Network component and wireless communication method

Abstract

A network component according to one aspect of the present disclosure comprises a reception unit that receives a request for a model transfer, and a control unit that controls the start and end of the model transfer on the basis of the request. This aspect of the present disclosure makes it possible to achieve suitable overhead reduction, channel estimation, and resource utilization.

Description

Network element and wireless communication method

[0001] The present disclosure relates to network components and wireless communication methods in next generation mobile communication systems.

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

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

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

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

[0006] When utilizing AI models, data set / model management is being considered, but in some cases, this is not sufficiently considered. If this consideration is insufficient, optimal overhead reduction, channel estimation, and resource utilization cannot be achieved, which may hinder improvements in communication throughput and communication quality.

[0007] Therefore, one of the objects of the present disclosure is to provide a network component and a wireless communication method that can achieve suitable overhead reduction / channel estimation / resource utilization.

[0008] A network element according to one aspect of the present disclosure includes a receiving unit that receives a request for model transfer, and a control unit that controls the start and end of model transfer based on the request.

[0009] According to one aspect of the present disclosure, it is possible to achieve favorable overhead reduction / channel estimation / resource utilization.

[0010] FIG. 1 is a diagram illustrating an example of a framework for managing AI models. FIG. 2 is a diagram illustrating an example of specifying an AI model. FIG. 3 is a diagram illustrating an example of an ORAN architecture. FIG. 4 is a diagram illustrating an example of a dataset. FIG. 5 is a diagram illustrating an example of model validation. FIG. 6 is a diagram illustrating an example of model training. FIG. 7 is a diagram illustrating an example of model distribution. FIG. 8 is a diagram illustrating an example of model registration. FIG. 9 is a diagram illustrating an example of a request for model validation according to the first embodiment. FIG. 10 is a diagram illustrating an example of a request for model training according to the third embodiment. FIG. 11 is a diagram illustrating an example of a request for transferable model information according to embodiment 4-1. FIG. 12 is a diagram illustrating an example of a notification of transferable model information according to embodiment 4-2. FIG. 13 is a diagram illustrating an example of a request for model distribution according to embodiment 4-3. FIG. 14 is a diagram illustrating an example of model distribution according to option 1 in a variation of the fourth embodiment. FIG. 15 is a diagram illustrating an example of model distribution according to option 2 in a variation of the fourth embodiment. FIG. 16 is a diagram illustrating an example of model registration according to embodiment 6-1. FIG. 17 is a diagram illustrating an example of model registration according to option 1 in the variations of the first, second, third, and sixth embodiments. Fig. 18 is a diagram showing an example of model registration related to option 2 in the variations of the first, second, third, and sixth embodiments. Fig. 19 is a diagram showing an example of a schematic configuration of a wireless communication system according to an embodiment. Fig. 20 is a diagram showing an example of a configuration of a base station according to an embodiment. Fig. 21 is a diagram showing an example of a configuration of a user terminal according to an embodiment. Fig. 22 is a diagram showing an example of the hardware configuration of a base station and a user terminal according to an embodiment. Fig. 23 is a diagram showing an example of a vehicle according to an embodiment.

[0011] (Application of Artificial Intelligence (AI) Technology to Wireless Communications) With regard to future wireless communications technologies, the use of AI technology such as machine learning (ML) for network / device control and management is being considered.

[0012] For example, it is being considered that terminals (user terminals, user equipment (UE)) / base stations (BSs) will utilize AI technology to improve Channel State Information (CSI) feedback (e.g., reduced overhead, improved accuracy, prediction), improve beam management (e.g., improved accuracy, prediction in the time / space domain), and improve position measurement (e.g., improved position estimation / prediction).

[0013] Based on the input information, the AI ​​model may output at least one information such as an estimate, a prediction, a selected action, a classification, etc. The UE / BS may input channel state information, reference signal measurements, etc. to the AI ​​model and output highly accurate channel state information / measurements / beam selection / location, future channel state information / radio link quality, etc.

[0014] In the present disclosure, AI may be interpreted as an object (also called a subject, object, data, function, program, etc.) that has (performs) at least one of the following characteristics: - Estimation based on observed or collected information; - Selection based on observed or collected information; - Prediction based on observed or collected information.

[0015] In the present disclosure, estimation, prediction, and inference may be used interchangeably. Also, in the present disclosure, estimate, predict, and infer may be used interchangeably.

[0016] In the present disclosure, an object may be, for example, an apparatus, device, etc., such as a UE or a BS. Also, in the present disclosure, an object may correspond to a program / model / entity that operates in the apparatus.

[0017] Also, in the present disclosure, an AI model may be interpreted as an object that has (performs) at least one of the following characteristics: - Generates an estimate by feeding information; - Predicts an estimate by feeding information; - Discovers features by feeding information; - Selects an action by feeding information.

[0018] Additionally, in this disclosure, an AI model may refer to a data-driven algorithm that applies AI techniques to generate a set of outputs based on a set of inputs.

[0019] In addition, in the present disclosure, the terms AI model, model, ML model, predictive analytics, predictive analysis model, tool, autoencoder, encoder, decoder, neural network model, AI algorithm, scheme, etc. may be interchangeable. The AI ​​model may be derived using at least one of regression analysis (e.g., linear regression analysis, multiple regression analysis, logistic regression analysis), support vector machine, random forest, neural network, deep learning, etc.

[0020] In this disclosure, the term "autoencoder" may be interchangeably referred to as any autoencoder, such as a stacked autoencoder, a convolutional autoencoder, etc. The encoder / decoder of this disclosure may employ a model such as a Residual Network (ResNet), a DenseNet, or a RefineNet.

[0021] Furthermore, in the present disclosure, the terms encoder, encoding, encode / encoded, modification / alteration / control by an encoder, compressing, compress / compressed, generating, generate / generated, etc. may be read interchangeably.

[0022] In addition, in the present disclosure, decoder, decoding, decode / decoded, modification / alteration / control by decoder, decompressing, decompress / decompressed, reconstructing, reconstruct / reconstructed, etc. may be read interchangeably.

[0023] In the present disclosure, a layer (of an AI model) may be interchangeably read as a layer (such as an input layer or an intermediate layer) used in the AI ​​model. The layer in the present disclosure may correspond to at least one of an input layer, an intermediate layer, an output layer, a batch normalization layer, a convolutional layer, an activation layer, a dense layer, a normalization layer, a pooling layer, an attention layer, a dropout layer, a fully connected layer, etc.

[0024] In this disclosure, methods for training an AI model may include supervised learning, unsupervised learning, reinforcement learning, federated learning, etc. Supervised learning may refer to the process of training a model from inputs and corresponding labels. Unsupervised learning may refer to the process of training a model without labeled data. Reinforcement learning may refer to the process of training a model from inputs (i.e., states) and feedback signals (i.e., rewards) resulting from the model's outputs (i.e., actions) in an environment with which the model interacts.

[0025] In the present disclosure, terms such as generate, calculate, derive, etc. may be interchangeable. In the present disclosure, terms such as implement, operate, operate, execute, etc. may be interchangeable. In the present disclosure, terms such as train, learn, update, retrain, etc. may be interchangeable. In the present disclosure, terms such as infer, after-training, live use, actual use, etc. may be interchangeable. In the present disclosure, signal may be interchangeable with signal / channel.

[0026] FIG. 1 is a diagram illustrating an example of a framework for managing AI models. In this example, each stage related to an AI model is shown as a block. This example is also referred to as AI model life cycle management (LCM).

[0027] The data collection stage corresponds to a stage of collecting data for generating / updating an AI model. The data collection stage may include data organization (e.g., determining which data to transfer for model training / model inference), data transfer (e.g., transferring data to an entity (e.g., UE, gNB) that performs model training / model inference), etc.

[0028] Note that data collection may refer to a process in which data is collected by a network node, a management entity, or a UE for the purpose of AI model training / data analysis / inference. In this disclosure, the terms "process" and "procedure" may be interchangeable. Also, in this disclosure, collection may refer to obtaining a data set (e.g., usable as input / output) for AI model training / inference based on measurements (e.g., channel measurements, beam measurements, radio link quality measurements, position estimation, etc.).

[0029] In the present disclosure, offline field data may be data collected from the field (real world) and used for offline training of an AI model. Also, in the present disclosure, online field data may be data collected from the field (real world) and used for online training of an AI model.

[0030] In the model training stage, model training is performed based on the data (training data) transferred from the collection stage. This stage may include data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model training / validation, model testing (e.g., verifying whether the trained model meets a performance threshold), model exchange (e.g., transferring the model for distributed learning), and model deployment / update (deploying / updating the model to the entity that will perform model inference).

[0031] It should be noted that AI model training may refer to a process for training an AI model in a data-driven manner and obtaining a trained AI model for inference.

[0032] AI model validation may also refer to a sub-process of training that evaluates the quality of an AI model using a dataset different from the dataset used to train the model, which helps select model parameters that generalize beyond the dataset used to train the model.

[0033] AI model testing may also refer to a sub-process of training for evaluating the performance of the final AI model using a dataset different from that used for model training / validation. Note that, unlike validation, testing does not necessarily require subsequent model tuning.

[0034] In the model inference stage, model inference is performed based on the data (inference data) transferred from the collection stage. This stage may include data preparation (e.g., performing data preprocessing, cleaning, formatting, transformation, etc.), model inference, model monitoring (e.g., monitoring the performance of model inference), model performance feedback (feeding back model performance to the entity training the model), and output (providing model output to the actor).

[0035] Additionally, AI model inference may refer to the process of using a trained AI model to produce a set of outputs from a set of inputs.

[0036] Also, a UE side model may refer to an AI model whose inference is performed entirely in the UE, and a network side model may refer to an AI model whose inference is performed entirely in the network (e.g., gNB).

[0037] Also, a one-sided model may refer to a UE-side model or a network-side model. A two-sided model may refer to a pair of AI models in which joint inference is performed. Here, joint inference may include AI inference in which the inference is performed jointly across the UE and the network, e.g., a first part of the inference may be performed first by the UE and the remaining part by the gNB (or vice versa).

[0038] In addition, AI model monitoring may refer to a process for monitoring the inference performance of an AI model, and may be interchangeably read as model performance monitoring, performance monitoring, etc.

