Terminal, wireless communication method, and base station

Abstract

A terminal according to one embodiment of the present disclosure is provided with: a receiving unit that receives information for specifying an artificial intelligence (AI) model on the basis of a model migration category; and a control unit that performs reasoning based on the AI model, the AI model being determined based on the information. According to one embodiment of the present disclosure, it is possible to achieve appropriate overhead reduction / appropriate reasoning based on an AI model / utilization of resources.

Description

Technical Field

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

[0002] In the Universal Mobile Telecommunications System (UMTS) network, Long Term Evolution (LTE) has been standardized for the purpose of further high-speed data rates and low latency (Non-Patent Document 1). In addition, LTE-Advanced (3GPP Rel. 10-14) has been standardized for the purpose of further increasing capacity and improving (evolution) of LTE (Release (Rel.) 8, 9 of the Third Generation Partnership Project (3GPP)).

[0003] Subsequent systems of LTE (for example, also known as the fifth generation mobile communication system (5G), 5G+(plus), the sixth generation mobile communication system (6G), New Radio (NR), 3GPP Rel.15 and later, etc.) are also discussed.

[0004] Prior art literature

[0005] Non-patent literature

[0006] Non-patent document 1: 3GPP TS 36.300 V8.12.0 “Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description; Stage 2 (Release 8)”, April 2010 Summary of the invention

[0007] Problems to be solved by the invention

[0008] Regarding future wireless communication technologies, the use of artificial intelligence (AI) technologies such as machine learning (ML) for network / device control and management is being discussed.

[0009] However, when using AI models, partial or complete migration of AI models / datasets is required between multiple entities (e.g., terminals, user terminals, user equipment (UE)), and base stations (BS). For example, consider that the BS / UE sends information related to the model that it or the other party can use to the other party, or instructs / sets the model that the other party should use.

[0010] In addition, consider that the BS trains an AI model and sends an instruction for the UE to deploy the trained model. In addition, consider that the UE trains an AI model and reports the trained model to the BS. In addition, consider that in the joint AI model training of the BS and the UE, information / data sets used for training are sent to each other.

[0011] However, although the migration of AI models / datasets requires the communication overhead of the wireless interface, no research has been conducted on an effective AI model / dataset migration method that also takes into account interoperability between vendors, etc. In addition, no research has been conducted on the implementation of advanced functions such as online model updates / data transfers for high-end terminals.

[0012] Although there are various model formats, model and code matching methods, etc., if the above-mentioned migration methods, implementation methods, etc. are not properly specified, appropriate overhead reduction / appropriate reasoning based on AI models / efficient resource utilization cannot be achieved, and there is a risk of inhibiting the improvement of communication throughput / communication quality.

[0013] Therefore, one of the objects of the present disclosure is to provide a terminal, a wireless communication method, and a base station that can achieve appropriate overhead reduction / appropriate reasoning / resource utilization based on an AI model.

[0014] Means for solving problems

[0015] A terminal according to one embodiment of the present disclosure comprises: a receiving unit, which receives information for determining an artificial intelligence (AI) model based on a model migration category; and a control unit, which performs inference based on the AI ​​model, wherein the AI ​​model is determined based on the information.

[0016] Effects of the Invention

[0017] According to one aspect of the present disclosure, appropriate cost reduction / appropriate reasoning based on AI models / utilization of resources can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a diagram showing an example of a framework for managing AI models.

[0019] Figure 2 This is a diagram showing an example of a file format used for AI model migration.

[0020] Figure 3A as well as Figure 3B It is a schematic diagram of the migration of a partial or complete AI model.

[0021] Figure 4A-4C This is a diagram showing an example of matching between a model and a code.

[0022] Figure 5 This is a diagram showing an example of the process of implementation mode 1.1.

[0023] Figure 6 This is a diagram showing an example of the process of implementation mode 1.1.

[0024] Fig. 7A as well as Figure 7B This is a diagram showing an example of a network structure specified by network structure information in Implementation 1.2.1.

[0025] Figure 8A-8C This is a diagram showing an example of a network structure specified by network structure information in Implementation 1.2.2.

[0026] Fig. 9 This is a diagram showing an example of the process of implementation mode 1.2.1 / 1.2.2.

[0027] Fig.10 This is a diagram showing an example of the process of implementation mode 1.2.3.

[0028] Fig.11 This is a diagram showing an example of the process of implementation mode 1.3.

[0029] Fig.12 This is a diagram showing an example of the process of implementation mode 1.4.

[0030] Fig.13A as well as Fig. 13B This is a diagram showing an example of the position of the MMU in the protocol stack according to the second embodiment.

[0031] Fig.14 This is a diagram showing an example of a schematic configuration of a wireless communication system according to an embodiment.

[0032] Fig.15 This is a diagram showing an example of the configuration of a base station according to an embodiment.

[0033] Fig.16 This is a diagram showing an example of a configuration of a user terminal according to an embodiment.

[0034] Fig.17 This is a diagram showing an example of the hardware configuration of a base station and a user terminal according to an embodiment.

[0035] Fig.18 It is a diagram showing an example of a vehicle according to an embodiment. DETAILED DESCRIPTION

[0036] (Application of Artificial Intelligence (AI) technology to wireless communications)

[0037] Regarding future wireless communication technologies, research is underway into the use of AI technologies such as machine learning (ML) for control and management of networks and devices.

[0038] For example, with regard to future wireless communication technologies, research is underway into the use of AI technology to improve channel state information (Channel State Information Reference Signal (CSI)) feedback (e.g., reducing overhead, improving accuracy, prediction), improving beam management (e.g., improving accuracy, prediction in the time / space domain), improving position measurement (e.g., improvement in position estimation / prediction), etc.

[0039] Reasoning using AI models can be performed in a terminal (termina, user terminal, user equipment (UE)) or in a network (Network (NW)) (for example, a base station (BS)).

[0040] Figure 1 This is a diagram showing an example of a framework for managing an AI model. In this example, each stage associated with an AI model is shown by a framework. This example can also be described as life cycle management of an AI model.

[0041] The data collection stage is equivalent to the stage of collecting data for the generation / update of AI models. The data collection stage includes data collation (e.g., deciding which data to migrate for model training / model inference) and data migration (e.g., migrating data to entities that perform model training / model inference (e.g., UE, gNB)).

[0042] In the model training phase, model training is performed based on the data (training data) migrated from the collection phase. This phase may also include data preparation (e.g., implementation of data preprocessing, cleaning, formatting, conversion, etc.), model training / validation, model testing (e.g., confirmation of whether the trained model meets the performance threshold), model exchange (e.g., migration of models for distributed learning), model deployment / update (deployment / update of models to entities that perform model reasoning), etc.

[0043] In the model inference phase, model inference is performed based on the data (inference data) migrated from the collection phase. This phase may also include data preparation (e.g., implementation of data preprocessing, cleaning, formatting, conversion, etc.), model inference, model monitoring (e.g., monitoring the performance of model inference), model performance feedback (feedback on model performance to entities performing model training), output (providing model output to actors), etc.

[0044] The actor stage may also include action triggers (e.g., decisions on whether to trigger actions on other entities), feedback (e.g., providing feedback on training data / inference data / information required for performance feedback), etc.

[0045] In addition, for example, the training of models for mobility optimization can also be performed in the Operation, Administration and Maintenance (OAM) / gNodeB (gNB) in the NW. The former has advantages in interoperability, large-capacity storage, operator manageability, model flexibility (feature engineering, etc.). The latter has advantages in latency (latency) that does not require model updates, data exchange for model expansion (model deployment), etc. Reasoning of the above models can also be performed in the gNB, for example.

[0046] Furthermore, the entity doing the training / inference can also be different depending on the use case.

[0047] For example, for AI-assisted beam management based on measurement reports, model training can also be performed by OAM / gNB, and model inference can be performed by gNB.

[0048] For AI-assisted UE-assisted positioning, model training can also be performed by the Location Management Function (LMF), which performs model reasoning.

[0049] For CSI feedback / channel estimation using an autoencoder, OAM / gNB / UE can also perform model training, and gNB / UE (jointly) can perform model inference.

[0050] For AI-assisted beam management based on beam measurement or AI-assisted UE positioning, OAM / gNB / UE can also perform model training and UE can perform model inference.

[0051] However, when using AI models, partial or complete migration of AI models / datasets is required between multiple entities (e.g., UE and BS). For example, consider that the BS / UE sends information about the model that it or the other party can use to the other party, or instructs / sets the model that the other party should use.

[0052] In addition, consider that the BS trains an AI model and sends an instruction for the UE to deploy the trained model. In addition, consider that the UE trains an AI model and reports the trained model to the BS. In addition, consider that in the joint AI model training of the BS and the UE, information / data sets used for training are sent to each other.

[0053] However, although the migration of AI models / datasets requires the communication overhead of the wireless interface, there has been no research on an effective AI model / dataset migration method that also takes into account interoperability between vendors, etc. In addition, there has been no research on the implementation method of advanced functions such as online model updates / data migration for high-end terminals.

[0054] Several examples are described below. Figure 2This is a diagram showing an example of a file format for AI model migration. As shown in the figure, the current AI model frameworks include TensorFlow (registered trademark), Pytorch, Keras, TensorRT, Open Neural Network Exchange (ONNX), etc. In addition, there is more than one file format for each AI model framework. Each file format has different features (for example, if it is ckpt, the model architecture and weights can be migrated separately).

[0055] In addition, the file format may also correspond to a file extension, representing the structure (model, format) of multiple files. For example, the ckpt format may also be composed of a main file describing the structure of the model (extension such as meta), a data file of weight information (extension such as data-00000-of-00001), and an index file representing the relationship between each file and the step (extension such as index). In addition, the h5 (also known as the Hierarchical Data Format 5 (HDF5)) format may also be composed of a file representing the structure, weights, etc. of each layer of the AI ​​model.

[0056] In the present disclosure, information represented by a certain extension (file format) may also mean information represented by a form conforming to the file format (that is, the extension itself may also be arbitrary).

[0057] Figure 3A as well as Figure 3B This is a schematic diagram of the migration of a partial or complete AI model. This example is an example in which the BS trains a model and migrates the information of the trained model to the UE.

[0058] Figure 3A This is an example of migrating a complete AI model. The BS downloads information (e.g., 5 megabytes (MB)) of a template model (e.g., a model that determines the number of layers, what the input is, what the output is, etc.) in advance via the Internet. The BS uses its own (from scratch) data (e.g., information such as measurement results reported by the UE) to implement model training based on the template model, determine the weights of each layer, etc., and derive a model (trained model) that can perform appropriate reasoning.

[0059] The BS sends information (e.g., 5 MB) related to the trained model (or a program for implementing the model) to the UE. The UE may also deploy the trained model. The trained model may also be referred to as a common model, a base model, a reference model, etc., which is the basis of the updated model described later.

[0060] Figure 3B This is an example of the migration of some AI models. BS is sometimes updated Figure 3A For example, BS assumes Figure 3B The first three layers (shallow layers) of the model are frozen without updating (adjusting) the parameters (e.g., weights) associated with them, and only the last two layers (deep layers, corresponding to functions (fc)1 and fc2) are retrained. Such a model can also be called a fine-tuned model.

[0061] The BS transmits information on updated parameters for the retrained model to the UE (for example, in Python (registered trademark), this information may be represented by a dictionary, a data structure that associates keys with values) (for example, 10 kilo(K)B). Figure 3B In the example, this information indicates the values ​​of the parameters of the last two layers (fc1, fc2). The UE can also Figure 3A Describe the deployed model and apply the updated parameters to update the model.

