodiments may be replaced with a first UE, a second UE, a third UE, etc. In other words, any UE or gNB in ​​the present disclosure may be replaced with any UE or gNB. Furthermore, the NW, base station (BS), gNB, and TRP may be replaced with each other.

[0062] In the present disclosure, the terms antenna port, subband, angle, and delay may be interchangeable. In the present disclosure, the terms NW, base station, gNB, and RAN may be interchangeable.

[0063] 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.

[0064] In the present disclosure, timing, time, duration, time instance, slot, subslot, symbol, subframe, etc. may be read interchangeably.

[0065] In the present disclosure, information element (IE) and [higher layer] parameter may be interchangeable. In the present disclosure, transmission and reporting may be interchangeable. Path and additional path may be interchangeable.

[0066] Examples of the present disclosure are applicable to all Life Cycle Management (LCM) procedures, for example, the reported information may be applied to data collection for model inference, performance monitoring, model training and model updates, etc., related to AI / ML.

[0067] The present disclosure is applicable to any use case utilizing an AI model (e.g., CSI feedback, beam management, terminal positioning).

[0068] (Wireless Communication Method) The embodiments of the present disclosure can be broadly categorized as follows: - 0th embodiment: Overview of federated learning - 1st embodiment: Procedure of federated learning.

[0069] Each embodiment will be described below based on these. Each embodiment / option may be applied alone or in combination.

[0070] In the present disclosure, NW / NF may refer to a function / device (which may also be called a network function / device) implemented inside or outside the core network.

[0071] The UE / NW (gNB / other NF) may apply each of the embodiments described below to train the model and perform various operations related to it (measurement / prediction / reporting / transmission / reception).

[0072] The UE / NW (gNB) may receive various settings for training (including training-related datasets). Furthermore, the UE / NW (gNB) may report / transmit (feedback) the results of the corresponding training to the NW (other NF).

[0073] The NW (gNB / other NF) may send various settings for training to the UE / gNB. The NW may also receive corresponding training results (reports) from the UE / gNB.

[0074] The UE / NW (gNB / other NF) may control various operations related to training (transmission and reception of related information) by applying the embodiments of the present disclosure and the various provisions described above. Furthermore, the UE / NW (gNB / other NF) may perform information exchange between multiple entities to realize these various operations.

[0075] According to each embodiment of the present disclosure, the procedure for federated learning of an AI / ML model is clarified. As a result, it is possible to improve the accuracy of the model. As a result of the improved accuracy of the model, appropriate performance monitoring (LCM) can be realized, and improvements in communication throughput / quality can be expected.

[0076] In the present disclosure, UE, gNB, RAN, other network functions (NFs), and specific entities (sources) may be interchangeable.

[0077] In the present disclosure, the NF may include, for example, at least one of the following: Network Data Analytics Function (NWDAF) (e.g., a function for analyzing network data); User Equipment (UE) (e.g., a function for user access to network services via a radio interface); (Radio) Access Network ((R)AN) (e.g., a function for providing a radio access network); Application Function (AF) (e.g., a function for realizing an application server external to the 5G Core Network (5GC)); Access and Mobility management Function (AMF) (e.g., a function for managing UE registration, location, etc.); Model Training Function (MTF) (e.g., a function related to model training); Operation, Administration and Maintenance (Management) (OAM) (e.g., a function for providing means for operation and maintenance management); Network Exposure Function (NEF) (e.g., a function for providing an application interface for 5GC NF services to the outside). Network Repository Function (NRF) (for example, a function that registers the services of each network function).

[0078] For example, the MTF is the entity that performs the model training and may be realized by the NWDAF or other NF / entity (e.g., AF / OAM).

[0079] It should be understood that these are merely examples and that other NFs are also covered by the present disclosure.

[0080] The NF (e.g., MTF) in the present disclosure can be replaced with any of the above-mentioned NFs. In other words, terms related to 5G / 6G in the present disclosure can be replaced with terms of other technologies / systems. Furthermore, when this replacement is made, it is naturally understood by those skilled in the art that, for example, the NF can be replaced with a function similar to the NF of 5G / 6G (or a device having a similar function).

[0081] In the present disclosure, the terms NF, entity, vendor, server, and client may be interchangeable.

[0082] In this disclosure, the terms service, procedure, and step may be interpreted interchangeably.

[0083] In this disclosure, training, learning, and federated learning may be read interchangeably.

[0084] <Tenth Embodiment> The tenth embodiment relates to an overview of federated learning.

[0085] The federated learning of the present disclosure can be classified as follows depending on how the dataset for training / learning is handled (the data of interest): Horizontal Federated Learning (HFL) Vertical Federated Learning (Learning Federated Learning).