[0039] Note that model registration may refer to assigning a version identifier to a model and making the model executable (registering) the model by compiling it into the specific hardware used in the inference stage. Also, model deployment may refer to distributing (or activating in) a runtime image (or an image of an execution environment) of a fully developed and tested model to (or enabling in) a target (e.g., UE / gNB) where inference will be performed.

[0040] An actor stage may include action triggers (e.g., deciding whether to trigger an action on another entity), feedback (e.g., feeding back information needed for training data / inference data / performance feedback), etc.

[0041] For example, training of a model for mobility optimization may be performed in, for example, Operation, Administration and Maintenance (Management) (OAM) / gNodeB (gNB) in a network (NW). In the former case, interoperability, large-capacity storage, operator manageability, and model flexibility (feature engineering, etc.) are advantageous. In the latter case, the latency of model updates and the need for data exchange for model deployment are advantageous. Inference of the above model may be performed in, for example, a gNB.

[0042] The entity that performs training / inference may vary depending on the use case (i.e., the function of the AI ​​model), which may include beam management, beam prediction, autoencoder (or information compression), CSI feedback, positioning, etc.

[0043] For example, for AI-assisted beam management based on measurement reports, the OAM / gNB may perform model training and the gNB may perform model inference.

[0044] For AI-assisted UE-assisted positioning, a Location Management Function (LMF) may perform model training and the LMF may perform model inference.

[0045] For CSI feedback / channel estimation using an autoencoder, the OAM / gNB / UE may perform model training and the gNB / UE may perform model inference (jointly).

[0046] For AI-assisted beam management or AI-assisted UE-based positioning based on beam measurements, the OAM / gNB / UE may perform model training and the UE may perform model inference.

[0047] Note that model activation may mean activating an AI model for a specific function, model deactivation may mean disabling an AI model for a specific function, and model switching may mean deactivating a currently active AI model for a specific function and activating a different AI model.

[0048] Model transfer may also refer to distributing an AI model over the air interface. This distribution may include distributing parameters of a model structure already known at the receiving end, or a new model with parameters, or both. This distribution may include a complete model or a partial model. Model download may refer to transferring a model from the network to the UE. Model upload may refer to transferring a model from the UE to the network.

[0049] 2 is a diagram showing an example of specifying an AI model. In this example, a UE and a NW (e.g., a base station (BS)) can recognize models #1 and #2 (although they do not need to fully understand the details of the models). The UE may report, for example, the capabilities of model #1 and model #2 to the NW, and the NW may instruct the UE on the AI ​​model to use.

[0050] (Open RAN (ORAN)) The ORAN architecture will be described below with reference to FIG.

[0051] In 5G NR, standardization of open RAN (ORAN / ORAN Alliance) is being considered to reduce the burden on operators in building and operating RAN and to introduce automation using AI / ML models.

[0052] In the ORAN Alliance architecture, in order to realize network operation utilizing the AI / ML model, a RIC (RAN Intelligent Controller) may be defined as a logical node that automates and optimizes the parameter design / configuration / operation of base stations.

[0053] As shown in FIG. 3, the RIC may include a non-real-time RIC and a near real-time RIC (which may simply be called a real-time RIC).

[0054] The non-real-time RIC may be located within Service Management and Orchestration (SMO), which monitors, maintains, and orchestrates the RAN.

[0055] The non-real-time RIC may be connected to the near real-time RIC via an A1 interface.

[0056] The near real-time RIC may be connected to E2 nodes such as an O-eNB (ORAN base station), an O-CU (ORAN central unit), and an O-DU (ORAN distributed unit) via an E2 interface. The SMO may be connected to the O-eNB, the O-CU, and the O-DU (ORAN distributed unit) via an O1 interface.

[0057] The non-real-time RIC may cooperate with a functional unit that provides OAM services within the SMO and collect data accumulated within the E2 node, such as Performance Management Counters, Fault Management Data, and Trace Management Data.

[0058] The near real-time RIC may collect information about the E2 node from the E2 node using the E2 interface, and may control the E2 node according to a policy notified by the non-real-time RIC.

[0059] The ORAN architecture shown in FIG. 3 is merely an example, and is not limited to this example.

[0060] (Analysis) In future wireless communication systems (for example, Rel. 19 and later), use cases that require datasets when utilizing AI / ML models are expected.

[0061] Possible use cases include model training, model validation, model inference, and performance monitoring (verifying performance in an actual field). These use cases may be collectively referred to as model management.

[0062] In addition, it is being considered to utilize data collection in the core network (CN), operation administration and maintenance (management) (OAM), and over the top (OTT) for model training on the UE side.

[0063] However, there has been insufficient consideration of how to specifically manage models.

[0064] If this consideration is insufficient, it may not be possible to properly utilize AI / ML models in future wireless communication systems, which could hinder improvements in communication throughput.

[0065] Therefore, the present inventors came up with a solution to this problem.

[0066] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. Wireless communication methods according to the embodiments may be applied independently or in combination.

[0067] (Various Reinterpretations) In the present disclosure, a word enclosed in "( )" in a sentence may indicate an explanation of the word immediately preceding it (for example, an explanation of spelling), a paraphrase, a specific example, a supplementary explanation, etc. Furthermore, in the present disclosure, a word enclosed in "[ ]" in a sentence may be interpreted including the word in the meaning of the entire sentence, or may be interpreted excluding (ignoring) the word in the meaning of the entire sentence. Note that "( )" and "[ ]" may also be used for purposes / meanings other than those mentioned above.

[0068] In the present disclosure, "A / B" and "at least one of A and B" may be interpreted interchangeably. Also, in the present disclosure, "A / B / C" may mean "at least one of A, B, and C."

[0069] In the present disclosure, terms such as notify, activate, deactivate, indicate (or indicate), select, configure, update, and determine may be read interchangeably. In the present disclosure, terms such as support, control, controllable, operate, and operate may be read interchangeably.

[0070] In the present disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, higher layer parameters, fields, information elements (IEs), settings, etc. may be interchangeable. In the present disclosure, Medium Access Control (MAC) control elements (CEs), update commands, activation / deactivation commands, etc. may be interchangeable.

[0071] In the present disclosure, the higher layer signaling may be, for example, any one of Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information, other messages (e.g., messages from the core network such as positioning protocol (e.g., NR Positioning Protocol A (NRPPa) / LTE Positioning Protocol (LPP)) messages), or a combination thereof.

[0072] In the present disclosure, MAC signaling may use, for example, a MAC Control Element (MAC CE), a MAC Protocol Data Unit (PDU), etc. Broadcast information may be, for example, a Master Information Block (MIB), a System Information Block (SIB), Remaining Minimum System Information (RMSI), Other System Information (OSI), etc.

[0073] In the present disclosure, physical layer signaling may be, for example, Downlink Control Information (DCI), Uplink Control Information (UCI), and the like.

[0074] (Wireless Communication Method) The following describes terms / phrases used in this disclosure.

[0075] In this disclosure, a "dataset" may refer to a set of data samples that are assigned a dataset ID [and dataset-related information].

[0076] In this disclosure, a "data sample" may refer to a component of a data set.

[0077] "Dataset delivery" in this disclosure may refer to the distribution of a dataset (e.g., from a DSM to a consumer / from a provider to a DSM).

[0078] "Data" in this disclosure is a generic term and may refer to a data sample (a component of a data set) or a data set.

[0079] "Data collection" in this disclosure may refer to the collection of data (including the distribution / transfer of data samples).

[0080] "Dataset identification" in this disclosure may refer to a procedure for achieving a common understanding / knowledge of a dataset (e.g., understanding the mapping between dataset IDs and datasets).

[0081] In the present disclosure, a "Dataset manage entity (DSM)" may be an entity that manages a dataset.

[0082] A DSM may include at least one of the following functions: - dataset manage function (DSMF) (e.g., a function for managing dataset identification / dataset distribution / data collection); - dataset storage function (DSSF) (e.g., a function for storing datasets / data); - dataset creating function (DSCF) (e.g., a function for creating a dataset based on data); - dataset registration function (DSRF) (e.g., a function for registering a dataset in the DSM (assigning a dataset ID to a dataset)); - dataset discovery function (DSDF) (e.g., a function for discovering a specific dataset in the DSM); - dataset generation function (DSGF) (e.g., a function for generating data / datasets based on samples / statistical models).

[0083] A DSM may be composed of multiple entities, for example, a DSM may include a DSSF entity and a DSCF entity.

[0084] Multiple types of DSMs may be defined. For example, DSM#1 may include DSMF, and DSM#2 may include DSSF / DSCF / DSRF / DSDF.

[0085] The DSM may be any Core Network (CN) / Network Function (NF) / CN NF / SMO / non real-time RIC / real-time RIC.

[0086] In the present disclosure, the NF may include, for example, at least one of the following: Application Function (AF) (e.g., a function that realizes an application server outside the 5G Core Network (5GC)); Access and Mobility management Function (AMF) (e.g., a function that manages UE registration, location, etc.); Data Network (DN) (e.g., a function that realizes a data network outside 5GC); Location Management Function (LMF) (e.g., a communication control function related to location-based services); Non-3GPP Inter-Working Function (N3IWF) (e.g., a function that connects 5GC with an untrusted non-3GPP access network); Network Exposure Function (NEF) (e.g., a function that provides an application interface for 5GC NF services to the outside); Network Slice Selection Function (NSSF) (e.g., a function that selects a network slice); Network Data Analytics Function (NWDAF) (e.g., a function that analyzes network data). Operation, Administration and Maintenance (Management) (OAM) (e.g., a function that provides means for operation, maintenance and management). Policy Control Function (PCF) (e.g., a function that controls the quality, policy, etc. of data transfer paths). Session Management Function (SMF) (e.g., a function that manages sessions). Trusted Non-3GPP Gateway Function (TNGF) (e.g., a function that connects trusted non-3GPP access networks with 5GC).- Trusted WLAN Interworking Function (TWIF) (e.g., a function that connects a trusted non-3GPP access network with 5GC for a non-5G-capable UE via a Wireless Local Area Network (LAN)). - (Radio) Access Network ((R)AN) (e.g., a function that provides a radio access network). - User Equipment (UE) (e.g., a function that provides user access to network services via the radio interface). - Unified Data Management (UDM) (e.g., a function that stores / manages subscriber information, UE authentication information, etc.). - Unified Data Repository (UDR) (e.g., a function that manages authentication / authorization based on subscriber information). - User Plane Function (UPF) (e.g., a function that transmits user data packets). - Over The Top (OTT) (e.g., content / services / functions provided by an independent provider bypassing the carrier's network). - Analytics Data Repository Function (ADRF) (e.g., a function that stores / manages analytical data in the communications network). Data Collection Analytics Function (DCAF) (e.g., a function that collects / analyzes data in a communication network) Data Collection Coordination Function (DCCF) (e.g., a function that collects / coordinates data in a communication network) Network Repository Function (NRF) (e.g., a function that registers the services of each network function).