[0062] according to Figure 3B Such a method of notifying only the information required for model update can appropriately reduce the load caused by gNB model update, communication overhead to UE, etc. Figure 3B The method can also be used with Figure 3A It is applied to speed up training when the time difference of training (also called full training) is small.

[0063] In order to determine (or deploy, update, etc.) the AI ​​model, information such as application model (which may include model architecture, files with parameters for training, etc.), application module (which may also include programs for training / inference), and data (dataset) is required.

[0064] These three entities can be managed separately. UE / BS can also use their respective identifiers (ID) to match (or associate) models, programs (codes), etc. These information can also be managed in a database.

[0065] Figure 4A-4C This is a diagram showing an example of matching between a model and a code. Figure 4AThis is an example of the association between the three entities mentioned above. In this example, the application module called Pytorch-rnn is associated with the application model called beam-management-time-domain / beam-management-xxx-domain. In addition, data corresponding to the application model (with the same name) can also be used.

[0066] Figure 4B An example of a database representing an application module is shown. Information associated with a module ID (eg, Git commit ID, module source Uniform Resource Locator (URL), framework, model architecture) is shown.

[0067] Figure 4C An example of a database showing an application model is shown. Information associated with a model ID (for example, the source URL of the model, module ID, data ID) is shown.

[0068] In addition, in the present disclosure, a source URL (also simply referred to as a source) may also mean a URL for uploading (sending) / downloading (acquiring) information related to a module / model, etc.

[0069] As shown above, although there are various model formats, model and code matching methods, etc., if the above-mentioned migration methods, implementation methods, etc. are not properly specified, appropriate overhead reduction / appropriate reasoning based on AI models / efficient resource utilization cannot be achieved, and there is a concern that communication throughput / communication quality improvement may be suppressed.

[0070] Therefore, the inventors of the present invention have come up with an appropriate control method for model migration. In addition, each embodiment of the present disclosure can also be applied without using AI / prediction.

[0071] In one embodiment of the present disclosure, a terminal (terminal, user terminal, user equipment (UE)) / base station (Base Station (BS)) trains an ML model in a training mode and implements the ML model in an inference mode (also referred to as inference mode, inference mode, etc.). In the inference mode, the accuracy of the ML model trained in the training mode can also be verified (Validation).

[0072] In the present disclosure, UE / BS can also input channel state information, reference signal measurement values, etc. into the ML model, and output high-precision channel state information / measurement values / beam selection / position, future channel state information / radio link quality, etc.

[0073] In addition, in the present disclosure, AI can also be rewritten as an object (also referred to as object, object, data, function, program, etc.) having (implementing) at least one of the following characteristics:

[0074] Estimates based on observed or collected information;

[0075] · Selection based on observed or collected information;

[0076] Predictions based on observed or collected information.

[0077] In the present disclosure, an object may also be, for example, a device, such as a terminal or a base station, etc. In addition, in the present disclosure, an object may also correspond to a program / model / entity operating in the device.

[0078] In addition, in the present disclosure, the ML model may also be rewritten as an object having (implementing) at least one of the following features:

[0079] Producing estimates by feeding information;

[0080] ·Predict estimated values ​​by providing information;

[0081] Discover features by providing information;

[0082] · Select an action by providing information.

[0083] In addition, in the present disclosure, ML models, models, AI models, predictive analytics, predictive analytics models, etc. can also be rewritten to each other. In addition, ML models can also be derived using at least one of regression analysis (e.g., linear regression analysis, multivariate regression analysis, logistic regression analysis), support vector machine (Support Vector Machine), random forest (Random Forest), neural network (Neural Networks), deep learning (Deep Learning), etc. In the present disclosure, models can also be rewritten as at least one of encoders, decoders, tools, etc.

[0084] The ML model outputs at least one piece of information such as an estimated value, a predicted value, a selected operation, a classification, etc. based on the input information.

[0085] ML models can also include supervised learning, unsupervised learning, reinforcement learning, etc. Supervised learning can also be used to learn general rules for mapping inputs to outputs. Unsupervised learning can also be used to learn features of data. Reinforcement learning can also be used to learn operations for maximizing a goal.

[0086] In the present disclosure, the words "generate", "calculate", "derive", etc. may be replaced by each other. In the present disclosure, the words "implement", "run", "operate", "execute", etc. may be replaced by each other. In the present disclosure, the words "train", "learn", "update", "retrain", etc. may be replaced by each other. In the present disclosure, the words "inference", "after-training", "formal use", "actual use", etc. may be replaced by each other. The words "signal" and "signal / channel" may be replaced by each other.

[0087] In the present disclosure, the training mode may also correspond to a mode in which the UE / BS sends / receives signals for the ML model (in other words, an operation mode during training). In the present disclosure, the inference mode may also correspond to a mode in which the UE / BS implements the ML model (e.g., implements the trained ML model to predict output) (in other words, an operation mode during inference).

[0088] In the present disclosure, the training mode may also mean a mode in which a specific signal with high overhead (eg, large amount of resources) is transmitted with respect to a specific signal transmitted in the inference mode.

[0089] In the present disclosure, the training mode may also mean a mode with reference to a first setting (e.g., a first DMRS setting, a first CSI-RS setting, a first CSI report setting). In the present disclosure, the inference mode may also mean a mode with reference to a second setting different from the first setting (e.g., a second DMRS setting, a second CSI-RS setting, a second CSI report setting). The first setting may also be set more than the second setting in terms of at least one of the time resources, frequency resources, code resources, and ports (antenna ports) related to the measurement. In addition, for example, the CSI report setting may also include settings related to the autoencoder.

[0090] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The wireless communication methods according to the embodiments may be applied individually or in combination.

[0091] In the present disclosure, "A / B" and "at least one of A and B" may be replaced with each other. In addition, in the present disclosure, "A / B / C" may also mean "at least one of A, B, and C".

[0092] In the present disclosure, activate, deactivate, indicate (or specify), select, configure, update, determine, etc. may also be mutually rewritten. In the present disclosure, support, control, controllable, operate, and can operate may also be mutually rewritten.

[0093] In the present disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, high-layer parameters, fields, Information Element (IE), settings, etc. may also be overwritten with each other. In the present disclosure, Medium Access Control (MAC) control elements (MAC Control Element (CE)), update commands, activation / deactivation commands, etc. may also be overwritten with each other.

[0094] 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, etc., or a combination thereof.

[0095] In the present disclosure, MAC signaling may also use, for example, MAC Control Element (MACCE), MAC Protocol Data Unit (MAC Protocol Data Unit (PDU)), etc. Broadcast information may also be, for example, Master Information Block (MIB), System Information Block (SIB), minimum system information (Remaining Minimum System Information (RMSI)), Other System Information (Other System Information (OSI)), etc.

[0096] In the present disclosure, the physical layer signaling may be, for example, downlink control information (Downlink Control Information (DCI)), uplink control information (Uplink Control Information (UCI)), etc.

[0097] In the present disclosure, index, identifier (ID), indicator, resource ID, etc. may also be mutually overwritten. In the present disclosure, sequence, list, set, group, cluster, subset, etc. may also be mutually overwritten.

[0098] In the present disclosure, estimation, prediction, and inference may be replaced by each other. In addition, in the present disclosure, estimation, prediction, and inference may be replaced by each other.

[0099] In the present disclosure, the autoencoder, encoder, decoder, etc. can also be rewritten as at least one of a model, an ML model, a neural network model, an AI model, an AI algorithm, etc. In addition, the autoencoder can also be rewritten with any autoencoder such as a stacked autoencoder (StackedAutoencoder) and a convolutional autoencoder. The encoder / decoder of the present disclosure can also adopt models such as a residual network (Residual Network (ResNet)), DenseNet, and RefineNet.

[0100] In addition, in the present disclosure, encoders, encoding processes, encoding, and corrections, changes, and controls based on encoders may be replaced with each other. In addition, in the present disclosure, decoders, decoding processes, decoding, and corrections, changes, and controls based on decoders may be replaced with each other.

[0101] In the present disclosure, bits, bit strings, bit sequences, sequences, values, information, values ​​obtained from bits, information obtained from bits, etc. may also be replaced with each other.

[0102] In the present disclosure, the layers (for the encoder) may also be mutually rewritten with the layers (input layer, intermediate layer, etc.) used in the AI ​​model. The layers of the present disclosure may also be equivalent to at least one of the input layer, intermediate layer, output layer, batch normalization layer, convolution layer, activation layer, dense layer, normalization layer, pooling layer, attention layer, random hidden layer (dropout layer), fully connected layer, etc.

[0103] In the present disclosure, deploy, be able to utilize, equip, deploy, etc. may also be rewritten mutually.

[0104] In the present disclosure, the data server may also be rewritten as a repository, uploader, library, cloud server, or simply rewritten to a server, etc. In addition, the data server in the present disclosure may be provided by any platform such as GitHub (registered trademark), and may be operated by any enterprise / organization.

[0105] (Wireless Communication Method)

[0106] <First Embodiment>

[0107] The first embodiment relates to a model transfer category. The model transfer category may be represented as a type X (X is an integer), for example, and may also include at least one of the following:

[0108] Category 0 (supports methods that minimize air interface signals);

[0109] Category 1 (supporting partial model migration via the air interface);

[0110] Category 2 (supports partial or complete model migration via the air interface);

[0111] • Category 3 (ways to support migration of data sets for training / validation / testing via the air interface).

[0112] In addition, the air interface may also refer to the (NR) wireless communication interface between UE and BS, and the air interface signal may also refer to the signal transmitted using the air interface.

[0113] The determination of which category the BS / UE operates based on will be described in the third embodiment.

[0114] [Implementation 1.1: Category 0 operation]

[0115] Category 0 BS / UE deploys a model obtained from a third party (data server). In implementation 1.1, the details of the model, the method of obtaining the model, etc. may be specified outside the scope of 3GPP. For example, a specific vendor / operator / AI team uploads / downloads the AI ​​model to / from the data server. The BS / UE may also deploy a model represented by the model information received from the data server.

[0116] Model information may also mean information including at least one of the following:

[0117] Information about the input / output of the AI ​​model (which may also include meta-information);

[0118] Information for pre-processing / post-processing of AI model input / output;

[0119] Information about the parameters of the AI ​​model;

[0120] Training information for AI models;

[0121] Reasoning information for AI models;

[0122] Performance information related to AI models;

[0123] An index used to identify the AI ​​model (e.g., model ID).

[0124] When BS / UE downloads (receives) model information from a data server, it can download images (images, images) (for example, Docker (registered trademark) images (images, images), Anaconda images (images, images)) from the data server / other servers. BS / UE can also start (construct, utilize) the execution environment of the AI ​​model based on the image (also referred to as a container. For example, a virtual environment that can execute Python and contains the packages (packages) / libraries (libraries) required for the AI ​​model), and can also generate an executable AI model in the container.

[0125] The UE may also use the UE capability information to report the AI ​​capability to the NW (BS). The capability may be, for example, information related to the characteristics of the AI ​​model that the UE can deploy, or may represent, for example, at least one of the following:

[0126] The maximum floating point operations (FLOPs (s is a lowercase letter)) of the AI ​​model that the UE can deploy (this means the number of floating point operations);

[0127] The maximum number of parameters of the AI ​​model that the UE can deploy;

[0128] The minimum granularity of the weight information (the value represented by this) supported by the UE;

[0129] UE supported layers / algorithms / functions.