[0086] Horizontal associative learning may be referred to as first associative learning, and vertical associative learning may be referred to as second associative learning. Furthermore, horizontal associative learning and vertical associative learning may be collectively referred to as "associative learning."

[0087] In this disclosure, federated learning may refer to learning performed / achieved collaboratively by multiple different entities. More specifically, federated learning may refer to a distributed model learning mechanism in which multiple FL clients jointly learn a model under the supervision of an FL server, with or without sharing local training data.

[0088] <<Horizontal Associative Learning>> Figure 3 is a diagram showing an example of a dataset for horizontal associative learning (HFL). The horizontal axis of Figure 3 represents the feature [amount], and the vertical axis represents the sample [number]. The same applies to Figure 4 described below.

[0089] As shown in Figure 3, data from a particular entity #A (which may be referred to as dataset #A) refers to a sample set containing samples of multiple UEs (UEs #m to #z) for a certain feature (UE mobility feature), with each sample having a label associated with it.

[0090] Also, dataset #A may refer to mobility information about UEs #m to #z that one entity / NF (e.g., NWDAF #2) collects from another entity (entity #A).

[0091] Data from a particular entity #B (which may be referred to as dataset #B) refers to a sample set that includes samples of multiple UEs (UE #1 to #n) for a certain feature (UE mobility feature), with each sample having a label associated with it.

[0092] Also, dataset #B may refer to mobility information about UEs #1 to #n that one entity / NF (e.g., NWDAF #1) collects from another entity (entity #B).

[0093] As shown in Figure 3, features (i.e., UE mobility features) may overlap (or may be the same) between Data Set A and Data Set B. Also, in Data Set A and Data Set B, samples for UEs m to z and samples for UEs 1 to n may or may not overlap.

[0094] In HFL, different entities (NWDAF#1, #2) jointly train a model for a common feature (e.g., UE mobility feature) using multiple samples (different samples for NWDAF#1, #2).

[0095] That is, in HFL, training data features are common across multiple entities, but training is performed based on different sample sets. For example, when there are not enough samples for a certain feature, HFL can improve the accuracy of model training by aggregating the number of samples across multiple entities.

[0096] 3, training is performed based on overlapping features (e.g., UE mobility features) in datasets A and B collected by different entities. That is, HFL may mean federated learning focusing on the horizontal axis (features) as the dataset for training.

[0097] According to HFL, the accuracy of model training can be improved by utilizing more examples of a feature across multiple entities.

[0098] Note that in HFL, the model / functionality to be trained for each entity may be the same or different.

[0099] (Example of HFL Procedure) An example of the HFL procedure will be described below. Note that the following procedure is merely an example and is not limited to this. The procedure of the first embodiment described later may be applied to the HFL.

[0100] All FL clients / servers (entities) may have access to the same feature dataset (samples), and each entity may have its own local training dataset (samples), which may or may not be shared with other clients (entities).

[0101] All FL clients / servers (entities) may possess the same model / functionality.

[0102] The overall procedure of HFL can be exemplified as follows: ・The FL server sends the latest model to the FL client (collection step). ・The FL client trains / updates the model using its own local learning dataset (training step). ・The FL client sends / reports the updated model to the FL server (reporting step). ・The FL server aggregates / integrates the updated models received from [multiple] FL clients to generate a new (updated) model. ・The FL server checks the stopping criterion (such as model accuracy), and if it is not met (sufficient model accuracy is not achieved / model performance is not satisfied), it repeats the loop (each of the steps described above).

[0103] <<Vertical Associative Learning>> FIG. 4 is a diagram showing an example of a dataset for vertical associative learning (VFL).

[0104] As shown in FIG. 4, data from a particular entity #A (which may be referred to as data set #A) refers to a sample set that includes samples of multiple UEs (UE #1 to #n) regarding UE mobility characteristics.

[0105] Furthermore, dataset #A may refer to communication-related information about UEs #1 to #n that an entity / NF (e.g., NWDAF) collects from another entity (entity #A).

[0106] Data from a particular entity #B (which may be referred to as dataset #B) refers to a sample set containing samples of multiple UEs (UE #1 to #n) for a certain UE QoE characteristic, with each sample having a label associated with it.

[0107] Also, dataset #B may refer to QoE related information for UEs #1 to #n that one entity / NF (e.g., AF) collects from another entity (entity #B).

[0108] It should be noted that QoE stands for Quality of Experience, and may represent, for example, a qualitative quality of experience from the user's perspective.

[0109] As shown in Figure 4, data set #A and data set #B may have overlapping (identical) sample sets for UEs #1 to #n, and features (e.g., UE mobility features and UE QoE features) may or may not overlap.