[0087] It should be understood that these are merely examples and that other NFs are also covered by the present disclosure.

[0088] In this disclosure, the operation of the DSM will be mainly described, but the DSM (the name of the DSM) is merely an example and may be interpreted as any network component / function.

[0089] A "consumer" in this disclosure may refer to an entity that receives a data set for a particular usage. In this disclosure, a consumer may be, for example, a UE, a RAN node (e.g., a base station), an OAM, a CN NF, an SMO, a non real-time RIC, a real-time RIC, etc.

[0090] In this disclosure, a "requestor" may refer to an entity that requests a DSM to deliver a data set to a consumer. For example, a requestor may be a consumer, a UE, a RAN node, an OAM, a CN NF, an SMO, a non-real-time RIC, a real-time RIC, etc.

[0091] "Provider" in this disclosure may refer to an entity that provides data / data samples / data sets to a DSM.

[0092] In this disclosure, a "source" may refer to an entity from which a DSM can collect data.

[0093] In the present disclosure, the terms "data" and "dataset" may be read interchangeably.

[0094] In the present disclosure, a dataset may include at least one of the following values: - Dataset ID (e.g., an identifier for the dataset); - Feature / Label (e.g., a representation of a data value); - Data value (e.g., the value of a data sample); - Data / Dataset related information.

[0095] The feature may be at least one of those described in Supplementary Note 8 below.

[0096] Labels may also be a feature in this disclosure.

[0097] Fig. 4 is a diagram showing an example of a dataset. In the example shown in Fig. 4, a dataset corresponding to dataset ID = 1 and a dataset corresponding to dataset ID = 2 are shown. Each dataset is composed of multiple data samples, and each data sample corresponds to a data sample ID / feature / label.

[0098] In the present disclosure, a "Model management entity (MM)" may be an entity that manages models and information associated with the models.

[0099] The MM may include at least one of the following functions: - Model manage function (MMF) (e.g., a function for administering model delivery / model registration / model training / model validation); - Model authentication function (MAF) (e.g., a function for authenticating model delivery / model registration / model training / model validation); - Model storage function (MSF) (e.g., a function for storing models); - Model training function (MTF) (e.g., a function for training models based on data); - Model validation function (MVF) (e.g., a function for validating models); - Model registration function (MRF) (e.g., a function for registering models in the MM (assigning a model ID to a model)); - Model discovery function (MDF) (e.g., a function for discovering a specific model in the MM).

[0100] The MM may be composed of multiple entities, for example, the MM may include an MTF entity and an MMF entity.

[0101] Multiple types of MMs may be defined. For example, MM#1 may have MMF, and MM#2 may have MSF / MTF / MRF / MDF.

[0102] The MM may be any NF / CN NF / SMO / non real-time RIC / real-time RIC / base station / OAM.

[0103] In this disclosure, "model validation" may refer to checking a performance model. In this disclosure, "model validation" may include comparing a performance model with performance requirements.

[0104] A "validation entity" in this disclosure may be an entity (eg, MM / MVF) that is [capable of] performing model validation.

[0105] In this disclosure, a "potential validation entity" may be an entity (e.g., MM) that may perform model validation.

[0106] A "validation requestor" in this disclosure may be an entity that can request a validation entity to perform model validation.

[0107] In this disclosure, a "potential verification requestor" may be an entity that may request a verification entity to perform model verification.

[0108] A "validation consumer" in this disclosure may be an entity that can obtain the results of model validation.

[0109] A "potential validation consumer" in this disclosure may be any entity that may obtain the results of model validation.

[0110] The validation entity / requestor / consumer may be any network entity (e.g., UE / RAN node / base station / OAM / CN NF / SMO / non real-time RIC / real-time RIC).

[0111] A "training entity" in this disclosure may be an entity (e.g., MM / MTF) that is [capable of] performing model training.

[0112] In this disclosure, a "potential training entity" may be an entity that may perform model training (e.g., an MM).

[0113] A "training requestor" in this disclosure may be an entity that can request a training entity to perform model training.

[0114] In this disclosure, a "potential training requestor" may be an entity that may request a training entity to perform model training.

[0115] A "training consumer" in this disclosure may be an entity that can obtain a trained model and / or information related to the trained model.

[0116] A "potential training consumer" in this disclosure may be an entity that may obtain a trained model and / or information related to the trained model.

[0117] The training entity / requestor / consumer may be any network entity (e.g., UE / RAN node / base station / OAM / CN NF / SMO / non real-time RIC / real-time RIC).

[0118] "Model distribution" in this disclosure may refer to distributing / transferring a model and / or information associated with the model / functionality.

[0119] In this disclosure, a "model sender" may refer to an entity that distributes models and / or information associated with the models / functions.

[0120] In this disclosure, a "potential model sender" may refer to an entity that may distribute models and / or information associated with the models / functions.

[0121] In this disclosure, a "model requester" may refer to an entity that requests a model sender to deliver a model.

[0122] In this disclosure, a "potential model requestor" may refer to an entity that may request a model sender to deliver a model.

[0123] In this disclosure, a "model consumer" may refer to an entity that obtains models and / or information associated with the models / functions from a model sender.

[0124] In this disclosure, a "potential model consumer" may refer to an entity that may obtain models and / or information associated with the models / functions from a model sender.

[0125] The model sender / requester / consumer may be any network entity (e.g., MM / UE / RAN node / base station / OAM / CN NF / SMO / non real-time RIC / real-time RIC).

[0126] "Model registration" in this disclosure may mean assigning an ID to a model / saved model.

[0127] In this disclosure, a "registration requestor" may refer to an entity that requests a model registration.

[0128] In this disclosure, a "potential registration requestor" may refer to an entity that may request a model registration.

[0129] The registration requestor / registration entity may be any network entity (e.g., MM / UE / RAN node / base station / OAM / CN NF / SMO / non real-time RIC / real-time RIC).

[0130] In the present disclosure, the term "potential" may be omitted. In other words, the terms "potential A" and "A" may be interchangeable.

[0131] <Model Validation> An overview of model validation will be described below.

[0132] A validation entity may validate the performance of the model.

[0133] The validation entity may be triggered to validate a model / function by a validation requestor.

[0134] The triggering of model / function verification is described in detail in the first embodiment (step 1) below.

[0135] The validation entity may obtain a dataset used for model validation, which may be transferred from the DSM.

[0136] The validation entity may acquire a model to be used for model validation. Model acquisition (model distribution) will be described in detail in the fifth embodiment (step 5) below.

[0137] The verification entity may calculate the performance, which will be described in detail in the second embodiment (step 2) below.

[0138] The validation entity may report the validation results to the validation consumer, which will be described in detail in the second embodiment (step 2) below.

[0139] The above-described operations related to model validation are merely examples, and each network element may perform at least one of these operations.

[0140] Fig. 5 shows an example of model validation. In the example shown in Fig. 5, a validation request is first sent from a validation requester to a validation entity (S501). Next, the validation entity acquires a dataset / model (S502) and calculates performance (S503). Furthermore, the validation entity reports the performance to a validation consumer (S504).

[0141] <Model Training> Below, an overview of model training will be explained.

[0142] A training entity may train the model.

[0143] The training entity may be triggered to train / retrain a model / function by a training requestor.

[0144] Triggering of model / function training is described in detail in the third embodiment (step 3) below.

[0145] The training entity may obtain a dataset used for model training, which may be transferred from the DSM.

[0146] The training entity may obtain information about a pre-trained model, which is an original model before training. The obtaining of information about the pre-trained model will be described in detail in the fifth embodiment (step 5) below.

[0147] A training entity may train the model.

[0148] The training entity may distribute / forward the trained model and / or information about the trained model to training / model consumers, as described in detail in the fifth embodiment (Step 5) below.

[0149] The above-described operations related to model validation are merely examples, and each network element may perform at least one of these operations.

[0150] Fig. 6 shows an example of model training. In the example shown in Fig. 6, a training request is first sent from a training requester to a training entity (S601). Next, the training entity acquires a dataset / model (S602) and performs model training (S603). Furthermore, the training entity reports the trained model to a training consumer / model consumer (S604).

[0151] <Model Transfer / Distribution> Below, an overview of model transfer / distribution will be explained.

[0152] A model sender may transfer / distribute models to model consumers.

[0153] In the present disclosure, the terms "transfer" and "delivery" may be interpreted interchangeably.

[0154] The model center may be triggered to distribute a model by a model requester.

[0155] The trigger for model distribution will be described in detail in the fourth embodiment (step 4) below.

[0156] A model sender may distribute models to model consumers.

[0157] Model transfer / distribution will be described in detail in the fifth embodiment (step 5) below.

[0158] Fig. 7 shows an example of model distribution. In the example shown in Fig. 7, first, a model requester sends a request for model transfer to a model sender (S701). Next, the model sender transfers / distributes the model to a model consumer (S702).

[0159] <Model Registration> The following provides an overview of model registration.

[0160] The MM may register the model.

[0161] The MM may be triggered to register a model by a registration requester.

[0162] The trigger for model registration will be described in detail in the sixth embodiment (step 6) below.

[0163] The MM may obtain a model to register.

[0164] For example, the MM may obtain the model to be registered through model distribution (for example, model distribution according to step 5).

[0165] Also, for example, the MM may obtain the model to be registered by model training.

[0166] The MM may register the model, for example, the MM may assign an ID to the model / saved model.

[0167] Model registration will be described in detail in the sixth embodiment (step 6) below.

[0168] The MM may notify / report the registration result to the registration requester.

[0169] The reporting of model registration will be described in detail in the sixth embodiment (step 6) below.