[0130] The BS and the UE may also exchange (send / receive) information for AI model determination (hereinafter also referred to as model determination information) via RRC signaling. The model determination information may also include at least one information of the source of the AI ​​model (e.g., Uniform Resource Locator (URL)), the model type (e.g., usage (in other words, purpose, function)), and the index information of the model file (e.g., the hash value of the model file (message digest algorithm 5 (MD5) code, etc.)).

[0131] The BS may also configure the UE to utilize (or compile) the model when the model is accurately deployed based on signaling.

[0132] In addition, in the present disclosure, "compile" can also be rewritten with setting / being set, activating / being activated, enabling / being enabled, recording / being recorded, etc. In addition, in the present disclosure, "compiling a certain AI model" can also be rewritten as "making it possible to apply reasoning using a certain AI model", "applying the compiled AI model", etc.

[0133] That is, the UE determines that although it can perform inference using the compiled AI model, it cannot perform inference using the uncompiled AI model.

[0134] Figure 5 This is a diagram showing an example of the flow of Embodiment 1.1. In addition, regarding the steps, the order is not limited to the order shown in the figure, and some steps may be omitted (the same applies to the subsequent flows).

[0135] Figure 5 The flowchart represents an example in which the UE determines the model it wants to use and sends model determination information for the model.

[0136] In steps S101 and S102, the BS / UE downloads (receives) model information for itself from the data server and deploys the model.

[0137] In step S103, the UE determines the model that the UE wants to use, and sends model determination information related to the model to the BS.

[0138] In step S104, the BS determines whether it can use the model specified by the model specification information. For example, if the model cannot be used, the BS obtains information for the model from the data server and updates / deploys the model.

[0139] In step S105, the BS transmits information for enabling the model determined by the model determination information in the UE, and information related to the corresponding configuration (eg, CSI configuration (CSI report configuration / CSI resource configuration / CSI-RS resource configuration)) to the UE.

[0140] After step S105 , the UE may control the BS to transmit the inference result using the model specified by the model specifying information.

[0141] Figure 6 This is a diagram showing an example of the process of implementation mode 1.1. Figure 6 The process represents the following example: the BS determines the model used (should be used) by the UE based on the capability of the UE, and sends model determination information for the model.

[0142] Step S201 is the same as step S101.

[0143] In step S202, the UE reports its AI capability to the BS using the UE capability information.

[0144] In step S203, the BS confirms the capability for AI and determines the model to be used by the UE. In addition, as needed, the BS may also obtain information for the model from the data server and update / deploy the model.

[0145] In step S204, the BS transmits model determination information related to the model in step S203 to the UE.

[0146] In step S205, the UE may download model information for the model determined by the model determination information from the data server, or search and obtain from pre-downloaded model information. The UE deploys the model based on the model information.

[0147] In step S206, the UE reports information indicating that the preparation for model deployment has been completed (or has been deployed) to the BS. The information may include at least a part of the model determination information, and may also be implicitly understood as corresponding to the latest model determination information.

[0148] Step S207 is the same as step S105. In addition, the model activated in step S207 corresponds to a model that is explicitly / implicitly indicated to have been deployed by the above-mentioned information indicating that the preparation for model deployment has been completed (or has been deployed).

[0149] [Implementation 1.2: Category 1 operation]

[0150] Category 1 BS / UE exchange partial model information, but not the minimum information like category 0 model determination information.

[0151] The information of the partial model may include information of the network structure (architecture) used for the model, and may also include weight information of the network. In addition, in the present disclosure, the network architecture may also be rewritten as a model architecture, etc.

[0152] The BS / UE that exchanges network structure information may also be referred to as a Category 1-1 BS / UE. The BS / UE that exchanges network weight information may also be referred to as a Category 1-2 BS / UE.

[0153] The network structure information may also include at least one of the following:

[0154] (Implementation 1.2.1) Hyperparameters corresponding to indexes of network structures

[0155] Value information;

[0156] (Implementation 1.2.2) Network (block) combination information;

[0157] (Implementation 1.2.3) Information for specifying a network structure (for example, a code describing the network structure based on a specific programming (description) language, a virtual code, for example, Python code).

[0158] For implementation 1.2.1, one or more network structures may be determined together with hyperparameters that can be set in UE / BS. In addition, hyperparameters are parameters determined (set) before training / inference, such as the number of layers, the number of nodes in a certain layer, and the learning rate.

[0159] For implementation 1.2.2, UE / BS can combine multiple networks to form a more complex network, and can also exchange combination information related to the combined network. In addition, a network that can be combined can also be called a block, and the information about the network structure of implementation 1.2.2 can also include information about the block. The information about the block can also be information about the value of a hyperparameter corresponding to the index of the block, similar to implementation 1.2.1.

[0160] For implementation 1.2.3, UE / BS can also implement any network structure based on pseudocode / Phthon code. In addition, the information used to specify the network structure of implementation 1.2.3 can also be the code itself, or binary data representing the code, or information that reproduces the code (for example, when several codes are pre-defined (the correspondence between code indexes and codes is specified) , One or more code indexes).

[0161] In order to implement 1.2.1 / 1.2.2, the UE / BS may understand the correspondence between the network structure index and the network structure and corresponding hyper parameters based on specifications (eg, 3GPP specifications) (ie, the UE / BS may also pre-store information on the correspondence).

[0162] For implementation 1.2.3, UE / BS may also understand the programming (description) language describing the network structure and the corresponding platform based on specifications (e.g., 3GPP specifications) (i.e., UE / BS can understand the language and implement processing based on the language).

[0163] Fig. 7A as well as Figure 7B This is a diagram showing an example of a network structure determined by the information on the network structure in Implementation 1.2.1.

[0164] Fig. 7A Represents the structure of network #1 corresponding to index #1. Network #1 is a structure whose input is processed in the order of convolution layer, batch normalization layer, leaky ReLU layer, linear function (identity function) layer, and sigmoid function layer, and then the output is processed. The corresponding hyperparameters may also include at least one of the following:

[0165] The dimensions of the input / output of the convolutional layer;

[0166] The kernel size of the convolutional layer;

[0167] Negative gradient coefficients of leaky ReLU layers;

[0168] The dimensions of the input / output of the linear function layer.

[0169] Figure 7B Represents the structure of network #2 corresponding to index #2. Network #2 is a structure whose input is processed in the order of a linear function (identity function) layer, a batch normalization layer, and a leaky ReLU layer, and then the output is processed. The corresponding hyperparameters may also include at least one of the following:

[0170] The dimensions of the input / output of the linear function layer;

[0171] Negative gradient coefficients for leaky ReLU layers.

[0172] Figure 8A-8C This is a diagram showing an example of a network structure determined by the information on the network structure in Implementation 1.2.2.

[0173] Fig. 8ARepresents the structure of block #1 corresponding to index #1. Block #1 is a structure whose input is processed in the order of convolutional layer, batch normalization layer, and leaky ReLU layer and then output. The corresponding hyperparameters may also include at least one of the following:

[0174] The dimensions of the input / output of the convolutional layer;

[0175] The kernel size of the convolutional layer;

[0176] Negative gradient coefficients for leaky ReLU layers.

[0177] Figure 8B Represents the structure of network #2 corresponding to index #2. Block #2 is a structure whose input is processed in the order of a linear function (identity function) layer, a batch normalization layer, and a leaky ReLU layer, and then the output is processed. The corresponding hyperparameters may also include at least one of the following:

[0178] The dimensions of the input / output of the linear function layer;

[0179] Negative gradient coefficients for leaky ReLU layers.

[0180] Figure 8C This shows an example of a network structure determined by using combination information. The combination information in this example is index #2, #1, and #2. In addition, when there are multiple blocks with the same index as in this example, the values ​​of the corresponding hyperparameters may be at least partially different or all the same.

[0181] Fig. 9 This is a diagram showing an example of the process of implementation mode 1.2.1 / 1.2.2.

[0182] In step S301, the UE sends information about the network structure (eg, information about a hyperparameter value corresponding to an index of the network structure) to the BS.

[0183] In step S302, the BS notifies the UE of signaling for indicating deployment of a network corresponding to the above-mentioned network structure information (having a model of the network).

[0184] In step S303, the UE reports to the BS information indicating that the model deployment preparation (or deployment) has been completed. The information may include index / combination information of the network structure, or may be implicitly understood as the information corresponding to the latest sent network structure information (the represented network).

[0185] Step S304 is the same as step S207.

[0186] Fig.10 This is a diagram showing an example of the process of implementation mode 1.2.3.

[0187] In step S401, the UE transmits network configuration information (eg, a PHP code describing the network configuration) to the BS.

[0188] Steps S402-S404 may also be the same as steps S302-S304.

[0189] [Implementation 1.3: Category 2 operation]

[0190] Category 2 BS / UE exchanges partial or complete model information.

[0191] This information (model determination information) may be information represented by an existing model file format (e.g., with an extension of onnx, bin, tflite, etc.), or information represented by other file formats (e.g., with an extension of json, csv, tsv, xml, etc.). Information represented by the other file formats may also be used to exchange information about some parameters. For example, when the UE / BS updates the model parameters of a certain model, the UE / BS may also notify the information about the parameters updated using the other file formats.

[0192] Fig.11 This is a diagram showing an example of a flowchart of implementation mode 1.3.

[0193] Step S501 may also be the same as step S202. Here, the capability information for AI may also indicate supported file formats.

[0194] In step S502, the BS confirms the capability for AI, for example, by sending model confirmation information for at least a part of a certain model represented by a file format (for example, ONNX format) supported by the UE to the UE in the PDSCH.

[0195] In step S503, the UE reports information indicating that the preparation for model deployment has been completed (or has been deployed) to the BS. The information may include at least a part of the model determination information, and may also be implicitly understood as corresponding to the latest model determination information.

[0196] Step S504 may also be the same as step S207.

[0197] [Implementation 1.4: Category 3 operation]

[0198] Category 3 BS / UE may also exchange information of data sets for training / validation / testing.

[0199] This information (which may also be referred to as dataset information) may also include at least one of the source of the dataset (eg, URL) and index information (eg, hash value (MD5 code, etc.) of the dataset file).

[0200] The data set information may also be information represented by a file format (for example, extension csv) used for an existing data file.

[0201] The dataset information may also include at least one of the following information: information indicating the purpose for which the dataset will be used (e.g., training / validation / testing), information indicating which model the dataset will be used for (e.g., the model determination information described in the above embodiment), etc.

[0202] Fig.12 This is a diagram showing an example of the process of implementation mode 1.4.

[0203] Step S601 may also be the same as step S202. Here, the capability information for AI may also indicate the format of the supported data set and the like.

[0204] In step S602, the BS confirms the capability for AI, for example, by sending updated data set information for a certain model represented by a file format supported by the UE to the UE.

[0205] In step S603, the UE may also download a data set determined by the data set information from the data server, and based on the data set, the UE updates / deploys a model corresponding to the data set.

[0206] In step S604, the UE sends model determination information for at least a part of the above model represented by a specific file format (eg, ONNX format) to the BS, for example, in PUSCH.

[0207] Step S605 is the same as step S105.

[0208] According to the first embodiment described above, the UE / BS can appropriately control the model migration based on the model migration type.