[0110] In VFL, different entities (NWDAF, AF) jointly train models for different features (e.g., UE mobility features and UE QoE features) using a common sample set (sample set for UE #1 to #n).

[0111] That is, in VFL, the features of the training data differ across multiple entities (each entity has its own unique features), but training is performed based on a common sample set. For example, when there are enough samples for a certain feature but not enough features, VFL can improve the accuracy of model training by aggregating the features across multiple entities.

[0112] Thus, in Fig. 4, training is performed using the same / common set of samples from datasets A and B collected by different entities, based on different features (e.g., UE mobility features and UE QoE features). That is, VFL may refer to federated learning that focuses on the vertical axis (number of samples) in Fig. 4 as the dataset for training.

[0113] VFL allows for more common samples of features across multiple entities, thereby improving the accuracy of model training.

[0114] In VFL, the model / functionality to be trained for each entity may be different or the same.

[0115] In the above-described HFL / VFL, the UE mobility feature / UE QoE feature is exemplified as a feature [quantity] of a data set, but the feature [quantity] is not limited to this. The feature [quantity] may be replaced with any feature [quantity].

[0116] According to this embodiment, the content of the FL becomes clear, and it is possible to improve the accuracy of model training according to the training dataset (number of samples / amount of features).

[0117] First Embodiment The first embodiment relates to a procedure for associative learning.

[0118] <<Overall Procedure>> Figure 5 is a conceptual diagram showing the overall training procedure of the present disclosure. While Figure 5 describes the VFL procedure, the present disclosure is not limited to this. The first embodiment is also applicable to HFL. That is, in the present disclosure, HFL and VFL may be interchangeable.

[0119] In the present disclosure, a [VFL] client, a [VFL] server, and a [VFL] participant(s) may be read as interchangeable.

[0120] The overall procedure of VFL may consist of at least one of the following steps (see FIG. 5):

[0121] (Step #1) Each client (VFL client #1, #2) trains a model using a training dataset containing different features (F1 to Fi or Fj to Fm) for a certain sample (set) (e.g., samples S1 to Sn).

[0122] (Step #2) Each client sends / reports intermediate results (intermediate values) from model training to the server (VFL server).

[0123] Intermediate results can be, for example: any output of hidden layers in VFL participants, partial loss values ​​if VFL has access to them for specific labels, feature contributions.

[0124] (Step #3) The server trains a model using the intermediate results (or training dataset (samples)) obtained from each client, and calculates the loss of the model.

[0125] (Step #4) The server returns / sends the weight updates (updated weight values) to each client.

[0126] (Step #5) Each client updates its model using the updates (updated weight values) received from the server.

[0127] (Step #6) The above steps are repeated.

[0128] <<VFL Participant(s)>> An entity associated with a VFL procedure (an entity capable of performing VFL-related operations) may be called a VFL participant. A VFL participant may have multiple clients / servers. A VFL participant may refer to an entity that supports operations / functions related to training performed collaboratively among multiple entities (i.e., federated learning).

[0129] Each UE vendor / gNB vendor may have its own training server.

[0130] The entity that performs the training (which may be called a training entity) may reside inside or outside the core network.

[0131] In the present disclosure, the terms VFL participant (which may simply be referred to as a participant or an FL participant), UE vendor, gNB vendor, training entity, [VFL] client, and [VFL] server may be interchangeable.

[0132] VFL participants act as [VFL] clients / [VFL] servers in training scenarios of different models / functionalities.

[0133] For example, the UE server / UE vendor can be the entity that trains the UE-side model (VFL server).

[0134] Alternatively, the UE server / UE vendor can be the entity (VFL client) that trains the NW-side model, which requires UE-side data.

[0135] Each VFL participant may collect its own training dataset (which may be referred to as a local training dataset) from its corresponding entity, and each VFL participant may have its own model based on the features available in the dataset.

[0136] Model training may be split (collaborative) among multiple entities, with each entity training its own "local" model, in which case intermediate results may be shared among the multiple entities.

[0137] Additionally, entities may perform model training collaboratively without sharing local data (i.e., their own data) / local models between multiple entities.

[0138] 6 is a diagram showing an example of the correspondence between entities in the training procedure of the present disclosure, in which the solid line indicates the flow of vendor-specific data collection, and the dashed dotted line indicates the flow of standard data collection.

[0139] As shown in Figure 6, gNB Vendor #1 (training server), UE Vendor #1 (training server), and UE Vendor #2 (training server) may be (may be included in) VFL participants. As mentioned above, each VFL participant may have its own model.

[0140] Furthermore, examples of NFs on the core network side include MTF, NEF, NRF, NF#1, NF#2, NF, etc.

[0141] For example, the MTF is one of the training entities in the core network and has access to the training data from the core network.