[0170] The MM may receive a model discovery request for a registered model.

[0171] The model discovery request will be described in detail in the seventh embodiment (step 7) below.

[0172] FIG. 8 is a diagram showing an example of model registration. In the example shown in FIG. 8, first, a registration requester transmits a request for model registration to an MM (S801). Next, the MM performs at least one of receiving a model distribution from a model sender (S802a) and training the model (S802b). Furthermore, the MM notifies the registration requester of the result of the model registration (S803).

[0173] <First Embodiment (Procedure 1)> The first embodiment (Procedure 1) relates to model verification.

[0174] Model validation may include at least one of the following steps 1 and 2.

[0175] <<Embodiment 1-1 (Step 1)>> A verification entity may report specific capabilities.

[0176] For example, a verification entity may report its capabilities directly to potential verification requesters.

[0177] Also, for example, a validation entity may register a service / function with a particular entity (e.g., a Network Repository Function (NRF)), which may then notify a validation requester of the registration of the service / function (corresponding to a particular capability).

[0178] The reporting of a particular capability may be a report / notification regarding at least one of the following: - What / which model can be verified (e.g., model ID); - What / which / what dataset can be used for model validation (e.g., dataset ID); - What / which performance / metric can be calculated; - What / which performance requirements can be used; - What / which information can be reported to the validation requester (this will be described in detail in embodiment 1-2); - Model information that the validation entity can verify (model information may be specified as described in supplement 1 below).

[0179] According to embodiment 1-1, the request to be sent to the verification entity can be appropriately defined.

[0180] <<Embodiment 1-2 (Step 2)>> A validation requester may request a validation entity to validate a model.

[0181] The request may include information about at least one of the following: - What / which model is to be validated (e.g., model ID); - What / which / what dataset is to be used for model validation (e.g., dataset ID); - What / which performance / metric is to be calculated; - What / which performance requirements are to be used (may be cross-validation parameters, for example); - What / which information is to be reported to the validation consumer.

[0182] The information indicating what / which information is reported to the validation consumer may include, for example, information on at least one of the following: Whether the performance requirements are met for each model; Models / model IDs that meet the performance requirements; Performance / metrics for each model.

[0183] The information about the performance / metrics of each model may include, for example, information about at least one of the following: performance / metrics of [only] models that meet the performance requirements; robustness / sensitivity / error analysis of the model; and reliability of the performance requirements.

[0184] According to the first and second embodiments, the request to be sent to the verification entity can be sent appropriately.

[0185] 9 is a diagram showing an example of a request for model validation according to the first embodiment. In the example shown in FIG. 9, first, the validation entity reports capability information to the validation requester (S901) (or the validation entity may report the capability information via NRF). Next, the validation requester requests model validation from the validation entity (S902).

[0186] According to the first embodiment, model verification can be appropriately triggered.

[0187] Second Embodiment (Procedure 2) The second embodiment (Procedure 2) relates to deriving / reporting the results of model validation.

[0188] The validation entity may derive results of the model validation, and then report the results to the validation consumer.

[0189] The validation results may include information on at least one of the following: Whether the performance / metric of each model exceeds / falls short of the performance requirements; Model IDs corresponding to the performance / metrics that exceed / fall short of the performance requirements; Performance / metrics of each model (e.g., at least one of the model robustness / sensitivity, error analysis, and reliability of the performance requirements).

[0190] The information and / or performance / metrics included in the validation results may be predefined / configured / instructed or may be determined based on the associated model, e.g., performance / metric validation results may be reported only for models that exceed / fall short of a performance / metric performance requirement.

[0191] According to the second embodiment, the model verification results can be appropriately derived and reported.

[0192] <Third Embodiment (Procedure 3)> The third embodiment relates to model training.

[0193] Model training may include at least one of steps 1 to 3 below.

[0194] <<Embodiment 3-1 (Step 1)>> A training entity may report a specific capability.

[0195] For example, the training entity may report the capabilities directly to the training requester.

[0196] Also, for example, a training entity may register a service / function with a particular entity (e.g., NRF), which may then notify a training requester of the registration of the service / function (corresponding to a particular capability).

[0197] The reporting of a particular capability may be reporting / informing about at least one of the following: Model ID / model information of models that can be trained by the training entity (model information may be defined as described in Supplementary Note 1 below); Model ID / model information of models that can be retrained by the training entity (e.g., may be called potential pre-trained models) (model information may be defined as described in Supplementary Note 1 below); What / which datasets can be used for model training (e.g., dataset ID); Training information for AI / ML models (described in Supplementary Note 1 below); Available performance requirements (including performance metrics) of the model to be trained.

[0198] According to embodiment 3-1, the request to be sent to the training entity can be appropriately defined.

[0199] <<Embodiment 3-2 (Step 2)>> A training requester may request a training entity to train a model.

[0200] The request may include information about at least one of the following: Model information (model information may be defined as described in Supplementary Note 1 below); The model to be retrained by the training entity (e.g., may be called a pre-trained model); What / which / what dataset(s) are used for model training (e.g., dataset ID); Training information for AI / ML models (described in Supplementary Note 1 below); Performance requirements (including performance metrics) for the model to be trained.

[0201] According to embodiment 3-2, a request to a training entity can be sent appropriately.

[0202] 10 is a diagram illustrating an example of a request for model training according to the third embodiment. In the example illustrated in FIG. 10, first, the training entity reports capability information to the training requester (S1001) (or the training entity may report the capability information via NRF). Next, the training requester requests model training from the training entity (S1002).

[0203] <<Embodiment 3-3 (Step 3)>> The training entity may report the training status to the training requester.

[0204] The training entity may, for example, send information regarding at least one of the following: Model information (model information may be defined as described in Appendix 1 below); Performance metrics of the current model; Resource usage of training; Expected time to achieve required performance; Training time; Learning rate.

[0205] According to the third embodiment, model training can be appropriately triggered.

[0206] <Fourth embodiment (procedure 4)> The fourth embodiment (procedure 4) relates to a request for model distribution.

[0207] The request for model distribution may include at least one of steps 1 to 3 below.

[0208] <<Embodiment 4-1 (Step 1)>> A model requester may receive information regarding transferable model information.

[0209] A potential model requestor may request a model sender to send information regarding at least one of the models that can be transferred and how the models can be transferred.

[0210] The message / signaling related to the request may include information about at least one of the following: - Transferable model ID. - Model information about the transferable model (model information may be defined as described in Supplementary Note 1 below). - Model distribution related information about the transferable model (model distribution related information may be defined as described in Supplementary Note 6 below). - Entity related information about the entity for which transferable model information is requested (entity related information may be defined as described in Supplementary Note 7 below).

[0211] After sending / receiving the request, the potential model requester may receive a response to the request.

[0212] The response may include information on at least one of the following: Acknowledgment (for example, it may be transferable model information. Transferable data information will be described in detail in the following embodiment 4-2). Reject (for example, it may include the reason for rejection).

[0213] 11 is a diagram showing an example of a request for transferable model information according to embodiment 4-1. In the example shown in FIG. 11, first, the potential model requester transmits a request for transferable model information to the potential model sender (S1101). Next, the potential model sender transmits a response signal (e.g., transferable model information) in response to the request to the potential model requester (S1102).

[0214] According to the embodiment 4-1, it is possible to appropriately request transferable model information.

[0215] <<Embodiment 4-2 (Step 2)>> A potential model requester may notify transferable model information.

[0216] Potential model requesters may receive information regarding at least one of the models that are available for transfer and how the models can be transferred.

[0217] The informational message / signaling may include information about at least one of the following: - Transferable model ID; - Model information about the transferable model (model information may be defined as described in Supplementary Note 1 below); - Model distribution related information about the transferable model (model distribution related information may be defined as described in Supplementary Note 6 below); - Entity related information about the entity for which the transferable model information is requested (entity related information may be defined as described in Supplementary Note 7 below); - A requested permission token for the model request.

[0218] The message / signaling relating to the information may include, for example, part / subset (only) of the information requested in embodiment 4-1 (step 1) above.

[0219] The informational message / signaling may, for example, contain [only] information that is different (eg, updated) from when the previous message / signaling was received.

[0220] 12 is a diagram showing an example of notification of transferable model information according to embodiment 4-2. In the example shown in Fig. 12, the potential model sender transmits transferable model information (#1 and #2) to the potential model requester (S1201 / S1202).

[0221] In the example shown in FIG. 12, for example, transferable model information #2 may include [only] information that has been updated from transferable model information #1.

[0222] According to the embodiment 4-2, it is possible to appropriately notify transferable model information.

[0223] <<Embodiment 4-3 (Step 3)>> A model requester may receive information regarding transferable model information.

[0224] A model requester (e.g., MM / UE / ARN node / base station / NF / OAM / AF / SMO / non real-time RIC / real-time RIC) may request a model sender to send information regarding at least one of the model information that can be transferred and how the model can be transferred.

[0225] The message / signaling for the request may include information about at least one of the following: - Requested model information (model information may be defined as described in Supplementary Note 1 below); - Model ID; - Model distribution related information of the requested transferable model (model distribution related information may be defined as described in Supplementary Note 6 below); - Entity related information of the model requester / consumer (entity related information may be defined as described in Supplementary Note 7 below); - Permission token for the model request.

[0226] After the model sender receives the request, the model requester may receive a response to the request from the model sender.

[0227] The response may include information regarding at least one of the following: Acknowledgment. Reject (which may include, for example, the reason for the rejection).

[0228] Fig. 13 is a diagram showing an example of a model distribution request according to embodiment 4-3. In the example shown in Fig. 13, first, the model requester requests the model sender to distribute the model (S1301). Next, the model sender transmits a response to the request to the model requester (S1302). Furthermore, the model sender distributes the model to the model consumer (S1303).

[0229] According to embodiment 4-3, transferable model information can be appropriately requested.

[0230] According to the fourth embodiment, a request for model distribution can be made appropriately.

[0231] <<Variations of the Fourth Embodiment>> The following describes permission / authentication for model distribution / transfer.

[0232] <<<<Option 1>>> The model requester may request the model sender to distribute the model (model distribution may correspond to step 3 in the fourth embodiment above).

[0233] The potential model sender may send the MM information along with the MAF, and then obtain an authorization token.