[0209] <Second Embodiment>

[0210] The second embodiment relates to a unit (entity) for processing model migration, namely a Model Management Unit (MMU). In addition, the MMU can also be mutually rewritten with the Model Transfer Unit (MTU), the Model Transfer Management Unit (MTMU), etc.

[0211] The MMU may also be defined in both the BS and the UE. The functions of the MMU may also include one or more of the following.

[0212] Process the corresponding RRC / MAC / PHY layer signals;

[0213] Packing / unpacking and sending / receiving of AI models (via user plane data or RRC signaling);

[0214] or downloading / uploading of AI models (e.g., from / to a data server);

[0215] Deployment / update of AI models;

[0216] Packing / unpacking and sending / receiving of data sets; and training of networks using the data sets;

[0217] Produce the corresponding container based on the downloaded image.

[0218] In addition, packing can also mean converting a certain data (structure) into binary, and unpacking can also mean converting binary into a certain data (structure).

[0219] Fig.13A as well as Fig. 13B This is a diagram showing an example of the position of the MMU in the protocol stack according to the second embodiment.

[0220] The NR user (User (U)) plane protocol stack is constructed from bottom to top to include the physical (Physical (PHY)) layer, MAC layer, packet data convergence protocol (Packet Data Convergence Protocol (PDCP)) layer, and radio link control (Radio Link Control (RLC)) layer. In addition, for simplicity, the upper layers of PDCH are omitted.

[0221] The NR control (Control (C)) plane protocol stack is constructed to include a PHY layer, a MAC layer, a PDCP layer, an RLC layer, and an RRC layer from bottom to top. In addition, the layers above the RRC are omitted for simplicity.

[0222] In this example, it is assumed that the AI ​​model is installed in the PHY / MAC layer (controlled / processed in the PHY / MAC layer).

[0223] exist Fig.13A In the present invention, MMU is defined as an independent sublayer of the third layer same as RRC, capable of processing both control plane signals and user plane data, and capable of controlling the model installed in the PHY / MAC layer.

[0224] exist Fig. 13B In the present invention, the MMU is defined as a functional unit of the RRC, which is capable of forwarding all messages related to the model migration including the model data through the RRC signaling.

[0225] BS and UE can also use MMU as Fig.13A / 13B protocol stack to control processing related to the AI ​​model.

[0226] According to the second embodiment described above, the MMU can be appropriately specified, and the UE / BS can appropriately handle the model migration.

[0227] <Third Embodiment>

[0228] The third embodiment relates to signaling notification between UE and BS and corresponding UE operation. The description is based on the model transition type described in the first embodiment.

[0229] For all categories, at least one of the following may also be used:

[0230] The UE receives an indication command of model information from the BS via DL signaling. The model information may also be the model determination information described in the first embodiment. After receiving the indication command, the UE may also deploy the downloaded or searched model;

[0231] The UE may also send a model preparation completion / non-correspondence command to the BS via UL signaling. The model preparation completion command may also be information indicating that the preparation for model deployment has been completed (or deployed) as described in the first embodiment. The UE may also send a non-correspondence command when it does not support / cannot correspond to the specified model;

[0232] The UE receives a model activation / deactivation command from the BS via DL signaling. The activation command may also be information for activating the model determined by the model determination information in the UE as described in the first embodiment. After receiving the activation command, the UE may also start using the indicated model for reasoning. In addition, after receiving the deactivation command, the UE may also,

[0233] Stop using the indicated model for inference.

[0234] For category 1-1, at least one of the following may also be used:

[0235] · File formats that define network structures;

[0236] • Network structured files are migrated as user plane data and processed by the MMU on PDCP, or migrated / processed as part of RRC signaling in both DL and UL.

[0237] For categories 1-2 or category 2, at least one of the following may also be used:

[0238] Define the file format of the AI ​​model (ONNX, etc.);

[0239] The AI ​​model file is migrated as user plane data and processed by the MMU on PDCP, or migrated / processed as part of RRC signaling in both DL and UL.

[0240] For category 3, the following can also be used:

[0241] The UE receives a data set indication command from the BS via DL signaling. The model information may also be the data set information described in the first embodiment. After receiving the indication command, the UE may also train / update the AI ​​model using the downloaded or searched data set.

[0242] In addition, the UE / BS may also be notified of which category to comply with through other devices (BS / UE / OAM / LMF, etc.).

[0243] According to the third embodiment described above, signaling or corresponding UE operations can be appropriately controlled in association with the model migration category.

[0244] <Supplement>

[0245] The notification of arbitrary information to the UE (from the NW) in the above-mentioned implementation manner (in other words, the reception of arbitrary information from the base station in the UE) can be carried out using physical layer signaling (e.g., DCI), high-layer signaling (e.g., RRC signaling, MACCE), specific signals / channels (e.g., PDCCH, PDSCH, reference signals), or a combination thereof.

[0246] When the above notification is performed through MAC CE, the MAC CE may be identified by including a new logical channel ID (Logical Channel ID (LCID)) not specified in the existing specification in the MAC header.

[0247] When the above notification is performed through DCI, the above notification can also be performed through a specific field of the DCI, a Radio Network Temporary Identifier (RNTI) used in scrambling the cyclic redundancy check (CRC) bits assigned to the DCI, the format of the DCI, etc.

[0248] In addition, notification of any information in the above-mentioned embodiments to the UE may be performed periodically, semi-continuously or aperiodically.

[0249] The notification of arbitrary information from the UE (to the NW) in the above-mentioned embodiment (in other words, the sending of arbitrary information from the UE to the base station) can also be carried out using physical layer signaling (e.g., UCI), high-layer signaling (e.g., RRC signaling, MAC CE), specific signals / channels (e.g., PUCCH, PUSCH, reference signals), or a combination thereof.

[0250] When the above notification is performed through MAC CE, the MAC CE can also be identified by including a new LCID that is not specified in the existing specifications in the MAC header.

[0251] When the notification is performed through UCI, the notification may be sent through PUCCH or PUSCH.

[0252] In addition, the notification of arbitrary information from the UE in the above-mentioned embodiments may be performed periodically, semi-continuously, or aperiodically.

[0253] The encoder / decoder in the above-mentioned implementation mode may also be rewritten with the AI ​​model equipped in the UE / base station. That is to say, the present disclosure is not limited to the case of using an autoencoder, but may also be applied to the case of using any model for reasoning. In addition, the object compressed by the UE / base station of the present disclosure using the encoder is not limited to CSI (or channel / precoding matrix), but may also be any information.

[0254] At least one of the above-mentioned embodiments may also be applied only to a UE that reports a specific UE capability (UE capability) or supports the specific UE capability.

[0255] The specific UE capability may also represent at least one of the following:

[0256] Supporting specific processing / operation / control / information for at least one of the above embodiments;

[0257] Support for specific AI capabilities (e.g., supported model types, model sizes, model identification information);

[0258] Support AI model migration (e.g., categories 0-3).

[0259] In addition, the above-mentioned specific UE capabilities can be capabilities that are applied across all frequencies (regardless of frequency, publicly), or they can be capabilities by frequency (for example, one or a combination of cells, bands, BWPs, band combinations, component carriers, etc.), or they can be capabilities by frequency range (for example, Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or they can be capabilities by subcarrier spacing (SubCarrier Spacing (SCS)).

[0260] In addition, the above-mentioned specific UE capabilities can be capabilities applied across full-duplex mode (regardless of the duplex mode and are commonly used), or can be capabilities applied in duplex mode (for example, time division duplex (TDD) and frequency division duplex (FDD)).

[0261] In addition, at least one of the above embodiments may also be applied when the UE is set with specific information associated with the above embodiments through high-level signaling. For example, the specific information may also be information indicating the use of the activated AI model, any RRC parameter for a specific version (e.g., Rel.18), etc.

[0262] The UE may also apply operations such as Rel.15 / 16 without supporting at least one of the above-mentioned specific UE capabilities or without being set with the above-mentioned specific information.

[0263] In addition, at least one of the above-mentioned embodiments may also be used for transmission (compression) of information between UE-BS other than CSI feedback. For example, the UE may also report information related to position (or positioning) / information related to position estimation in LMF to the network according to at least one of the above-mentioned embodiments (for example, generated by an encoder). The information may also be information on the channel impulse response (Channel Impulse Response (CIR)) per subband / per antenna port. If it is reported, the base station can estimate the position of the UE even if the angle / time difference of the received signal is not reported.

[0264] (Note)

[0265] The following inventions are added to one embodiment of the present disclosure.

[0266] [Note 1]

[0267] A terminal having:

[0268] A receiving unit receives information for determining an artificial intelligence (AI) model based on the model transfer category; and

[0269] A control unit performs inference based on the AI ​​model, wherein the AI ​​model is determined based on the information.

[0270] [Note 2]

[0271] A terminal as described in Appendix 1, wherein:

[0272] The information includes a source Uniform Resource Locator (URL) of the AI ​​model or index information of a file used for the AI ​​model.

[0273] [Note 3]

[0274] A terminal as described in Supplement 1 or Supplement 2, wherein:

[0275] The information may also include information about the network structure used for the AI ​​model.

[0276] [Note 4]

[0277] A terminal as described in any one of Notes 1 to 3, wherein:

[0278] The control unit controls processing related to the AI ​​model based on a protocol stack utilizing a model management unit for processing model migration.

[0279] (Wireless Communication System)

[0280] Hereinafter, a configuration of a wireless communication system according to an embodiment of the present disclosure will be described. In the wireless communication system, communication is performed using any one of the wireless communication methods according to the above-mentioned embodiments of the present disclosure or a combination thereof.

[0281] Fig.14 1 is a diagram showing an example of a schematic structure of a wireless communication system involved in an embodiment. The wireless communication system 1 (may also be referred to as the system 1) may also be a system that implements communication using Long Term Evolution (LTE) standardized by the Third Generation Partnership Project (3GPP), the fifth generation mobile communication system New Radio (5G NR), and the like.

[0282] In addition, the wireless communication system 1 can also support dual connectivity (Multi-RAT Dual Connectivity (MR-DC)) between multiple radio access technologies (Radio Access Technology (RAT)). MR-DC can also include dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA))) and NR (E-UTRA-NR Dual Connectivity (E-UTRA-NR Dual Connectivity (EN-DC))), dual connectivity between NR and LTE (NR-E-UTRA Dual Connectivity (NR-E-UTRA Dual Connectivity (NE-DC))), etc.

[0283] In EN-DC, the base station (eNB) of LTE (E-UTRA) is the master node (Master Node (MN)), and the base station (gNB) of NR is the secondary node (Secondary Node (SN)). In NE-DC, the base station (gNB) of NR is the MN, and the base station (eNB) of LTE (E-UTRA) is the SN.

[0284] The wireless communication system 1 can also support dual connectivity between multiple base stations within the same RAT (for example, dual connectivity (NR-NR Dual Connectivity (NN-DC)) where both MN and SN are NR base stations (gNB)).

[0285] The wireless communication system 1 may also include a base station 11 that forms a macro cell C1 with a relatively wide coverage, and a base station 12 (12a-12c) that is configured in the macro cell C1 and forms a small cell C2 that is narrower than the macro cell C1. The user terminal 20 may also be located in at least one cell. The configuration and number of each cell and user terminal 20 are not limited to the method shown in the figure. Hereinafter, when the base stations 11 and 12 are not distinguished, they are collectively referred to as base stations 10.