[0142] Note that each NF in the core network can exchange (send and receive) information (training data, etc.) with each other.

[0143] For example, each UE vendor #1, #2 [training server] may collect information on UEs corresponding to its own vendor via any NF (e.g., NEF) within the gNB / core network.

[0144] The training server of gNB vendor #1 may collect information on gNBs corresponding to its vendor via any NF (e.g., NEF) within the gNB / core network.

[0145] In the present disclosure, the gNB / UE vendor's [training server] may be collectively referred to as a trusted (trusted) AF (trusted application function) / untrusted (untrusted) AF (untrusted application function). That is, the gNB vendor's [training server] and the UE vendor's [training server] may be interchangeably referred to as a trusted AF (trusted AF) / untrusted AF (untrusted AF).

[0146] In the present disclosure, a VFL participant may be interchangeably referred to as a trusted AF (trusted AF) / untrusted AF (untrusted AF), and an entity (NF) that is not a VFL participant may be interchangeably referred to as an untrusted AF (untrusted AF).

[0147] In this disclosure, a trusted AF may refer to an entity that can directly interact with NFs of other core networks (CNs) (e.g., send and receive various information), as will be described in the registration / discovery procedures below. For example, a trusted AF can communicate with other NFs (NRFs) via a control plane (C-plane) that handles control messages, etc.

[0148] In this disclosure, an untrusted AF may refer to an entity that, unlike a trusted AF, cannot directly interact with NFs of other CNs, in which case the untrusted AF achieves interaction with NFs (NRFs) of other CNs via a relay function such as an NEF.

[0149] Specific processes in the training procedure of the present disclosure will be described below. <<Registration Procedure>> Fig. 7 is a sequence diagram showing an example of the training procedure (registration procedure) of the present disclosure.

[0150] There are two possible cases for the registration procedure depending on the type of VFL participant.

[0151] If the VFL participant is a specific NF (eg, a trusted AF), the registration procedure may be realized between the VFL participant and the NRF by steps #1 and #2.

[0152] Specifically, the VFL participant transmits profile information regarding a registration request (NRF_Registration_Request) to the NRF (step #1).

[0153] The NRF sends / returns a response (NRF_Registration_Response) to the receipt of the profile information to the VFL participant (step #2).

[0154] In this way, if the VFL participant is a trusted AF, the VFL participant can perform a registration request directly to the NRF.

[0155] On the other hand, if the VFL participant is another specific NF (untrusted AF), the registration procedure may be realized by steps #1a, #1b, #2a, and #2b between the VFL participant and the NRF via the NEF.

[0156] Specifically, the VFL participant transmits profile information regarding AF registration (AF registration) to the NEF (step #1a).

[0157] The NEF sends profile information regarding the registration request (NRF_Registration_Request) to the NRF (step #1b).

[0158] The NRF sends / returns a response (NRF_Registration_Response) to the NEF in response to receiving the profile information (step #2a).

[0159] The NEF sends / returns an AF registration response to the VFL participant (step #2b).

[0160] In this way, if the VFL participant is an untrusted AF, it is possible for the NEF to perform the registration request indirectly to the NRF [on behalf of the AF].

[0161] The profile information may include VFL capability information for indicating the capability of the NF in the VFL procedure. The VFL capability information may include information on supported models. The information on supported models may include, for example, at least one of the following information (fields):

[0162] - Model ID - Server function support - Client function support - Features.

[0163] The VFL capability information may include all information related to the VFL capability of the NF.

[0164] The information about supported models may refer to a list of models that the NF supports for VFL operation.

[0165] Model ID may refer to a unique identifier for a model (e.g., applicable decoder / reference model) with a particular structure / characteristic.

[0166] The information about support of server functions may refer to a field indicating whether VFL server functions are supported for a specified model.

[0167] The information regarding support of client functions may refer to a field indicating whether VFL client functions are supported for a specified model.

[0168] Information about features may refer to a list of features for the dataset (samples) available in the NF for training / inference.

[0169] <<Discovery Procedure>> The discovery procedure may refer to a procedure for discovering NFs (VFL participants). Fig. 8 is a sequence diagram showing an example of a training procedure (discovery procedure) of the present disclosure.

[0170] The discovery procedure assumes two cases depending on the type of VFL participant.

[0171] If the VFL participant is a specific NF (eg, a trusted AF), a discovery procedure may be realized between the VFL participant and the NRF by steps #1 and #2.

[0172] Specifically, the VFL participant transmits parameters (NRF_NFDiscovery_Request) related to a NF discovery request to the NRF (step #1).

[0173] The NRF sends / returns a response (NRF_NFDiscovery_Response) to the reception of the parameters to the VFL participant (step #2).

[0174] In this way, if the VFL participant is a trusted AF, the VFL participant can perform NF discovery requests directly to the NRF.