[0234] A model requester may request an authorization token from the MM with the MAF. The request may include information about at least one of the following: entity-related information of the potential model requester (the entity-related information may be defined as described in Supplementary Note 7 below); entity-related information of the potential model sender (the entity-related information may be defined as described in Supplementary Note 7 below); requested model information (the model information may be defined as described in Supplementary Note 1 below); model ID; model distribution-related information of the requested model distribution (the model distribution-related information may be defined as described in Supplementary Note 6 below).

[0235] The model requestor may receive a response signal to the request, which may indicate either an authorization token or a reason for denial.

[0236] The model requester may request the model sender to distribute the model along with the authorization token (model distribution may correspond to step 3 in the fourth embodiment above).

[0237] 14 is a diagram showing an example of model distribution according to option 1 in a variation of the fourth embodiment. In the example shown in FIG. 14, first, the model requester sends a model distribution request to the model sender (S1401). Next, the model sender instructs the model requester to redirect to the MM with the MAF (to obtain an authorization token) (S1402).

[0238] Next, the model requester requests the MM to obtain a permission token (S1403), and the MM sends the permission token to the model requester (S1404).Then, the model requester requests the model sender to distribute the model (S1405).

[0239] <<<<Option 2>>> The model requester may request the model sender to distribute the model (model distribution may correspond to step 3 in the fourth embodiment above).

[0240] A potential model sender may query the MM with the MAF to confirm permission for the model distribution requested by the model requester.

[0241] The MM with the MAF may send a response to the query to the model sender, which may include any of the following: Acknowledgment (e.g., may include information indicating the valid authorization period), Reject (e.g., may include the reason for the rejection).

[0242] The potential model sender may send a response signal to a request from the model requester [based on the response from the MM].

[0243] FIG. 15 is a diagram showing an example of model distribution according to option 2 in a variation of the fourth embodiment. In the example shown in FIG. 15, first, the model requester sends a model distribution request to the model sender (S1501). Next, the model sender confirms with the MM, which includes the MAF, whether permission for model distribution is granted (S1502). Next, the model sender receives permission (acknowledgment) from the MM (S1503). Thereafter, the model sender sends a response to the model distribution request to the model requester (S1504).

[0244] According to the variation of the fourth embodiment described above, permission / authentication regarding model distribution can be performed appropriately.

[0245] Fifth Embodiment (Procedure 5) The fifth embodiment (Procedure 5) relates to model distribution.

[0246] Model distribution may include at least one of the following steps 1 and 2.

[0247] <<Embodiment 5-1 (Step 1)>> A model sender may initiate model distribution to a model consumer.

[0248] The model sender may notify / receive / send what / which information is to be transferred / delivered.

[0249] For example, the model sender may notify / receive / send model information (model information may be defined as described in Supplementary Note 1 below) of the model to be transferred.

[0250] The model sender may notify / receive / send how it is to be transferred / distributed.

[0251] For example, the model sender may notify / receive / send model distribution related information (model distribution related information may be defined as described in Appendix 6 below) of the requested model distribution.

[0252] According to the embodiment 5-1, the model transfer can be started appropriately.

[0253] <<Embodiment 5-2 (Step 2)>> A model sender / consumer may terminate model distribution in certain cases.

[0254] The particular case may be, for example, when the model sender receives an end instruction.

[0255] The model sender may receive the termination indication from the model consumer / requester.

[0256] The termination instruction may include information about at least one of the following: - The reason why the model distribution is terminated. - How many model distribution messages / signalings will be sent after the termination instruction is sent / received.

[0257] The particular case may also be when the model consumer / requester receives a termination indication.

[0258] The model consumer / requester may receive the termination indication from the model sender.

[0259] The termination instruction may include information about at least one of the following: - The reason why the model distribution is terminated. - How many model distribution messages / signalings will be sent after the termination instruction is sent / received.

[0260] The particular case may also be when a model sender has completed distribution of multiple (eg, all) models.

[0261] For example, the particular case may be when the model sender notifies the model requester that the model delivery is complete.

[0262] According to the embodiment 5-2, the model transfer can be appropriately terminated.

[0263] According to the fifth embodiment, model transfer can be performed appropriately.

[0264] <Sixth Embodiment (Procedure 6)> The sixth embodiment (Procedure 6) relates to model registration.

[0265] Model registration may include at least one of steps 1 to 4 below.

[0266] <<Embodiment 6-1 (Step 1)>> An MM with an MRF may report specific capabilities regarding model registration.

[0267] For example, the MM may report the capabilities directly to the registration requester.

[0268] Also, for example, the MM may register a service / function with a particular entity (e.g., NRF), which may then notify the registration requester of the service / function (corresponding to a particular capability).

[0269] The particular capability may indicate, for example, model information (which may be defined as described in Supplementary Note 1 below) regarding models that the MM can store / register.

[0270] 16 is a diagram showing an example of model registration according to embodiment 6-1. In the example shown in FIG. 16, first, an MM with an MRF reports capability information to a registration requester (S1601) (alternatively, the MM may report the capability information via an NRF). Next, the registration requester requests the MM to register a model (S1602).

[0271] According to the embodiment 6-1, the request for model registration to be sent to the MM can be sent appropriately.

[0272] <<Embodiment 6-2 (Step 2)>> A registration requester may request a validation entity to validate a model.

[0273] A request for model validation may include information on at least one of the following: - Model information requested to be registered (the model information may be specified as described in Supplementary Note 1 below); - Performance of the model requested to be registered; - Model distribution related information regarding model distribution for the model to be registered (the model distribution related information may be specified as described in Supplementary Note 6 below); - Entity related information of the model registration (the entity related information may be specified as described in Supplementary Note 7 below); - Inference type (for example, this may be information indicating the category of inference that the model can provide).

[0274] <<Embodiment 6-3 (Step 3)>> The MM may perform model registration.

[0275] For example, the MM may assign an ID to the model / saved model.

[0276] <<Embodiment 6-4 (Step 4)>> The MM may notify the registration requester of a response to the request for model registration (for example, the result of model registration).

[0277] The response may include, for example, any of the following: Acknowledgment (which may include, for example, the ID assigned to the model being registered). Reject (which may include, for example, the reason for the rejection).

[0278] According to the sixth embodiment (embodiments 6-1, 6-2, 6-3, and 6-4), model registration can be performed appropriately.

[0279] Seventh Embodiment (Procedure 7) The seventh embodiment (Procedure 7) relates to model discovery.

[0280] Model discovery may include at least one of steps 1 to 4 below.

[0281] <<Embodiment 7-1 (Step 1)>> A model discovery requester may request the MM to discover a model.

[0282] A request for model discovery may include information about at least one of the following: - Model information for which discovery is requested (model information may be specified as described in Supplementary Note 1 below); - Capabilities of the model for which discovery is requested; - Model distribution related information regarding model distribution for the model to be discovered / registered (model distribution related information may be specified as described in Supplementary Note 6 below); - Model discovery entity related information (entity related information may be specified as described in Supplementary Note 7 below); - Inference type (e.g., may be information indicating the category of inference that the model can provide).

[0283] <<Embodiment 7-2 (Step 2)>> The MM may discover a model.

[0284] <<Embodiment 7-3 (Step 3)>> The MM may notify the discovery requester of a response to the model discovery request (for example, the result of the model discovery).

[0285] The response may include, for example, any of the following: Acknowledgment (which may include, for example, the ID assigned to the model being registered). Reject (which may include, for example, the reason for the rejection).

[0286] <<Embodiment 7-4 (Step 4)>> The MM may provide the discovered model to the requester according to / based on the provided information.

[0287] According to the seventh embodiment, model discovery can be performed appropriately.

[0288] <Variations of the First, Second, Third, and Sixth Embodiments> The following describes permission / authentication for model registration / training / validation.

[0289] <<Option 1>> Enrollment / Training / Validation A requester may request enrollment / training / validation of a model from a MM with MRF / MTF / MVF.

[0290] The MM with the MRF / MTF / MVF may send information of the MM with the MAF, and then the MM with the MRF / MTF / MVF may obtain an authorization token.

[0291] An MM with MRF / MTF / MVF may request an authorization token from an MM with MAF. The request may include information about at least one of the following: Entity-related information of the potential enrollment / training / validation requester (the entity-related information may be defined as described in Supplementary Note 7 below). Entity-related information of the MM with MRF / MTF / MVF (the entity-related information may be defined as described in Supplementary Note 7 below). Model information of the model for which enrollment / training / validation is requested (the model information may be defined as described in Supplementary Note 1 below). Model ID.

[0292] The enrollment / training / validation requester may receive a response signal to the request, which may indicate either an authorization token or a reason for denial.

[0293] An enrollment / training / validation requester may request enrollment / training / validation of a model with an authorization token from a MM with an MRF / MTF / MVF.

[0294] 17 is a diagram showing an example of model registration according to option 1 in the variations of the first, second, third, and sixth embodiments. In the example shown in FIG. 17, first, a registration requester transmits a model registration request to an MM with an MRF (S1701). Next, the MM with an MRF instructs the registration requester to redirect to an MM with an MAF (to obtain an authorization token) (S1702).

[0295] Next, the registration requester requests the MM with the MAF to obtain an authorization token (S1703), and the MM with the MAF sends the authorization token to the registration requester (S1704). After that, the registration requester requests the MM with the MRF to register the model (S1705).

[0296] <<Option 2>> Enrollment / Training / Validation A requester may request enrollment / training / validation of a model from a MM with MRF / MTF / MVF.

[0297] The MM with the MRF / MTF / MVF may query the MM with the MAF to confirm authorization for the model registration / training / validation requested by the registration / training / validation requestor.

[0298] The MM with the MAF may send a response to the query to the MM with the MRF / MTF / MVF. The response may include any of the following: Acknowledgment (e.g., information indicating the valid authorization period may be included). Reject (e.g., the reason for the rejection may be included).

[0299] The MM with MRF / MTF / MVF may send a response signal to a request from the enrollment / training / validation requester [based on the response from the MM with MAF].