[0286] The user terminal 20 may be connected to at least one of the plurality of base stations 10. The user terminal 20 may use at least one of carrier aggregation (CA) using a plurality of component carriers (CC) and dual connectivity (DC).

[0287] Each CC may also be included in at least one of the first frequency band (Frequency Range 1 (FR1)) and the second frequency band (Frequency Range 2 (FR2)). The macro cell C1 may also be included in FR1, and the small cell C2 may also be included in FR2. For example, FR1 may be a frequency band below 6 GHz (below 6 GHz (sub-6 GHz)), and FR2 may be a frequency band higher than 24 GHz (above-24 GHz). In addition, the frequency bands and definitions of FR1 and FR2 are not limited to these. For example, FR1 may also be equivalent to a frequency band higher than FR2.

[0288] Furthermore, the user terminal 20 may perform communication in each CC using at least one of time division duplex (TDD) and frequency division duplex (FDD).

[0289] Multiple base stations 10 may also be connected via wired (e.g., optical fiber based on Common Public Radio Interface (CPRI)), X2 interface, etc.) or wireless (e.g., NR communication). For example, when NR communication between base stations 11 and 12 is used as a backhaul, the base station 11 equivalent to the upper station may also be referred to as an Integrated Access Backhaul (IAB) donor, and the base station 12 equivalent to a relay station (relay) may also be referred to as an IAB node.

[0290] The base station 10 may also be connected to the core network 30 via other base stations 10 or directly. The core network 30 may also 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.

[0291] The core network 30 may also include, for example, user plane functions (User Plane Function (UPE)), access and mobility management function (Access and Mobility management Function (AMF)), session management function (Session Management Function (SMF)), unified data management (Unified Data Management (UDM)), application function (Application Function (AF)), data network (Data Network (DN)), location management function (Location Management Function (LMF)), maintenance and operation management (OAM) and other network functions (Network Functions (NF)). In addition, multiple functions may be provided by one network node. In addition, communication with an external network (e.g., the Internet) may also be performed via the DN.

[0292] The user terminal 20 may also be a terminal that supports at least one of communication modes such as LTE, LTE-A, and 5G.

[0293] In the wireless communication system 1, a wireless access method based on orthogonal frequency division multiplexing (OFDM) may be used. For example, in at least one of the downlink (DL) and the uplink (UL), cyclic prefix OFDM (CP-OFDM), discrete Fourier transform spread OFDM (DFT-s-OFDM), orthogonal frequency division multiple access (OFDMA), single carrier frequency division multiple access (SC-FDMA), etc. may be used.

[0294] The radio access scheme may also be referred to as a waveform. In addition, in the wireless communication system 1, other radio access schemes (for example, other single-carrier transmission schemes, other multi-carrier transmission schemes) may be used as the radio access schemes for UL and DL.

[0295] As downlink channels, the wireless communication system 1 may use 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.

[0296] In addition, as uplink channels, 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)) and the like can also be used in the wireless communication system 1.

[0297] User data, high-layer control information, system information block (SIB), etc. are transmitted through PDSCH. User data, high-layer control information, etc. can also be transmitted through PUSCH. In addition, master information block (MIB) can also be transmitted through PBCH.

[0298] The lower layer control information may also be transmitted via the PDCCH. The lower layer control information may include, for example, downlink control information (Downlink Control Information (DCI)) including scheduling information of at least one of the PDSCH and the PUSCH.

[0299] In addition, the DCI for scheduling the PDSCH may also be referred to as DL allocation, DL DCI, etc., and the DCI for scheduling the PUSCH may also be referred to as UL grant, UL DCI, etc. In addition, the PDSCH may also be rewritten as DL data, and the PUSCH may also be rewritten as UL data.

[0300] In the detection of PDCCH, the control resource set (CORESET) and the search space can also be used. CORESET corresponds to the resources for searching DCI. The search space corresponds to the search area and search method of PDCCH candidates. A CORESET can also be associated with one or more search spaces. The UE can also monitor the CORESET associated with a search space based on the search space setting.

[0301] A search space may also correspond to a PDCCH candidate corresponding to one or more aggregation levels. One or more search spaces may also be referred to as a search space set. In addition, the "search space", "search space set", "search space setting", "search space set setting", "CORESET", "CORESET setting" and the like in the present disclosure may also be rewritten mutually.

[0302] Uplink control information (uplink control information (UCI)) including at least one of channel state information (CSI), delivery confirmation information (e.g., also known as hybrid automatic repeat request confirmation (HARQ-ACK), ACK / NACK, etc.), and scheduling request (SR) can also be transmitted through PUCCH. The random access preamble used to establish a connection with a cell can also be transmitted through PRACH.

[0303] In the present disclosure, downlink, uplink, etc. may be expressed without the word “link.” In addition, various channels may be expressed without the word “physical” at the beginning.

[0304] In the wireless communication system 1, a synchronization signal (Synchronization Signal (SS)), a downlink reference signal (Downlink Reference Signal (DL-RS)), etc. can also be transmitted. As DL-RS, in the wireless communication system 1, a cell-specific reference signal (Cell-specific Reference Signal (CRS)), a channel state information reference signal (Channel State Information Reference Signal (CSI-RS)), a demodulation reference signal (DeModulation Reference Signal (DMRS)), a positioning reference signal (Positioning Reference Signal (PRS)), a phase tracking reference signal (Phase Tracking Reference Signal (PTRS)), etc. can also be transmitted.

[0305] The synchronization signal may be, for example, at least one of a primary synchronization signal (PSS) and a secondary synchronization signal (SSS). A signal block including SS (PSS, SSS) and PBCH (and DMRS for PBCH) may also be referred to as SS / PBCH block, SS block (SSB), etc. In addition, SS, SSB, etc. may also be referred to as reference signals.

[0306] In addition, in the wireless communication system 1, as an uplink reference signal (Uplink Reference Signal (UL-RS)), a measurement reference signal (Sounding Reference Signal (SRS)), a demodulation reference signal (DMRS), etc. can also be transmitted. In addition, DMRS can also be called a user terminal specific reference signal (UE-specific Reference Signal).

[0307] (Base Station)

[0308] Fig.151 is a diagram showing an example of a structure of a base station involved in one embodiment. The base station 10 includes a control unit 110, a transmitting and receiving unit 120, a transmitting and receiving antenna 130, and a transmission path interface (transmission line interface) 140. In addition, the control unit 110, the transmitting and receiving unit 120, the transmitting and receiving antenna 130, and the transmission path interface 140 may each be provided with more than one.

[0309] In addition, in this example, the functional blocks of the characteristic parts in this embodiment are mainly shown, and it is also assumed that the base station 10 also has other functional blocks required for wireless communication. Part of the processing of each unit described below may also be omitted.

[0310] The control unit 110 controls the entire base station 10. The control unit 110 can be composed of a controller, a control circuit, and the like that are described based on common knowledge in the technical field to which the present disclosure relates.

[0311] The control unit 110 may also control signal generation, scheduling (e.g., resource allocation, mapping), etc. The control unit 110 may also control transmission and reception, measurement, etc. using the transmission and reception unit 120, the transmission and reception antenna 130, and the transmission path interface 140. The control unit 110 may also generate data, control information, sequences, etc. to be sent as signals, and forward them to the transmission and reception unit 120. The control unit 110 may also perform call processing (setting, release, etc.) of communication channels, state management of the base station 10, management of wireless resources, etc.

[0312] The transmitting and receiving unit 120 may also include a baseband unit 121, a radio frequency (RF) unit 122, and a measuring unit 123. The baseband unit 121 may also include a transmitting processing unit 1211 and a receiving processing unit 1212. The transmitting and receiving unit 120 may be composed of a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter (phase shifter), a measuring circuit, a transmitting and receiving circuit, etc., which are described based on the common knowledge in the technical field involved in the present disclosure.

[0313] The transmitting and receiving unit 120 may be configured as an integrated transmitting and receiving unit, or may be configured as a transmitting unit and a receiving unit. The transmitting unit may also be configured as a transmitting processing unit 1211 and an RF unit 122. The receiving unit may also be configured as a receiving processing unit 1212, an RF unit 122, and a measuring unit 123.

[0314] The transmitting / receiving antenna 130 can be constituted by an antenna described based on common knowledge in the technical field involved in the present disclosure, such as an array antenna.

[0315] The transmitting and receiving unit 120 may also transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transmitting and receiving unit 120 may also receive the above-mentioned uplink channel, uplink reference signal, etc.

[0316] The transmitting and receiving unit 120 may also use digital beamforming (eg, precoding), analog beamforming (eg, phase rotation), etc. to form at least one of a transmitting beam and a receiving beam.

[0317] The sending and receiving unit 120 (sending processing unit 1211), for example, may also perform Packet Data Convergence Protocol (PDCP) layer processing, Radio Link Control (RLC) layer processing (for example, RLC retransmission control), Medium Access Control (MAC) layer processing (for example, HARQ retransmission control), etc. on the data and control information obtained from the control unit 110 to generate a bit string to be sent.

[0318] The transmitting and receiving unit 120 (transmitting processing unit 1211) can also perform channel coding (which may also include error correction coding), modulation, mapping, filter processing (filtering processing), discrete Fourier transform (Discrete Fourier Transform (DFT)) processing (as needed), inverse fast Fourier transform (Inverse Fast Fourier Transform (IFFT)) processing, precoding, digital-to-analog conversion and other transmission processing on the bit string to be sent, and output a baseband signal.

[0319] The transmitting and receiving unit 120 (RF unit 122 ) may also perform modulation, filter processing, amplification, etc. on the baseband signal to a radio frequency band, and transmit the signal in the radio frequency band via the transmitting and receiving antenna 130 .

[0320] On the other hand, the transmitting and receiving unit 120 (RF unit 122 ) may also perform amplification, filter processing, demodulation to a baseband signal, etc. on the signal in the radio frequency band received by the transmitting and receiving antenna 130 .

[0321] The sending and receiving unit 120 (receiving processing unit 1212) can also apply analog-to-digital conversion, fast Fourier transform (FFT) processing, inverse discrete Fourier transform (IDFT) processing (as needed), filter processing (filtering processing), demapping, demodulation, decoding (which may also include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal to obtain user data, etc.

[0322] The transmitting and receiving unit 120 (measuring unit 123) may also implement measurements related to the received signal. For example, the measuring unit 123 may also perform radio resource management (Radio Resource Management (RRM)) measurements, channel state information (CSI) measurements, etc. based on the received signal. The measuring unit 123 may also measure received power (e.g., reference signal received power (Reference Signal Received Power (RSRP))), received quality (e.g., reference signal received quality (Reference Signal Received Quality (RSRQ)), signal to interference plus noise ratio (Signal to Interference plus Noise Ratio (SINR)), signal to noise ratio (Signal to Noise Ratio (SNR))), signal strength (e.g., received signal strength indicator (Received Signal Strength Indicator (RSSI))), propagation path information (e.g., CSI), etc. The measurement results may also be output to the control unit 110.

[0323] The transmission path interface 140 can also send and receive signals (return signaling) between the devices included in the core network 30 (for example, the network node providing NF), other base stations 10, etc., and can also obtain and transmit user data (user plane data) and control plane data for the user terminal 20.

[0324] In addition, the transmission unit and the reception unit of the base station 10 in the present disclosure may also be constituted by at least one of the transmission and reception unit 120 , the transmission and reception antenna 130 , and the transmission path interface 140 .