[0175] On the other hand, if the VFL participant is another specific NF (untrusted AF), the discovery procedure may be realized by steps #1a, #1b, #2a, and #2b between the VFL participant and the NRF via the NEF.

[0176] Specifically, the VFL participant transmits parameters related to a discovery request for an NF (NF Discovery Request) to the NEF (step #1a).

[0177] The NEF sends parameters (NRF_NFDiscovery_Request) related to a discovery request for NFs to the NRF (step #1b).

[0178] The NRF sends / returns a response (NRF_NFDiscovery_Response) to the reception of the parameters to the NEF (step #2a).

[0179] The NEF sends / returns an NF Discovery Response to the VFL participant (step #2b).

[0180] In this way, if the VFL participant is an untrusted AF, it is possible for the NEF to perform discovery requests for the NF indirectly to the NRF [on behalf of the NF].

[0181] The above-mentioned parameters may include additional parameters for discovering VFL participants, such as at least one of the following information (fields):

[0182] - Information about supported VFL participant roles (e.g. client / server) - Model ID (same as above) - Characteristics (same as above).

[0183] <<Training of UE-Side Model by an Entity Other Than the UE Vendor (e.g., MTF)>> FIG. 9 is an example sequence diagram illustrating the overall flow of the training procedure of the present disclosure.

[0184] 9 illustrates a procedure for the MTF to train a UE-side model in cooperation with a UE vendor's training server. Note that, although an example is described in which the MTF operates / functions as a VFL server and the UE vendor's training server operates / functions as a VFL client, this is of course not limiting.

[0185] In the example of Figure 9, standard UE features are collected by the MTF, while vendor-specific features are collected by the UE vendor's training server, and these collected features may not be shared with other entities.

[0186] The vendor-dependent part of the model may be trained at the vendor, while the standard (standardized) part may be collected at the server.

[0187] Note that in the example of FIG. 9, UE vendor #3 [training server] may not support vendor-specific functionality for training this model.

[0188] Based on the above, the training procedure shown in FIG. 9 will be explained below.

[0189] In step #1, registration of VFL participants is performed between UE vendors #1-#3 [training servers], MTF and NRF.

[0190] In step #2a, the MTF sends a discovery request for NFs (VFL participants) to the NRF.

[0191] In step #2b, the NRF sends a response to the previous discovery request to the MTF.

[0192] In step #3, the MTF initiates VFL for each UE vendor, for example, the MTF sends UE IDs, feature lists, etc. to UE vendors #1 and #2.

[0193] In step #4, the MTF collects standard (standardized) characteristics from the UE.

[0194] In step #5, each UE vendor (UE Vendor #1, #2) collects vendor-specific proprietary features from the UE.

[0195] In step #6, each UE vendor (UE vendors #1 and #2) trains its own model (local model) using its own collected data (local data).

[0196] In step #7, each UE vendor (UE vendor #1, #2) sends intermediate results to the MTF (forward propagation).

[0197] In step #8, the MTF trains the model using its own data and input (training data such as intermediate results) from the clients (i.e., UE vendors #1 and #2). The MTF also obtains loss and gradient updates for the model.

[0198] In step #9, the MTF sends the model gradient updates to each UE vendor (UE vendor #1, #2) (forward propagation).

[0199] In step #10, each UE vendor (UE vendor #1, #2) trains a model using model gradient updates from the server (i.e., MTF).

[0200] Note that steps #6 to #10 may be repeated until a stopping criterion is met.

[0201] <<Training of gNB-side model by an entity other than the gNB vendor (e.g., MTF)>> Figure 10 is an example sequence diagram showing the overall flow of the training procedure of the present disclosure.

[0202] 10 describes a procedure in which the MTF cooperates with the gNB vendor's training server to train the gNB-side model. Note that an example will be described in which the MTF operates / functions as a VFL server and the gNB vendor's [training server] operates / functions as a VFL client, but this is of course not limited to this.

[0203] In the example of Figure 10, standard gNB features are collected by the MTF, while vendor-specific features are collected by the gNB vendor's training server. These collected features may not be shared with other entities.

[0204] The vendor-dependent part of the model may be trained at the vendor, while the standard (standardized) part may be collected at the server.

[0205] 10 can be explained by replacing "UE" in FIG. 9 with "gNB." Therefore, a description of each process will be omitted.

[0206] <<Training of UE-side model by UE vendor [training server] supported by gNB features>> Figure 11 is an example sequence diagram showing the overall flow of the training procedure of the present disclosure.

[0207] Figure 11 describes the procedure by which a UE vendor's [training server] works in cooperation with a gNB vendor's [training server] to train a UE-side model.