[0300] FIG. 18 is a diagram illustrating an example of model registration according to option 2 in the variations of the first, second, third, and sixth embodiments. In the example illustrated in FIG. 18 , first, a registration / training / validation requester transmits a model registration request to an MM with an MRF (S1801). Next, the MM with an MRF confirms permission for model registration with the MM with an MAF (S1802). Next, the MM with an MRF receives permission (acknowledgment) from the MM with an MAF (S1803). Thereafter, the MM with an MRF transmits a response to the request for model registration to the registration requester (S1804).

[0301] According to the variations of the first, second, third and sixth embodiments described above, authorization / authentication regarding model registration / training / verification can be performed appropriately.

[0302] <Supplementary Note> <<Model Information (Supplementary Note 1)>> In the present disclosure, AI model information may mean information including at least one of the following: - Input / output information of the AI ​​model. - Pre-processing / post-processing information for the input / output of the AI ​​model. - Parameter information of the AI ​​model. - Training information for the AI ​​model. - Inference information for the AI ​​model. - Performance information regarding the AI ​​model.

[0303] Here, the input / output information of the AI ​​model may include information on at least one of the following: - Contents of the input / output data (e.g., RSRP, SINR, amplitude / phase information in the channel matrix (or precoding matrix), information on the angle of arrival (Angle of Arrival (AoA)), information on the angle of departure (Angle of Departure (AoD)), location information); - Auxiliary information of the data (which may be called meta-information); - Type of the input / output data (e.g., immutable value, floating-point number); - Bit width of the input / output data (e.g., 64 bits for each input value); - Quantization interval (quantization step size) of the input / output data (e.g., 1 dBm for L1-RSRP); - Range that the input / output data can take (e.g., [0, 1]).

[0304] In the present disclosure, the information on AoA may include information on at least one of an azimuth angle of arrival and a zenith angle of arrival (ZoA). The information on AoD may include information on at least one of an azimuth angle of departure and a zenith angle of departure (ZoD).

[0305] In the present disclosure, location information may be location information related to a UE / NW. The location information may include at least one of information (e.g., latitude, longitude, altitude) obtained using a positioning system (e.g., a satellite positioning system (Global Navigation Satellite System (GNSS), Global Positioning System (GPS), etc.)), information about a BS neighboring (or serving) the UE (e.g., a BS / cell identifier (ID), a BS-UE distance, a direction / angle of the BS (UE) as seen from the UE (BS), coordinates of the BS (UE) as seen from the UE (BS) (e.g., X / Y / Z axis coordinates), etc.), a specific address of the UE (e.g., an Internet Protocol (IP) address), etc. The location information of the UE is not limited to information based on the position of the BS, and may be information based on a specific point.

[0306] The location information may include information about its implementation (e.g., location / position / orientation of antennas, location / orientation of antenna panels, number of antennas, number of antenna panels, etc.).

[0307] The location information may include mobility information, which may include information indicating at least one of information indicating a mobility type, a moving speed of the UE, an acceleration of the UE, and a moving direction of the UE.

[0308] Here, the mobility type may correspond to at least one of a fixed location UE, a movable / moving UE, a no mobility UE, a low mobility UE, a middle mobility UE, a high mobility UE, a cell-edge UE, a not-cell-edge UE, etc.

[0309] In the present disclosure, environmental information (for data) may be information about the environment in which the data is acquired / used, and may correspond to, for example, frequency information (such as a band ID), environmental type information (information indicating at least one of indoor, outdoor, Urban Macro (UMa), Urban Micro (Umi), etc.), information indicating Line Of Site (LOS) / Non-Line Of Site (NLOS), etc.

[0310] Here, LOS may mean that the UE and the BS are in an environment where they can see each other (or there is no obstruction), and NLOS may mean that the UE and the BS are not in an environment where they can see each other (or there is an obstruction). The information indicating LOS / NLOS may indicate a soft value (e.g., the probability of LOS / NLOS) or a hard value (e.g., either LOS or NLOS).

[0311] In the present disclosure, meta-information may mean, for example, information regarding input / output information suitable for an AI model, information regarding acquired / acquirable data, etc. Specifically, meta-information may include information regarding beams of RS (e.g., CSI-RS / SRS / SSB, etc.) (e.g., the pointing angle of each beam, the 3 dB beam width, the shape of the pointed beam, the number of beams), layout information of the gNB / UE antenna, frequency information, environmental information, meta-information ID, etc. Note that meta-information may be used as input / output of the AI ​​model.

[0312] The pre-processing / post-processing information for the input / output of the AI ​​model may include information on at least one of the following: Whether to apply normalization (e.g., Z-score normalization (standardization), min-max normalization); Parameters for normalization (e.g., mean / variance for Z-score normalization, min / max for min-max normalization); Whether to apply a specific numerical conversion method (e.g., one hot encoding, label encoding, etc.); Selection rules for whether to use as training data.

[0313] For example, Z-score normalization (x) is performed as a preprocessing step for input information x. ｎｅｗ = (x - μ) / σ, where μ is the mean of x and σ is the standard deviation) ｎｅｗ may be input to the AI ​​model, and the output y ｏｕｔ may be subjected to post-processing to obtain the final output y.

[0314] The information on the parameters of the AI ​​model may include information on at least one of the following: - Information on weights in the AI ​​model (e.g., neuron coefficients (connection coefficients)); - Structure of the AI ​​model; - Type of the AI ​​model as a model component (e.g., Residual Network (ResNet), DenseNet, RefineNet, Transformer model, CRBlock, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)); - Function of the AI ​​model as a model component (e.g., decoder, encoder).

[0315] Note that the weight information in the AI ​​model may include information on at least one of the following: - Bit width (size) of the weight information; - Quantization interval of the weight information; - Granularity of the weight information; - Range that the weight information can take; - Weight parameters in the AI ​​model; - Information on the difference from the AI ​​model before update (if updating); - Weight initialization method (e.g., zero initialization, random initialization (based on normal distribution / uniform distribution / truncated normal distribution), Xavier initialization (for sigmoid function), He initialization (for rectified linear units (ReLU))).

[0316] The structure of the AI ​​model may also include information about at least one of the following: the number of layers, the type of layer (e.g., convolutional layer, activation layer, dense layer, normalization layer, pooling layer, attention layer), layer information, time series specific parameters (e.g., bidirectionality, time step), parameters for training (e.g., type of function (e.g., L2 regularization, dropout function, etc.), where (e.g., after which layer) to place this function).

[0317] The layer information may include information about at least one of the following: Number of neurons in each layer; Kernel size; Stride for pooling / convolutional layers; Pooling method (MaxPooling, AveragePooling, etc.); Residual block information; Number of heads; Normalization method (Batch normalization, instance normalization, layer normalization, etc.); Activation function (Sigmoid, tanh function, ReLU, leaky ReLU information, Maxout, Softmax).

[0318] An AI model may be included as a component of another AI model, for example, an AI model that includes model component #1, ResNet, model component #2, a Transformer model, a dense layer, and a normalization layer in that order.

[0319] The training information for the AI ​​model may include information about at least one of the following: - Information for the optimization algorithm (e.g., type of optimization (Stochastic Gradient Descent (SGD)), AdaGrad, Adam, etc.), parameters of the optimization (learning rate, momentum information, etc.); - Information on the loss function (e.g., information on metrics of the loss function (Mean Absolute Error (MAE)), Mean Square Error (MSE), Cross Entropy Loss, NLL Loss, Kullback-Leibler (KL) Divergence, etc.)); - Parameters to be frozen for training (e.g., layers, weights); - Parameters to be updated (e.g., layers, weights); - Parameters to be (used as) initial parameters for training (e.g., layers, weights); - Method of training / updating the AI ​​model (e.g., (recommended) number of epochs, batch size, number of data to use for training).

[0320] The inference information for the AI ​​model may include information regarding decision tree branch pruning, parameter quantization, and functions of the AI ​​model, etc. Here, the functions of the AI ​​model may correspond to at least one of, for example, time domain beam prediction, spatial domain beam prediction, an autoencoder for CSI feedback, and an autoencoder for beam management.

[0321] An autoencoder for CSI feedback may be used as follows: The UE inputs the CSI / channel matrix / precoding matrix into the AI ​​model of the encoder and transmits the encoded bits as CSI feedback (CSI report). The BS inputs the received encoded bits into the AI ​​model of the decoder to reconstruct the CSI / channel matrix / precoding matrix, which is the output.

[0322] In spatial domain beam prediction, the UE / BS may input measurement results (beam quality, e.g., RSRP) based on sparse (or thick) beams into an AI model and output dense (or thin) beam quality.

[0323] In time domain beam prediction, the UE / BS may input time series (past, present, etc.) measurement results (beam quality, e.g., RSRP) into an AI model and output future beam quality.

[0324] The performance information regarding the AI ​​model may include information regarding the expected value of a loss function defined for the AI ​​model.

[0325] The AI ​​model information in the present disclosure may include information regarding the application range (applicable range) of the AI ​​model. The application range may be indicated by a physical cell ID, a serving cell index, etc. The information regarding the application range may be included in the above-mentioned environment information.

[0326] AI model information regarding a specific AI model may be predetermined in a standard or may be notified to a UE from a network (NW). An AI model defined in a standard may be referred to as a reference AI model. AI model information regarding a reference AI model may be referred to as reference AI model information.

[0327] Note that the AI ​​model information in the present disclosure may include an index for identifying the AI ​​model (which may be referred to as, for example, an AI model index, an AI model ID, a model ID, etc.). The AI ​​model information in the present disclosure may include an AI model index in addition to / instead of the input / output information of the AI ​​model described above. The association between the AI ​​model index and the AI ​​model information (for example, input / output information of the AI ​​model) may be predetermined in a standard, or may be notified to the UE from the NW.

[0328] The AI ​​model information in the present disclosure may be associated with an AI model and may be referred to as AI model relevant information, simply relevant information, etc. The AI ​​model relevant information does not need to explicitly include information for identifying the AI ​​model. The AI ​​model relevant information may be information that includes only meta information, for example.

[0329] In the present disclosure, the ML model file information may be at least one of the format / size / encoding of the ML model file, the runtime context (eg, runtime environment / libraries), and the required computational resources.

[0330] <<Notification of Information to UE (Supplementary Note 2)>> In the above-described embodiments, any information may be notified to the UE [from a Network (NW) (e.g., a Base Station (BS))] (in other words, reception of any information from the BS by the UE) using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel (e.g., PDCCH, PDSCH, reference signal), or a combination thereof.