[0325] In addition, the transmitting and receiving unit 120 may also receive information for determining an artificial intelligence (AI) model based on the model transition category, and the control unit 110 may also perform reasoning based on the AI ​​model determined based on the information.

[0326] (User Terminal)

[0327] Fig.16 2 is a diagram showing an example of a configuration of a user terminal according to an embodiment. The user terminal 20 includes a control unit 210, a transmitting / receiving unit 220, and a transmitting / receiving antenna 230. In addition, the control unit 210, the transmitting / receiving unit 220, and the transmitting / receiving antenna 230 may be provided in one or more portions.

[0328] In addition, in this example, the functional blocks of the characteristic parts in this embodiment are mainly shown, and it is also conceivable that the user terminal 20 also has other functional blocks required for wireless communication. Part of the processing of each unit described below may be omitted.

[0329] The control unit 210 controls the entire user terminal 20. The control unit 210 can be composed of a controller, a control circuit, and the like that are described based on common knowledge in the technical field to which the present disclosure relates.

[0330] The control unit 210 may also control signal generation, mapping, etc. The control unit 210 may also control transmission and reception, measurement, etc. using the transmission and reception unit 220 and the transmission and reception antenna 230. The control unit 210 may also generate data, control information, sequences, etc. to be transmitted as signals, and forward them to the transmission and reception unit 220.

[0331] The transmitting and receiving unit 220 may also include a baseband unit 221, an RF unit 222, and a measuring unit 223. The baseband unit 221 may also include a transmitting processing unit 2211 and a receiving processing unit 2212. The transmitting and receiving unit 220 may be composed of a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measuring circuit, a transmitting and receiving circuit, etc., which are described based on the common knowledge in the technical field involved in the present disclosure.

[0332] The transmitting and receiving unit 220 may be configured as an integrated transmitting and receiving unit, or may be configured as a transmitting unit and a receiving unit. The transmitting unit may also be configured as a transmitting processing unit 2211 and an RF unit 222. The receiving unit may also be configured as a receiving processing unit 2212, an RF unit 222, and a measuring unit 223.

[0333] The transmitting / receiving antenna 230 can be constituted by an antenna described based on common knowledge in the technical field involved in the present disclosure, such as an array antenna.

[0334] The transmitting and receiving unit 220 may also receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transmitting and receiving unit 220 may also transmit the above-mentioned uplink channel, uplink reference signal, etc.

[0335] The transmitting and receiving unit 220 may also use digital beamforming (eg, precoding), analog beamforming (eg, phase rotation), etc. to form at least one of a transmitting beam and a receiving beam.

[0336] The sending and receiving unit 220 (sending processing unit 2211) can also perform PDCP layer processing, RLC layer processing (for example, RLC retransmission control), MAC layer processing (for example, HARQ retransmission control), etc. on the data, control information, etc. obtained from the control unit 210 to generate a bit string to be sent.

[0337] The transmitting and receiving unit 220 (transmitting processing unit 2211) can also perform channel coding (which may also include error correction coding), modulation, mapping, filter processing (filtering processing), DFT processing (as needed), IFFT processing, precoding, digital-to-analog conversion and other transmission processing on the bit string to be sent, and output a baseband signal.

[0338] In addition, whether to apply DFT processing may also be based on the setting of transform precoding. For a certain channel (e.g., PUSCH), when transform precoding is valid (enabled), the transmitting and receiving unit 220 (transmitting processing unit 2211) may also perform DFT processing as the above-mentioned transmission processing in order to transmit the channel using a DFT-s-OFDM waveform. Otherwise, the transmitting and receiving unit 220 (transmitting processing unit 2211) may also perform DFT processing as the above-mentioned transmission processing without performing DFT processing.

[0339] The transmitting and receiving unit 220 (RF unit 222 ) may perform modulation, filter processing (filtering processing), amplification, etc. on the baseband signal to a radio frequency band, and transmit the signal in the radio frequency band via the transmitting and receiving antenna 230 .

[0340] On the other hand, the transmitting and receiving unit 220 (RF unit 222) may perform amplification, filter processing (filter processing), demodulation into a baseband signal, etc. on the signal in the radio frequency band received by the transmitting and receiving antenna 230.

[0341] The sending and receiving unit 220 (receiving processing unit 2212) can also apply analog-to-digital conversion, FFT processing, IDFT processing (as needed), filter processing (filtering processing), demapping, demodulation, decoding (which may also include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the obtained baseband signal to obtain user data, etc.

[0342] The transmitting and receiving unit 220 (measuring unit 223) may also perform measurements related to the received signal. For example, the measuring unit 223 may also perform RRM measurements, CSI measurements, etc. based on the received signal. The measuring unit 223 may also measure received power (e.g., RSRP), received quality (e.g., RSRQ, SINR, SNR), signal strength (e.g., RSSI), propagation path information (e.g., CSI), etc. The measurement results may also be output to the control unit 210.

[0343] In addition, the transmitting unit and the receiving unit of the user terminal 20 in the present disclosure may also be composed of at least one of the transmitting and receiving unit 220 and the transmitting and receiving antenna 230 .

[0344] In addition, the transmitting and receiving unit 220 may also receive information for determining an artificial intelligence (AI) model based on the model migration category, and the control unit 210 may also perform reasoning based on the AI ​​model determined based on the information.

[0345] The information may also include a source Uniform Resource Locator (URL) of the AI ​​model or index information of a file used for the AI ​​model.

[0346] The information may also include information about the network structure used for the AI ​​model.

[0347] The control unit 210 may also control the processing related to the AI ​​model based on a protocol stack utilizing a model management unit for processing model migration.

[0348] (Hardware Structure)

[0349] In addition, the block diagram used in the description of the above-mentioned embodiment shows a block of a functional unit. These functional blocks (structural units) are implemented by any combination of at least one of hardware and software. In addition, the implementation method of each functional block is not particularly limited. That is, each functional block can be implemented by a device that is physically or logically combined, or two or more devices that are physically or logically separated can be directly or indirectly (for example, by wired, wireless, etc.) connected and implemented by these multiple devices. The functional block can also be implemented by combining the above-mentioned one device or the above-mentioned multiple devices with software.

[0350] Here, the functions include judging, deciding, determining, calculating, calculating, processing, deriving, investigating, searching, confirming, receiving, sending, outputting, accessing, solving, selecting, selecting, establishing, comparing, assuming, expecting, regarding, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, assigning, etc., but are not limited to these. For example, a functional block (structural unit) that implements a sending function may also be referred to as a transmitting unit, a transmitter, etc. Any one of them is as described above, and the implementation method thereof is not particularly limited.

[0351] For example, a base station, a user terminal, etc. in one embodiment of the present disclosure may also function as a computer that performs processing of the wireless communication method of the present disclosure. Fig.17 A diagram showing an example of a hardware configuration of a base station and a user terminal according to an embodiment. The base station 10 and the user terminal 20 described above may also 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, and the like.

[0352] In addition, in the present disclosure, the terms such as device, circuit, equipment, section, unit, etc. can be interchangeably used. The hardware structure of the base station 10 and the user terminal 20 may be configured to include one or more of the devices shown in the figure, or may be configured to exclude some of the devices.

[0353] For example, only one processor 1001 is shown, but there may be multiple processors. In addition, the processing may be performed by one processor, or may be performed by two or more processors simultaneously, sequentially, or in other ways. In addition, the processor 1001 may also be implemented by one or more chips.

[0354] The functions of the base station 10 and the user terminal 20 are realized, for example, by reading specific software (program) into hardware such as the processor 1001 and the memory 1002, so that the processor 1001 performs calculations and controls communication via the communication device 1004, or controls at least one of the reading and writing of data in the memory 1002 and the storage 1003.

[0355] The processor 1001 controls the entire computer by, for example, operating an operating system. The processor 1001 may also be composed of a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic device, a register, etc. For example, at least a part of the above-mentioned control unit 110 (210), the sending and receiving unit 120 (220), etc. may also be implemented by the processor 1001.

[0356] In addition, the processor 1001 reads the program (program code), software module, data, etc. from at least one of the storage 1003 and the communication device 1004 to the memory 1002, and performs various processes based on them. As a program, a program that causes a computer to perform at least a part of the operations described in the above-mentioned embodiments can be used. For example, the control unit 110 (210) can also be implemented by a control program stored in the memory 1002 and operated in the processor 1001, and the same can be implemented for other functional blocks.

[0357] The memory 1002 may also be a computer-readable recording medium, for example, composed of at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), or other appropriate storage media. The memory 1002 may also be referred to as a register, a cache, a main memory (main storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing the wireless communication method involved in one embodiment of the present disclosure.

[0358] The storage 1003 may also be a computer-readable recording medium, such as a flexible disk, a floppy disk, an optical disk (such as a compact disk (Compact Disc ROM (CD-ROM)), a digital versatile disk, a Blu-ray (Blu-ray) (registered trademark) disk), a removable disk, a hard disk drive, a smart card, a flash memory device (such as a card, a stick, a key drive), a magnetic stripe, a database, a server, or at least one of other suitable storage media. The storage 1003 may also be referred to as an auxiliary storage device.

[0359] The communication device 1004 is hardware (transmitting and receiving device) for communicating between computers via at least one of a wired network and a wireless network, and is also called a network device, a network controller, a network card, a communication module, etc. In order to realize at least one of frequency division duplex (Frequency Division Duplex (FDD)) and time division duplex (Time Division Duplex (TDD)), the communication device 1004 can also be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. For example, the above-mentioned transmitting and receiving unit 120 (220), the transmitting and receiving antenna 130 (230), etc. can also be realized by the communication device 1004. The transmitting and receiving unit 120 (220) can also be realized by physically or logically separating the transmitting unit 120a (220a) and the receiving unit 120b (220b).

[0360] The input device 1005 is an input device that receives input from the outside (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.). The output device 1006 is an output device that implements output to the outside (e.g., a display, a speaker, a light emitting diode (LED) lamp, etc.). In addition, the input device 1005 and the output device 1006 may also be an integrated structure (e.g., a touch panel).

[0361] In addition, the processor 1001, the memory 1002 and other devices are connected via a bus 1007 for communicating information. The bus 1007 may be configured as a single bus or may be configured as different buses between the devices.

[0362] In addition, the base station 10 and the user terminal 20 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), and a field programmable gate array (FPGA), and the hardware may be used to implement a part or all of each functional block. For example, the processor 1001 may also be implemented using at least one of these hardware.

[0363] (Variation Example)

[0364] In addition, the terms described in the present disclosure and the terms required for understanding the present disclosure may also be replaced with terms having the same or similar meanings. For example, channels, code elements, and signals (signals or signaling) may also be rewritten with each other. In addition, a signal may also be a message. A reference signal may also be referred to as RS, and may also be referred to as a pilot, a pilot signal, etc. depending on the standard applied. In addition, a component carrier (CC) may also be referred to as a cell, a frequency carrier, a carrier frequency, etc.

[0365] A wireless frame may also be composed of one or more periods (frames) in the time domain. Each period (frame) of the one or more periods (frames) constituting a wireless frame may also be referred to as a subframe. Further, a subframe may also be composed of one or more time slots in the time domain. A subframe may also be a fixed time length (e.g., 1 ms) that is not dependent on a parameter set (numerology).

[0366] Here, the parameter set may also be a communication parameter applied in at least one of the transmission and reception of a certain signal or channel. For example, the parameter set may also represent at least one of a subcarrier spacing (SCS), bandwidth, symbol length, cyclic prefix length, transmission time interval (TTI), the number of symbols per TTI, a wireless frame structure, a specific filter processing performed by a transmitter and receiver in the frequency domain, a specific windowing processing performed by a transmitter and receiver in the time domain, and the like.