[0208] In the example of Figure 11, the UE vendor's [training server] may act / function as a VFL client, and the gNB vendor's [training server] may act / function as a VFL server. By cooperating / collaborating between the UE vendor's [training server] and the gNB vendor's [training server], model training can be achieved without sharing proprietary training data.

[0209] Below, only the processing that differs from the processing already described will be mainly explained.

[0210] For example, in step #4, the UE vendor's [training server] collects features from the UE.

[0211] In step #5, each gNB vendor (gNB Vendor #1, #2) collects vendor-specific proprietary features from the gNB.

[0212] <<Training of gNB-side model by gNB vendor [training server] supported by UE features>> Figure 12 is an example sequence diagram showing the overall flow of the training procedure of the present disclosure.

[0213] Figure 12 describes the procedure by which a gNB vendor (its training server) works in cooperation with a UE vendor (its training server) to train a gNB-side model.

[0214] In the example of Figure 12, the UE vendor's [training server] may operate / function as a VFL server, and the gNB vendor's [training server] may operate / function as a VFL client. By cooperating / collaborating between the UE vendor's [training server] and the gNB vendor's [training server], model training can be achieved without sharing proprietary training data.

[0215] Below, only the processing that differs from the processing already described will be mainly explained.

[0216] For example, in step #4, the gNB vendor's [training server] collects features from the gNB.

[0217] In step #5, each UE vendor (UE Vendor #1, #2) collects vendor-specific proprietary characteristics from the UE.

[0218] According to this embodiment, the VFL procedure is made clear and UE / gNB vendors can perform model training collaboratively across multiple vendors (entities) without sharing each vendor's specific training data (measurements, features, etc.).

[0219] <Modifications> In the first embodiment described above, VFL is mainly exemplified, but the present invention is not limited to this. For example, as described in the 0th embodiment, HFL and VFL can be applied in appropriate combination.

[0220] As mentioned above, HFL (first federated learning) may refer to model training based on different numbers of samples in training datasets collected for each of multiple entities, and VFL (second federated learning) may refer to model training based on different features in training datasets collected for each of multiple entities.

[0221] The entity performing the training (e.g., MTF, training server) may decide to apply either HFL or VFL based on the number of samples and features associated with the collected training dataset.

[0222] Specifically, the entity performing the training may perform HFL if the training dataset does not have enough samples (e.g., the number of samples does not meet a certain threshold), or the entity performing the training may perform VFL if the training dataset does not have enough features (e.g., the features do not meet a certain threshold).

[0223] In this way, the selection of either HFL or VFL based on the number of samples and features associated with the collected training dataset allows for appropriate control of the associative learning, thereby advantageously improving the accuracy of model training.

[0224] <Supplementary Information> <<Notification of Information to UE>> In the above-described embodiments, notification of any information to the UE [from a Network (NW) (e.g., a Base Station (BS))] (in other words, reception of any information from the BS by the UE) may be performed 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.

[0225] 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.

[0226] 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.

[0227] In addition, notification of any information to the UE in the above-mentioned embodiments may be performed periodically, semi-persistently (triggered by an instruction from the UE or the gNB), or aperiodically (triggered by an instruction from the UE or the gNB).

[0228] In the above embodiment, the UE may receive information from the NW as at least one of the following QCL rules: QCL type A. QCL type B. QCL type C. QCL type D.

[0229] In the above-described embodiment, the QCL source RS for each QCL type may be at least one of the following several RSs: SSB; CSI-RS with / without repetition; TRS; DMRS of PDCCH / PDSCH.

[0230] In the above-described embodiment, the information from the NW may be set / indicated by the following methods: Common to multiple UEs or UE-specific; Cell-specific or common to multiple cells; Per UE / per CC / per BWP / per band / per cell / per cell group (CG).

[0231] <<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, PRACH, reference signal), or a combination thereof.

[0232] 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.

[0233] If the notification is made by UCI, the notification may be transmitted using PUCCH or PUSCH.

[0234] In addition, notification of any information from the UE in the above-mentioned embodiments may be periodic, semi-persistent (triggered by an instruction from the UE or gNB), or aperiodic (triggered by an instruction from the UE or gNB).

[0235] <<Regarding application of each embodiment>> In a UE / BS (NW / gNB / LMF / NG-RAN), specific (one or more) processes / operations / controls / assumptions / information for at least one of the above-described embodiments may be applied (used) when one or more of the following conditions are met: - A higher layer parameter indicating the specific processes / operations / controls / assumptions / information is configured; - The specific processes / operations / controls / assumptions / information is determined based on related higher layer parameters; - The specific processes / operations / controls / assumptions / information is specified / activated / triggered by a MAC CE / DCI / UCI / resource / channel / RS; - A specific UE capability indicating (or related to) the specific processes / operations / controls / assumptions / information is reported or supported; - The application of the specific processes / operations / controls / assumptions / information is determined based on specific conditions.