[0331] When the notification is performed by a MAC CE, the MAC CE may be identified by including a new Logical Channel ID (LCID) in the MAC subheader, which is not defined in existing standards.

[0332] When the notification is made by DCI, the notification may be made by a specific field of the DCI, a Radio Network Temporary Identifier (RNTI) used to scramble Cyclic Redundancy Check (CRC) bits assigned to the DCI, the format of the DCI, etc.

[0333] Furthermore, notification of any information to the UE in the above embodiments may be performed periodically, semi-persistently, or aperiodically.

[0334] <<Notification of Information from UE (Supplementary Note 3)>> In the above-described embodiments, notification of any information from the UE [to the NW] (in other words, transmission / report of any information from the UE to the BS) may be performed using physical layer signaling (e.g., UCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel (e.g., PUCCH, PUSCH, PRACH, reference signal), or a combination thereof.

[0335] When the notification is performed by a MAC CE, the MAC CE may be identified by including a new LCID, which is not defined in existing standards, in the MAC subheader.

[0336] If the notification is made by UCI, the notification may be transmitted using PUCCH or PUSCH.

[0337] Furthermore, any information in the above-described embodiments may be notified from the UE periodically, semi-persistently, or aperiodically.

[0338] <<Application of Each Embodiment (Supplementary 4)>> In a UE / BS, specific (one or more) processes / operations / controls / assumptions / information for at least one of the above-mentioned embodiments may be applied (used) when one or more of the following conditions are met: - A higher layer parameter indicating the specific processes / operations / controls / assumptions / information is configured; - The specific processes / operations / controls / assumptions / information is determined based on related higher layer parameters; - The specific processes / operations / controls / assumptions / information is specified / activated / triggered by a MAC CE / DCI / UCI / resource / channel / RS; - A specific UE capability indicating (or related to) the specific processes / operations / controls / assumptions / information is reported or supported; - The application of the specific processes / operations / controls / assumptions / information is determined based on specific conditions.

[0339] The particular UE capability may indicate that the particular process / action / control / assumption / information is supported.

[0340] Furthermore, the above-mentioned specific UE capability may be a capability that is applied across all frequencies (commonly regardless of frequency), or may be a capability for each frequency (e.g., one or a combination of a cell, a band, a band combination, a BWP, a component carrier, etc.), or may be a capability for each frequency range (e.g., Frequency Range 1 (FR1), FR2, FR3, FR4, FR5, FR2-1, FR2-2), or may be a capability for each subcarrier spacing (SubCarrier Spacing (SCS)), or may be a capability for each Feature Set (FS) or Feature Set Per Component-carrier (FSPC).

[0341] Furthermore, the specific UE capability may be a capability that is applied to all duplexing methods (commonly regardless of the duplexing method), or may be a capability for each duplexing method (e.g., Time Division Duplex (TDD) or Frequency Division Duplex (FDD)).

[0342] If the above conditions are not met, the UE / BS may follow the behavior specified in existing 3GPP releases.

[0343] <<Supplementary Note 5>> In the present disclosure, functionality may be a set of parameters (e.g., a set of parameters for CSI prediction / beam prediction / CSI compression) that are supported based on the conditions indicated by the UE capabilities.

[0344] In the present disclosure, the condition may be information indicated by UE capabilities.

[0345] In the present disclosure, additional conditions may not be indicated by UE capability information and may be assumed by training.

[0346] <<Supplementary Note 6>> The model distribution related information in the present disclosure may include at least one of the following information: - Periodicity type of model distribution signaling / message (e.g., periodic / semi-persistent / aperiodic) - Periodicity of model distribution signaling / message - Number of model distribution signals / messages.

[0347] <<Supplementary Note 7>> Entity-related information in the present disclosure may include at least one of the following information: PLMN / MNO to which the entity belongs, Area / location in which the entity is located, Access tokens held by the entity.

[0348] (Supplementary Notes) The following inventions are supplemented with respect to one embodiment of the present disclosure. [Supplementary Note 1-1] A network element having a receiver that receives a request for model validation, and a controller that calculates model performance based on the request. [Supplementary Note 1-2] A network element according to Supplementary Note 1-1, wherein the controller reports capability information related to the model validation. [Supplementary Note 1-3] A network element according to Supplementary Note 1-1 or 1-2, wherein the controller calculates the model performance based on at least one of a model validation dataset and a model for model validation. [Supplementary Note 1-4] A network element according to any one of Supplementary Notes 1-1 to 1-3, wherein the controller reports the calculated model performance. [Supplementary Note 2-1] A network element having a receiver that receives a request for model training, and a controller that performs model training based on the request. [Supplementary Note 2-2] A network element according to Supplementary Note 2-1, wherein the controller reports capability information related to the model training. [Supplementary Note 2-3] The network element according to Supplementary Note 2-1 or Supplementary Note 2-2, wherein the control unit performs the model training based on at least one of a model training dataset and a model for model training. [Supplementary Note 2-4] The network element according to any of Supplements 2-1 to 2-3, wherein the control unit reports on the trained model. [Supplementary Note 3-1] A network element having a receiving unit that receives a request for model transfer, and a control unit that controls start and end of model transfer based on the request. [Supplementary Note 3-2] The network element according to Supplementary Note 3-1, wherein the receiving unit receives a request for information on transferable models. [Supplementary Note 3-3] The network element according to Supplementary Note 3-1 or Supplementary Note 3-2, wherein the control unit controls start of model transfer based on at least one of information indicating which model is to be transferred and information indicating how the model is to be transferred. [Supplementary Note 3-4] The network element according to any of Supplements 3-1 to 3-3, wherein the control unit controls end of model transfer based on reception of an instruction to end model transfer.[Supplementary Note 3-5] A network element according to any one of Supplementary Notes 3-1 to 3-4, wherein the control unit controls termination of model transfer based on transmission of an instruction to terminate model transfer. [Supplementary Note 4-1] A network element having a receiving unit that receives a request for model registration, and a control unit that performs model registration based on the request. [Supplementary Note 4-2] The network element according to Supplementary Note 4-1, wherein the control unit performs the model registration based on at least one of model distribution and model training. [Supplementary Note 4-3] The network element according to Supplementary Note 4-1 or Supplementary Note 4-2, wherein the control unit reports a result of the model registration. [Supplementary Note 4-4] A network element according to any one of Supplementary Notes 4-1 to 4-3, wherein the control unit controls discovery of a specific model from among the registered models based on a model discovery request.

[0349] (Wireless Communication System) The configuration of a wireless communication system according to an embodiment of the present disclosure will be described below. In this wireless communication system, communication is performed using any one of the wireless communication methods according to the above embodiments of the present disclosure or a combination thereof.

[0350] 19 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment. The wireless communication system 1 (which may be simply referred to as system 1) may be a system that realizes communication using Long Term Evolution (LTE) or 5th generation mobile communication system New Radio (5G NR) specified by the Third Generation Partnership Project (3GPP).

[0351] The wireless communication system 1 may also support dual connectivity between multiple Radio Access Technologies (RATs) (Multi-RAT Dual Connectivity (MR-DC)). MR-DC may include dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (EN-DC)), dual connectivity between NR and LTE (NR-E-UTRA Dual Connectivity (NE-DC)), etc.

[0352] In EN-DC, the LTE (E-UTRA) base station (eNB) is the master node (Master Node (MN)), and the NR base station (gNB) is the secondary node (Secondary Node (SN)). In NE-DC, the NR base station (gNB) is the MN, and the LTE (E-UTRA) base station (eNB) is the SN.

[0353] The wireless communication system 1 may support dual connectivity between multiple base stations within the same RAT (for example, dual connectivity in which both the MN and SN are NR base stations (gNBs) (NR-NR Dual Connectivity (NN-DC))).

[0354] The wireless communication system 1 may include a base station 11 that forms a macrocell C1 with a relatively wide coverage, and base stations 12 (12a-12c) that are located within the macrocell C1 and form small cells C2 that are smaller than the macrocell C1. A user terminal 20 may be located within at least one of the cells. The location, number, shape, size, etc. of each cell and user terminal 20 are not limited to the embodiment shown in the figure. Hereinafter, when there is no need to distinguish between the base stations 11 and 12, they will be collectively referred to as the base station 10.

[0355] The wireless communication system 1 may utilize multi-input multi-output (MIMO). For example, one cell may be formed by one antenna / base station 10, or may be formed by multiple antennas / base stations 10. One [virtual] cell (which may be called, for example, a supercell) may be composed of multiple [virtual] cells (which may be called, for example, subcells). A supercell may correspond to a cell with a fixed physical range, and a subcell may correspond to a cell with a quasi-static / dynamically variable physical range. In this case, the wireless communication system 1 may be called a cell-free system.

[0356] The user terminal 20 may be connected to at least one of the multiple base stations 10. The user terminal 20 may utilize at least one of carrier aggregation (CA) using multiple component carriers (CCs) and dual connectivity (DC).

[0357] Each CC may be included in at least one of a first frequency band (Frequency Range 1 (FR1)) and a second frequency band (Frequency Range 2 (FR2)). The macro cell C1 may be included in FR1, and the small cell C2 may be included in FR2. For example, FR1 may be a frequency band of 6 GHz or less (sub-6 GHz), and FR2 may be a frequency band higher than 24 GHz (above-24 GHz). Note that the frequency bands and definitions of FR1 and FR2 are not limited to these, and for example, FR1 may correspond to a higher frequency band than FR2.

[0358] Furthermore, the user terminal 20 may perform communication using at least one of time division duplex (TDD) and frequency division duplex (FDD) in each CC.

[0359] The multiple base stations 10 may be connected by wire (e.g., optical fiber compliant with the Common Public Radio Interface (CPRI), an X2 / Xn interface, etc.) or wirelessly (e.g., NR communication). For example, when NR communication is used as a backhaul between the base stations 11 and 12, the base station 11 corresponding to the upper station may be called an Integrated Access Backhaul (IAB) donor, and the base station 12 corresponding to the relay station (relay) may be called an IAB node.

[0360] The base station 10 may be connected to the core network 30 directly or via another base station 10. The core network 30 may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), a Next Generation Core (NGC), and the like.