[0367] A time slot may also be composed of one or more symbols (Orthogonal Frequency Division Multiplexing (OFDM) symbols, Single Carrier Frequency Division Multiple Access (SC-FDMA) symbols, etc.) in the time domain. In addition, a time slot may also be a time unit based on a parameter set.

[0368] A time slot may also include multiple mini-slots. Each mini-slot may also be composed of one or more symbols in the time domain. In addition, a mini-slot may also be referred to as a sub-slot. A mini-slot may also be composed of fewer symbols than a time slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a mini-slot may also be referred to as a PDSCH (PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using a mini-slot may also be referred to as a PDSCH (PUSCH) mapping type B.

[0369] Radio frames, subframes, time slots, mini-time slots, and symbols all represent time units for transmitting signals. Radio frames, subframes, time slots, mini-time slots, and symbols may also be referred to by their respective names. In addition, time units such as frames, subframes, time slots, mini-time slots, and symbols in the present disclosure may also be replaced with each other.

[0370] For example, a subframe may be referred to as a TTI, a plurality of consecutive subframes may be referred to as a TTI, and a time slot or a mini time slot may be referred to as a TTI. That is, at least one of a subframe and a TTI may be a subframe (1 ms) in existing LTE, a period shorter than 1 ms (e.g., 1-13 symbols), or a period longer than 1 ms. In addition, the unit representing a TTI may be referred to as a time slot, a mini time slot, etc. instead of a subframe.

[0371] Here, TTI refers to, for example, the minimum time unit for scheduling in wireless communication. For example, in the LTE system, the base station schedules each user terminal to allocate wireless resources (frequency bandwidth, transmission power, etc. that can be used in each user terminal) in TTI units. In addition, the definition of TTI is not limited to this.

[0372] TTI may also be a transmission time unit for a data packet (transport block), a code block, a code word, etc. that has been channel-coded, and may also be a processing unit for scheduling, link adaptation, etc. In addition, when 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.

[0373] In addition, when a time slot or a mini time slot is called TTI, one or more TTIs (i.e., one or more time slots or one or more mini time slots) can also be the minimum time unit of scheduling. In addition, the number of time slots (mini time slots) constituting the minimum time unit of scheduling can also be controlled.

[0374] A TTI having a time length of 1 ms may also be referred to as a normal TTI (TTI in 3GPP Rel.8-12), a standard TTI, a long TTI, a normal subframe, a standard subframe, a long subframe, a time slot, etc. A TTI shorter than a normal TTI may also be referred to as a shortened TTI, a short TTI, a partial TTI (partial or fractional TTI), a shortened subframe, a short subframe, a mini time slot, a sub time slot, a time slot, etc.

[0375] In addition, a long TTI (e.g., normal TTI, subframe, etc.) can also be rewritten as a TTI with a time length exceeding 1 ms, and a short TTI (e.g., shortened TTI, etc.) can also be rewritten as a TTI with a TTI length shorter than that of the long TTI and longer than 1 ms.

[0376] 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 (subcarriers) in the frequency domain. The number of subcarriers included in an RB may also be the same regardless of the parameter set, for example, 12. The number of subcarriers included in an RB may also be determined based on the parameter set.

[0377] In addition, an RB may also include one or more symbols in the time domain, and may also be the length of a slot, a mini-slot, a subframe, or a TTI. A TTI, a subframe, etc. may also be composed of one or more resource blocks, respectively.

[0378] In addition, one or more RBs may also be referred to as a physical resource block (Physical RB (PRB)), a sub-carrier group (Sub-Carrier Group (SCG)), a resource element group (Resource Element Group (REG)), a PRB pair, an RB pair, etc.

[0379] In addition, a resource block may be composed of one or more resource elements (RE). For example, one RE may be a radio resource region of one subcarrier and one symbol.

[0380] A Bandwidth Part (BWP) (also referred to as a partial bandwidth, etc.) may also represent a subset of contiguous common RBs (common resource blocks) for a parameter set in a carrier. Here, a common RB may also be identified by an index of the RB relative to a common reference point of the carrier. PRBs may also be defined in a BWP and numbered within the BWP.

[0381] The BWP may include a UL BWP (BWP for UL) and a DL BWP (BWP for DL). For a UE, one or more BWPs may be configured in one carrier.

[0382] At least one of the set BWPs may be activated, and the UE may not assume that a specific signal / channel is transmitted or received outside the activated BWP. In addition, "cell", "carrier", etc. in the present disclosure may also be rewritten as "BWP".

[0383] In addition, the above-mentioned structures such as radio frames, subframes, time slots, mini-time slots and symbols are only examples. For example, the number of subframes included in a radio frame, the number of time slots in each subframe or radio frame, the number of mini-time slots included in a time slot, the number of symbols and RBs included in a time slot or mini-time slot, the number of subcarriers included in an RB, the number of symbols in a TTI, the symbol length, and the cyclic prefix (CP) length can be changed in various ways.

[0384] In addition, the information, parameters, etc. described in the present disclosure may be represented by absolute values, relative values ​​relative to a specific value, or other corresponding information. For example, wireless resources may also be indicated by a specific index.

[0385] In the present disclosure, the names used for parameters, etc. are not limiting in all respects. Furthermore, the mathematical formulas, etc. using these parameters may be different from those explicitly disclosed in the present disclosure. Various channels (PUCCH, PDCCH, etc.) and information elements can be identified by any suitable names, and therefore, the various names assigned to these various channels and information elements are not limiting in all respects.

[0386] Information, signals, etc. described in this disclosure may also be represented using any of a variety of different techniques. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be mentioned throughout the above description may also be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any combination thereof.

[0387] Furthermore, information, signals, etc. can be output in at least one of the following directions: from a higher layer (upper layer) to a lower layer (lower layer), and from a lower layer to a higher layer. Information, signals, etc. can also be input and output via multiple network nodes.

[0388] The input and output information, signals, etc. may be stored in a specific location (e.g., a memory), or may be managed using a management table. The input and output information, signals, etc. may be overwritten, updated, or appended. The output information, signals, etc. may also be deleted. The input information, signals, etc. may also be sent to other devices.

[0389] The notification of information is not limited to the methods / implementations described in the present disclosure, and may also be performed by other methods. For example, the notification of information in the present disclosure may also be implemented through physical layer signaling (e.g., downlink control information (Downlink Control Information (DCI))), uplink control information (Uplink Control Information (UCI))), high-layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB)), System Information Block (SIB)), etc.), Medium Access Control (MAC) signaling), other signals or a combination thereof.

[0390] In addition, physical layer signaling may also be referred to as layer 1 / layer 2 (Layer 1 / Layer 2 (L1 / L2)) control information (L1 / L2 control signal), L1 control information (L1 control signal), etc. In addition, RRC signaling may also be referred to as an RRC message, such as an RRC connection establishment (RRC Connection Setup) message, an RRC connection reconstruction (RRC Connection Reconfiguration) message, etc. In addition, MAC signaling may also be notified using, for example, a MAC control element (MACControl Element (CE)).

[0391] Furthermore, notification of specific information (eg, notification of “it is X”) is not limited to explicit notification, but may be performed implicitly (eg, by not notifying the specific information or by notifying other information).

[0392] The determination can be made by a value represented by a bit (0 or 1), by a true or false value (Boolean value) represented by true or false, or by comparison of numerical values ​​(for example, comparison with a specific value).

[0393] Whether software is called software, firmware, middle-ware, microcode, hardware description language, or other names, it should be broadly interpreted as meaning instructions, instruction sets, code, code segments, program code, program, sub-program, software modules, applications, software applications, software packages, routines, sub-routines, objects, executable files, execution threads, procedures, functions, etc.

[0394] In addition, software, instructions, information, etc. may also be sent and received via a transmission medium. For example, when software is sent from a website, server, or other remote source using at least one of wired technology (coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.) and wireless technology (infrared, microwave, etc.), at least one of these wired technology and wireless technology is included in the definition of transmission medium.

[0395] The terms "system" and "network" used in the present disclosure can be used interchangeably. "Network" may also refer to a device (eg, a base station) included in the network.

[0396] In the present disclosure, terms such as "precoding", "precoder", "weight (precoding weight)", "Quasi-Co-Location (QCL)", "Transmission Configuration Indication state (TCI state)", "spatial relation", "spatial domain filter", "transmission power", "phase rotation", "antenna port", "antenna port group", "layer", "number of layers", "rank", "resource", "resource set", "resource group", "beam", "beam width", "beam angle", "antenna", "antenna element", and "panel" can be used interchangeably.

[0397] In the present disclosure, terms such as "Base Station (BS)", "wireless base station", "fixed station", "NodeB", "eNB (eNodeB)", "gNB (gNodeB)", "access point", "Transmission Point (TP)", "Reception Point (RP)", "Transmission / Reception Point (TRP)", "panel", "cell", "sector", "cell group", "carrier", and "component carrier" can be used interchangeably. There are also cases where base stations are referred to by terms such as macro cell, small cell, micro-micro cell, and micro-micro cell.

[0398] A base station can accommodate one or more (for example, three) cells. When a base station accommodates multiple cells, the overall coverage area of ​​the base station can be divided into multiple smaller areas, and each smaller area can also provide communication services through a base station subsystem (for example, a small base station for indoor use (Remote Radio Head (RRH))). Terms such as "cell" or "sector" refer to a part or the entirety of the coverage area of ​​at least one of a base station and a base station subsystem that provides communication services within the coverage area.

[0399] In the present disclosure, the base station sending information to the terminal may also be rewritten as the base station instructing the terminal to control / operate based on the information.

[0400] In the present disclosure, terms such as “mobile station (MS)”, “user terminal”, “user device (User Equipment (UE))”, and “terminal” can be used interchangeably.

[0401] There are also cases where a mobile station is 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, hand set, user agent, mobile client, client or several other appropriate terms.

[0402] At least one of the base station and the mobile station may also be referred to as a transmission device, a reception device, a wireless communication device, etc. In addition, at least one of the base station and the mobile station may be a device mounted on a moving object, a moving object body, etc.

[0403] The mobile body refers to a movable object, and the moving speed is arbitrary, and of course it also includes the situation where the mobile body stops. The mobile body includes, for example, vehicles, transport vehicles, cars, automatic two-wheeled vehicles (motorcycles), bicycles, connected cars, loading shovels, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, trolleys, rickshaws, ships (ships and other watercrafts), airplanes, rockets, artificial satellites, drones, multi-rotor aircraft, quadcopters, balloons and objects carried on them, but are not limited to these. In addition, the mobile body can also be a mobile body that drives autonomously based on operating instructions.

[0404] The mobile body may be a means of transportation (e.g., a vehicle, an airplane, etc.), a mobile body that moves unmanned (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned). In addition, at least one of the base station and the mobile station also includes a device that does not necessarily move when performing communication operations. For example, at least one of the base station and the mobile station may also be an Internet of Things (IoT) device such as a sensor.