[0236] The specific UE capability may indicate at least one of the following: Supporting the specific process / action / control / assumption / information Supporting AI / ML-based (using AI / ML model) LCM Supporting federated learning (HFL / VFL) between different entities / NFs Supporting any NF.

[0237] Furthermore, the above-mentioned specific UE capability may be a capability that is applied across all frequencies (commonly regardless of frequency), or may be a capability for each frequency (e.g., one or a combination of a cell, a band, a band combination, a BWP, a component carrier, etc.), or may be a capability for each frequency range (e.g., Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or may be a capability for each subcarrier spacing (SubCarrier Spacing (SCS)), or may be a capability for each Feature Set (FS) or Feature Set Per Component-carrier (FSPC).

[0238] 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)).

[0239] If the above conditions are not met, the UE / BS may follow the behavior specified in existing 3GPP releases.

[0240] (Supplementary Notes) The following inventions are supplemented with respect to one embodiment of the present disclosure. [Supplementary Note 1] A network device comprising: a receiver that collects model training datasets from multiple entities; and a controller that performs training of the model based on the training datasets, wherein the training datasets are associated with the number of samples for a certain feature, and the controller performs training of the model based on different feature values ​​of the training datasets collected for each of the multiple entities. [Supplementary Note 2] The network device according to Supplementary Note 1, wherein the receiver receives intermediate results obtained by training the multiple entities based on their own datasets, and the controller controls the training of the model through federated learning using the intermediate results and the training datasets. [Supplementary Note 3] The network device according to Supplementary Note 1 or Supplementary Note 2, wherein the multiple entities are configured by participants in federated learning, trusted application functions, or untrusted application functions. [Supplementary Note 4] The network device according to any of Supplements 1 to 3, having a function related to training a terminal-side model or a network-side model. [Supplementary Note 5] The network device according to any one of Supplementary Note 1 to Supplementary Note 4, wherein the control unit determines whether to apply either a first federated learning method that trains the model based on different numbers of samples in the training dataset collected for each of a plurality of entities, or a second federated learning method that trains the model based on different features in the training dataset collected for each of a plurality of entities, based on the numbers of samples and features in the training dataset.

[0241] (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.

[0242] 13 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).

[0243] 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.

[0244] 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.

[0245] 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))).

[0246] 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 location, number, shape, size, etc. of each cell and user terminal 20 are not limited to the embodiment shown in the figure. Hereinafter, when there is no need to distinguish between the base stations 11 and 12, they will be collectively referred to as the base station 10.

[0247] The wireless communication system 1 may utilize multi-input multi-output (MIMO). For example, one cell may be formed by one antenna / base station 10, or may be formed by multiple antennas / base stations 10. One [virtual] cell (which may be called, for example, a supercell) may be composed of multiple [virtual] cells (which may be called, for example, subcells). A supercell may correspond to a cell with a fixed physical range, and a subcell may correspond to a cell with a quasi-static / dynamically variable physical range. In this case, the wireless communication system 1 may be called a cell-free system.

[0248] 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).

[0249] 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.

[0250] 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.

[0251] The multiple base stations 10 may be connected by wire (e.g., optical fiber compliant with the Common Public Radio Interface (CPRI), an X2 / Xn 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.

[0252] 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.

[0253] 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.

[0254] The user terminal 20 may be a terminal that supports at least one of communication methods such as LTE, LTE-A, and 5G.

[0255] 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).

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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).

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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).

[0269] 14 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.

[0270] 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.

[0271] 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.

[0272] 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.

[0273] 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.

[0274] 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.

[0275] 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.

[0276] 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.

[0277] 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.

[0278] 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.

[0279] 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.

[0280] 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.

[0281] On the other hand, the transmitting / receiving unit 120 (RF unit 122) may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency band signal received by the transmitting / receiving antenna 130.

[0282] 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.

[0283] 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.

[0284] 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.

[0285] 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.

[0286] The base station 10 may be separated into three elements: a radio unit (RU), a distributed unit (DU), and a central unit (CU). For example, the RU may implement RF processing (digital beamforming, digital-to-analog conversion, analog beamforming, etc.) and lower-level functions of the physical layer (precoding, IFFT, FFT, etc.). The DU may implement higher-level functions of the physical layer (coding to resource element mapping, etc.), MAC layer functions, and RLC layer functions. The CU may implement the functions of the PDCP layer, Service Data Adaptation Protocol (SDAP) layer, and RRC layer.

[0287] In the present disclosure, the base station 10 may include a single device that realizes all of the functions of the RU, DU, and CU, or may include multiple devices that each realize some of the functions of the RU, DU, and CU and are connected to each other. In the present disclosure, the base station 10 may be interchangeably read as RU / DU / CU.

[0288] In the present disclosure, the base station 10 may be interchangeably read as a network device having any of the above-described NF functions. The configuration of the base station 10 (e.g., the control unit 110 and the transceiver unit 120) may also be the configuration of a network device.

[0289] The control unit 110 may perform at least part of the processing of the control unit in the above appendix.

[0290] The transceiver unit 120 may perform at least part of the processing of the transmitter / receiver unit in the above appendix.

[0291] (User Terminal) Fig. 15 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.

[0292] 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.

[0293] 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.

[0294] 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.

[0295] 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.

[0296] 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.

[0297] 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.

[0298] 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.

[0299] 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.

[0300] 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.

[0301] 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.

[0302] 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.

[0303] 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.

[0304] 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.

[0305] 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.

[0306] 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.

[0307] 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.

[0308] 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.

[0309] The control unit 210 may perform at least part of the processing of the control unit in the above appendix.

[0310] The transceiver unit 220 may perform at least part of the processing of the transmitter / receiver unit in the above appendix.

[0311] (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.

[0312] 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.

[0313] 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. 16 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.

[0314] 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.

[0315] 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.

[0316] 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.

[0317] 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.

[0318] 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.

[0319] 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.

[0320] 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.

[0321] 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.

[0322] 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).

[0323] 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.

[0324] 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.

[0325] In addition, the devices included in the core network 30 (for example, network nodes that provide NF) may also be realized by the above-mentioned functional block / hardware configuration.

[0326] (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.

[0327] 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.

[0328] 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.

[0329] 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.

[0330] 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.

[0331] 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.

[0332] 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.

[0333] 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.

[0334] 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.

[0335] 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.

[0336] 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.

[0337] 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.

[0338] 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.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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.

[0343] 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.

[0344] 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."

[0345] 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.

[0346] 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.

[0347] 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.

[0348] 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.

[0349] 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.

[0350] 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.

[0351] With respect to any information (e.g., variables, constants, parameters) described in the present disclosure, even if not specifically stated in the above embodiments, any first device (e.g., UE / base station) may notify any second device (e.g., base station / UE) of information indicating / specifying (or relating to) the value of the any information.

[0352] 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.

[0353] 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).

[0354] 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).

[0355] 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).

[0356] 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.

[0357] 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.

[0358] 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).

[0359] 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.

[0360] 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.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," etc. may be used interchangeably.

[0371] 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.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] 17 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.

[0376] 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.

[0377] 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).

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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)).

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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).

[0392] 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."

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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...."

[0398] 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 ..." or "do ... (if the above "..." is a to-infinitive, a verb with "to")," etc. "does not expect ..." may be interchangeably read as "be not expected ..." or "does not ... (if the above "..." is a to-infinitive, a verb with "to")," etc. 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" (for example, if apparatus A is a UE, apparatus B may be a base station).

[0399] 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.

[0400] 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."

[0401] 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.

[0402] 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."

[0403] 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.

[0404] 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.

[0405] 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").

[0406] In this disclosure, the terms "of," "for," "regarding," "related to," "associated with," etc. may be read interchangeably.

[0407] 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.

[0408] In the present disclosure, timing, time, duration, time instance, any time unit (e.g., slot, subslot, symbol, subframe), period, occasion, resource, etc. may be read interchangeably.

[0409] 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 network device comprising: a receiving unit that collects model training datasets from multiple entities; and a control unit that performs training of the model based on the training datasets, wherein the training datasets are associated with the number of samples for a certain feature, and the control unit performs training of the model based on different features of the training datasets collected for each of the multiple entities.

2. The network device according to claim 1, wherein the receiving unit receives intermediate results obtained by training the plurality of entities based on their own datasets, and the control unit controls the training of the model by federated learning using the intermediate results and the training datasets.

3. The network device of claim 1, wherein the plurality of entities are composed of federated learning participants, trusted application functions, or untrusted application functions.

4. The network device according to claim 1, having functionality related to training of a terminal-side model or a network-side model.

5. The network device of claim 1, wherein the control unit determines whether to apply either a first federated learning method that trains the model based on different numbers of samples in the training dataset collected for each of multiple entities, or a second federated learning method that trains the model based on different features in the training dataset collected for each of multiple entities, based on the number of samples and features in the training dataset.

6. A wireless communication method for a network device, comprising: a step of collecting a dataset for training a model from a plurality of entities; and a step of training the model based on the training dataset, wherein the training dataset is associated with the number of samples for a certain feature, and the network device trains the model based on different features of the training dataset collected for each of the plurality of entities.

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