[0405] Fig.18A diagram of an example of a vehicle according to an embodiment is shown. The vehicle 40 includes a drive unit 41, a steering unit 42, an accelerator pedal 43, a brake pedal 44, a shift lever 45, left and right front wheels 46, left and right rear wheels 47, an axle 48, an electronic control unit 49, various sensors (including a current sensor 50, a rotation speed sensor 51, an air pressure sensor 52, a vehicle speed sensor 53, an acceleration sensor 54, an accelerator pedal sensor 55, a brake pedal sensor 56, a shift lever sensor 57, and an object detection sensor 58), an information service unit 59, and a communication module 60.

[0406] The driving unit 41 is composed of at least one of an engine, a motor, or a combination of an engine and a motor. The steering unit 42 is composed of at least a steering wheel (also called a handlebar), and steers at least one of the front wheels 46 and the rear wheels 47 based on the operation of the steering wheel operated by the user.

[0407] The electronic control unit 49 is composed of a microprocessor 61, a memory (ROM, RAM) 62, and a communication port (for example, an input / output (IO) port) 63. Signals from various sensors 50-58 provided in the vehicle are input to the electronic control unit 49. The electronic control unit 49 may also be referred to as an ECU (Electronic Control Unit).

[0408] The signals from the various sensors 50-58 include a current signal from a current sensor 50 for sensing the current of the motor, a speed signal of the front wheels 46 / rear wheels 47 obtained by a 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 stepping amount signal of the accelerator pedal 43 obtained by an accelerator pedal sensor 55, a stepping 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, a detection signal for detecting obstacles, vehicles, pedestrians, etc. obtained by an object detection sensor 58, and the like.

[0409] The information service unit 59 is composed of various devices such as a vehicle navigation system, an audio system, a speaker, 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 provides various information / services (for example, multimedia information / multimedia services) to the occupants of the vehicle 40 using information obtained from an external device via the communication module 60, etc.

[0410] The information service unit 59 may include input devices for accepting input from the outside (e.g., keyboard, mouse, microphone, switch, button, sensor, touch panel, etc.), and may also include output devices for implementing output to the outside (e.g., display, speaker, LED light, touch panel, etc.).

[0411] The driving assistance system unit 64 is composed of various devices such as millimeter wave radar, light detection and ranging (LiDAR), camera, positioning locator (e.g., Global Navigation Satellite System (GNSS)), map information (e.g., high-precision (High Definition (HD))) map, autonomous vehicle (Autonomous Vehicle (AV)) map, etc.), gyroscope system (e.g., inertial measurement unit (Inertial Measurement Unit (IMU))), inertial navigation unit (Inertial Navigation System (INS))), artificial intelligence (Artificial Intelligence (AI)) chip, AI processor, etc., for providing functions for preventing accidents before they happen or reducing the driver's driving load, and one or more ECUs for controlling these devices. In addition, the driving assistance system unit 64 sends and receives various information via the communication module 60 and realizes driving assistance function or autonomous driving function.

[0412] The communication module 60 can communicate with the microprocessor 61 and the components of the vehicle 40 via the communication port 63. For example, the communication module 60 transmits and receives data (information) between the drive unit 41, the steering unit 42, the accelerator pedal 43, the brake pedal 44, the shift lever 45, the left and right front wheels 46, the left and right rear wheels 47, the axle 48, the microprocessor 61 and the memory (ROM, RAM) 62 in the electronic control unit 49, and the various sensors 50-58 of the vehicle 40 via the communication port 63.

[0413] The communication module 60 can be controlled by the microprocessor 61 of the electronic control unit 49, and is a communication device that can communicate with an external device. For example, various information is sent and received with the external device via wireless communication. The communication module 60 can be located either inside or outside the electronic control unit 49. The external device can also be, for example, the above-mentioned base station 10, user terminal 20, etc. In addition, the communication module 60 can also be, for example, at least one of the above-mentioned base station 10 and user terminal 20 (it can also function as at least one of the base station 10 and user terminal 20).

[0414] The communication module 60 may also transmit at least one of the signals from the 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 also be referred to as an input unit that receives input. For example, the PUSCH transmitted through the communication module 60 may also include information based on the above input.

[0415] The communication module 60 receives various information (traffic information, signal information, vehicle-to-vehicle information, etc.) transmitted from an external device, and displays it to the information service unit 59 provided in the vehicle. The information service unit 59 may also be referred to as an output unit for outputting information (for example, outputting information to a display, a speaker, etc. based on the PDSCH received by the communication module 60 (or data / information decoded from the PDSCH)).

[0416] In addition, the communication module 60 stores various information received from the external device in the memory 62 that can be used by the microprocessor 61. The microprocessor 61 can also control the drive unit 41, the steering unit 42, the accelerator pedal 43, the brake pedal 44, the shift lever 45, the left and right front wheels 46, the left and right rear wheels 47, the axle 48, and various sensors 50-58 of the vehicle 40 based on the information stored in the memory 62.

[0417] In addition, the base station in the present disclosure may also be rewritten as a user terminal. For example, the various methods / implementations of the present disclosure may also be applied to a structure in which the communication between a base station and a user terminal is replaced by the communication between multiple user terminals (for example, it may also be referred to as device-to-device (D2D)), vehicle-to-everything (V2X), etc.). In this case, it may also be set as a structure in which the user terminal 20 has the functions possessed by the above-mentioned base station 10. In addition, terms such as "uplink", "downlink", etc. may also be rewritten as terms corresponding to communication between terminals (for example, "sidelink"). For example, uplink channels, downlink channels, etc. may also be rewritten as sidelink channels.

[0418] Likewise, the user terminal in the present disclosure may be rewritten as a base station. In this case, the base station 10 may have the functions of the user terminal 20 described above.

[0419] In the present disclosure, the actions are assumed to be performed by the base station, and sometimes by its upper node (uppernode) depending on the situation. Obviously, in a network including one or more network nodes having a base station, various operations performed for communication with a terminal can be performed by the base station, one or more network nodes other than the base station (for example, considering the Mobility Management Entity (MME)), the Serving-Gateway (S-GW), etc., but not limited to these) or a combination thereof.

[0420] The various methods / implementations described in this disclosure may be used alone or in combination, and may be used in a switched manner as the method is executed. In addition, the processing procedures, timings, flow charts, etc. of the various methods / implementations described in this disclosure may be reversed in order as long as they are not contradictory. For example, for the method described in this disclosure, the elements of various steps are presented in an illustrative order, but are not limited to the specific order presented.

[0421] The various modes / implementations described in the present disclosure may also be applied to 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 a decimal)), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New Radio Access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), systems using other appropriate wireless communication methods, and next-generation systems based on these enhancements, corrections, production or regulations, etc. In addition, multiple systems can also be combined (for example, LTE or LTE-A, combination with 5G, etc.) for application.

[0422] The phrase “based on” used in the present disclosure does not mean “based only on” unless otherwise specified. In other words, the phrase “based on” means both “based only on” and “based at least on”.

[0423] Any reference to an element using the terms "first", "second", etc. used in this disclosure does not fully define the amount or order of these elements. These terms can be used in this disclosure as a convenient method to distinguish between two or more elements. Therefore, reference to the first and second elements does not mean that only two elements can be used or that the first element must take precedence over the second element in some form.

[0424] The term "determining" used in the present disclosure may include a variety of actions. For example, "determining" may also refer to situations where judging, calculating, computing, processing, deriving, investigating, looking up (e.g., searching in a table, database or other data structure), ascertaining, etc. are considered to be "determining".

[0425] In addition, "judgment (decision)" may also be a situation where receiving (for example, receiving information), sending (for example, sending information), input (input), output (output), accessing (for example, accessing data in a memory), etc. are regarded as making a "judgment (decision)".

[0426] In addition, "judgment (decision)" can also be regarded as a situation where resolving, selecting, choosing, establishing, comparing, etc. are regarded as a situation where "judgment (decision)" is performed. That is, "judgment (decision)" can also be regarded as a situation where some actions are regarded as a situation where "judgment (decision)" is performed.

[0427] In addition, "judge (decide)" can also be rewritten as "assuming (assuming)", "expecting (expecting)", "considering (considering)" and so on.

[0428] The “maximum transmit power” recorded in the present disclosure may refer to the maximum value of the transmit power, the nominal maximum transmit power (the nominal UE maximum transmit power), or the rated maximum transmit power (the rated maximum transmit power).

[0429] The terms "connected", "coupled", or all their variations used in this disclosure refer to all direct or indirect connections or combinations between two or more elements, and may include the situation where one or more intermediate elements exist between two elements that are "connected" or "coupled" to each other. The combination or connection between elements may be physical, logical, or a combination thereof. For example, "connection" may also be rewritten as "access".

[0430] In the present disclosure, when two elements are connected, it is possible to consider being "connected" or "combined" to each other using one or more wires, cables, printed electrical connections, etc., as well as using electromagnetic energy with wavelengths in the wireless frequency domain, microwave region, light (both visible and invisible) region, etc. as several non-limiting and non-inclusive examples.

[0431] In the present disclosure, the term "A is different from B" may also mean "A and B are different from each other". In addition, the term may also mean "A and B are different from C, respectively". The terms "separate" and "combined" may also be interpreted in the same manner as "different".

[0432] When the terms “include,” “including,” and variations thereof are used in the present disclosure, these terms have an inclusive meaning, similar to the term “comprising.” Furthermore, the term “or” used in the present disclosure does not have an exclusive OR meaning.

[0433] In the present disclosure, when an article is added by translation like a, an, and the in English, for example, the present disclosure may also include a case where the noun following the article is in plural form.

[0434] In the present disclosure, "below", "less than", "above", "more than", "equal to", etc. can also be replaced by each other. In addition, in the present disclosure, sentences meaning "good", "bad", "big", "small", "high", "low", "early", "late", etc. can also be replaced by each other (without limiting the original degree, comparative degree, and superlative degree). In addition, in the present disclosure, sentences meaning "good", "bad", "big", "small", "high", "low", "early", "late", etc. can also be replaced by each other as expressions with "ith" attached (without limiting the original degree, comparative degree, and superlative degree) (for example, "highest" can also be replaced by "ith highest").

[0435] In the present disclosure, “of,” “for,” “regarding,” “related to,” “associated with,” etc. may also be replaced by each other.

[0436] The invention involved in the present disclosure is described in detail above, but it is obvious to those skilled in the art that the invention involved in the present disclosure is not limited to the embodiments described in the present disclosure. The invention involved in the present disclosure can be implemented as a modified and altered mode without departing from the gist and scope of the invention determined based on the description of the claims. Therefore, the description of the present disclosure is for the purpose of illustrative description and does not have any limiting meaning on the invention involved in the present disclosure.

Claims

1. A terminal having: A receiving unit receives information for determining an artificial intelligence (AI) model based on the model transfer category; and A control unit performs inference based on the AI ​​model, wherein the AI ​​model is determined based on the information.

2. The terminal according to claim 1, wherein: The information includes a source Uniform Resource Locator (URL) of the AI ​​model or index information of a file used for the AI ​​model.

3. The terminal according to claim 1, wherein: The information may also include information about the network structure used for the AI ​​model.

4. The terminal according to claim 1, wherein: The control unit controls processing related to the AI ​​model based on a protocol stack utilizing a model management unit for processing model migration.

5. A wireless communication method of a terminal, comprising: The step of receiving information for artificial intelligence (AI) model determination based on the model transfer category; and The step of performing reasoning based on the AI ​​model, wherein: The AI ​​model is determined based on the information.

6. A base station, comprising: A receiving unit receives information for determining an artificial intelligence (AI) model based on the model transfer category; and A control unit performs inference based on the AI ​​model, wherein the AI ​​model is determined based on the information.

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