[0361] The core network 30 may include network functions (Network Functions (NF)) such as a User Plane Function (UPF), an Access and Mobility management Function (AMF), a Session Management Function (SMF), a Unified Data Management (UDM), an Application Function (AF), a Data Network (DN), a Location Management Function (LMF), and Operation, Administration and Maintenance (Management) (OAM). A single network node may provide multiple functions. Communication with an external network (e.g., the Internet) may also be performed via the DN.

[0362] The user terminal 20 may be a terminal that supports at least one of communication methods such as LTE, LTE-A, and 5G.

[0363] An Orthogonal Frequency Division Multiplexing (OFDM)-based radio access scheme may be used in the wireless communication system 1. For example, Cyclic Prefix OFDM (CP-OFDM), Discrete Fourier Transform Spread OFDM (DFT-s-OFDM), Orthogonal Frequency Division Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-FDMA), or the like may be used in at least one of the downlink (DL) and uplink (UL).

[0364] The radio access scheme may also be called a waveform. Note that in the wireless communication system 1, other radio access schemes (e.g., other single-carrier transmission schemes, other multi-carrier transmission schemes) may be used as the UL and DL radio access schemes.

[0365] In the wireless communication system 1, a downlink shared channel (Physical Downlink Shared Channel (PDSCH)) shared by each user terminal 20, a broadcast channel (Physical Broadcast Channel (PBCH)), a downlink control channel (Physical Downlink Control Channel (PDCCH)), etc. may be used as the downlink channel.

[0366] Furthermore, in the wireless communication system 1, an uplink shared channel (Physical Uplink Shared Channel (PUSCH)) shared by each user terminal 20, an uplink control channel (Physical Uplink Control Channel (PUCCH)), a random access channel (Physical Random Access Channel (PRACH)), or the like may be used as an uplink channel.

[0367] The PDSCH transmits user data, higher layer control information, a System Information Block (SIB), etc. The PUSCH may transmit user data, higher layer control information, etc. Furthermore, the PBCH may transmit a Master Information Block (MIB).

[0368] Lower layer control information may be transmitted by the PDCCH. The lower layer control information may include, for example, Downlink Control Information (DCI) including scheduling information for at least one of the PDSCH and the PUSCH.

[0369] Note that the DCI for scheduling the PDSCH may be referred to as a DL assignment, a DL DCI, etc., and the DCI for scheduling the PUSCH may be referred to as a UL grant, a UL DCI, etc. Note that the PDSCH may be replaced with DL data, and the PUSCH may be replaced with UL data.

[0370] A control resource set (CORESET) and a search space may be used to detect the PDCCH. The CORESET corresponds to resources for searching for DCI. The search space corresponds to a search region and a search method for PDCCH candidates. One CORESET may be associated with one or more search spaces. The UE may monitor the CORESET associated with a certain search space based on the search space configuration.

[0371] One search space may correspond to PDCCH candidates corresponding to one or more aggregation levels. One or more search spaces may be referred to as a search space set. Note that the terms "search space," "search space set," "search space configuration," "search space set configuration," "CORESET," "CORESET configuration," and the like in the present disclosure may be read interchangeably.

[0372] The PUCCH may transmit uplink control information (UCI) including at least one of channel state information (CSI), delivery confirmation information (which may be called, for example, Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), ACK / NACK, etc.), and scheduling request (SR). The PRACH may transmit a random access preamble for establishing a connection with a cell.

[0373] In the present disclosure, downlink, uplink, etc. may be expressed without adding "link." Also, various channels may be expressed without adding "Physical" to the beginning.

[0374] In the wireless communication system 1, a synchronization signal (SS), a downlink reference signal (DL-RS), etc. may be transmitted. In the wireless communication system 1, as the DL-RS, a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS), a demodulation reference signal (DMRS), a positioning reference signal (PRS), a phase tracking reference signal (PTRS), etc. may be transmitted.

[0375] The synchronization signal may be, for example, at least one of a Primary Synchronization Signal (PSS) and a Secondary Synchronization Signal (SSS). A signal block including an SS (PSS, SSS) and a PBCH (and a DMRS for the PBCH) may be referred to as an SS / PBCH block, an SS Block (SSB), or the like. Note that the SS, SSB, and the like may also be referred to as a reference signal.

[0376] Furthermore, in the wireless communication system 1, a sounding reference signal (SRS), a demodulation reference signal (DMRS), or the like may be transmitted as an uplink reference signal (UL-RS). Note that the DMRS may also be called a user equipment-specific reference signal (UE-specific reference signal).

[0377] (Base Station) Fig. 20 is a diagram showing an example of the configuration of a base station according to an embodiment. The base station 10 includes a control unit 110, a transceiver unit 120, a transceiver antenna 130, and a transmission line interface 140. Note that the base station may include one or more of each of the control unit 110, the transceiver unit 120, the transceiver antenna 130, and the transmission line interface 140.

[0378] In this example, the functional blocks of the characteristic parts of the present embodiment are mainly shown, and it may be assumed that the base station 10 also has other functional blocks necessary for wireless communication. Some of the processing of each unit described below may be omitted.

[0379] The control unit 110 performs overall control of the base station 10. The control unit 110 can be configured from a controller, a control circuit, and the like that are described based on common understanding in the technical field to which the present disclosure relates.

[0380] The control unit 110 may control signal generation, scheduling (e.g., resource allocation, mapping), etc. The control unit 110 may control transmission and reception using the transceiver unit 120, the transceiver antenna 130, and the transmission path interface 140, measurement, etc. The control unit 110 may generate data, control information, sequences, etc. to be transmitted as signals, and transfer them to the transceiver unit 120. The control unit 110 may perform call processing (setting up, releasing, etc.) of communication channels, status management of the base station 10, management of radio resources, etc.

[0381] The transceiver unit 120 may include a baseband unit 121, a radio frequency (RF) unit 122, and a measurement unit 123. The baseband unit 121 may include a transmission processing unit 1211 and a reception processing unit 1212. The transceiver unit 120 may be configured with a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transceiver circuit, etc., which are described based on common understanding in the technical field related to the present disclosure.

[0382] The transmitting / receiving unit 120 may be configured as an integrated transmitting / receiving unit, or may be configured from a transmitting unit and a receiving unit. The transmitting unit may be configured from a transmission processing unit 1211 and an RF unit 122. The receiving unit may be configured from a reception processing unit 1212, the RF unit 122, and a measurement unit 123.

[0383] The transmitting and receiving antenna 130 can be configured from an antenna described based on common understanding in the technical field to which the present disclosure relates, such as an array antenna.

[0384] The transceiver 120 may transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transceiver 120 may receive the above-mentioned uplink channel, uplink reference signal, etc.

[0385] The transceiver 120 may form at least one of the transmit beam and the receive beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.

[0386] The transmitter / receiver unit 120 (transmission processing unit 1211) may perform Packet Data Convergence Protocol (PDCP) layer processing, Radio Link Control (RLC) layer processing (e.g., RLC retransmission control), Medium Access Control (MAC) layer processing (e.g., HARQ retransmission control), etc. on data, control information, etc. obtained from the control unit 110, and generate a bit string to be transmitted.

[0387] The transmitter / receiver unit 120 (transmission processing unit 1211) may perform transmission processing such as channel coding (which may include error correction coding), modulation, mapping, filtering, Discrete Fourier Transform (DFT) processing (if necessary), Inverse Fast Fourier Transform (IFFT) processing, precoding, and digital-to-analog conversion on the bit string to be transmitted, and output a baseband signal.

[0388] The transceiver unit 120 (RF unit 122) may perform modulation, filtering, amplification, etc. on the baseband signal to a radio frequency band, and transmit the radio frequency band signal via the transceiver antenna 130.

[0389] On the other hand, the transceiver unit 120 (RF unit 122) may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency band signal received by the transceiver antenna 130.

[0390] The transceiver 120 (reception processing unit 1212) may apply reception processing such as analog-to-digital conversion, Fast Fourier Transform (FFT) processing, Inverse Discrete Fourier Transform (IDFT) processing (if necessary), filtering, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal, thereby acquiring user data, etc.

[0391] The transceiver 120 (measurement unit 123) may perform measurements on the received signal. For example, the measurement unit 123 may perform Radio Resource Management (RRM) measurements, Channel State Information (CSI) measurements, etc. based on the received signal. The measurement unit 123 may measure received power (e.g., Reference Signal Received Power (RSRP)), received quality (e.g., Reference Signal Received Quality (RSRQ), Signal to Interference plus Noise Ratio (SINR), Signal to Noise Ratio (SNR)), signal strength (e.g., Received Signal Strength Indicator (RSSI)), propagation path information (e.g., CSI), etc. The measurement results may be output to the control unit 110.

[0392] The transmission path interface 140 may transmit and receive signals (backhaul signaling) between devices included in the core network 30 (e.g., network nodes that provide NF), other base stations 10, etc., and may acquire and transmit user data (user plane data), control plane data, etc. for the user terminal 20.

[0393] The transmitting section and receiving section of the base station 10 in the present disclosure may be configured by at least one of the transmitting / receiving section 120, the transmitting / receiving antenna 130, and the transmission path interface 140.

[0394] The base station 10 may be separated into three elements: a radio unit (RU), a distributed unit (DU), and a central unit (CU). For example, the RU may implement RF processing (digital beamforming, digital-to-analog conversion, analog beamforming, etc.) and lower-level functions of the physical layer (precoding, IFFT, FFT, etc.). The DU may implement higher-level functions of the physical layer (coding to resource element mapping, etc.), MAC layer functions, and RLC layer functions. The CU may implement the functions of the PDCP layer, Service Data Adaptation Protocol (SDAP) layer, and RRC layer.

[0395] In the present disclosure, the base station 10 may include a single device that realizes all of the functions of the RU, DU, and CU, or may include multiple devices that each realize some of the functions of the RU, DU, and CU and are connected to each other. In the present disclosure, the base station 10 may be interchangeably read as RU / DU / CU.

[0396] The transceiver 120 may receive a request for model verification, and the controller 110 may calculate model performance based on the request (first and second embodiments).

[0397] The control unit 110 may report capability information related to the model verification (first embodiment).

[0398] The control unit 110 may calculate the model performance based on at least one of a dataset for model validation and a model for model validation (first embodiment).

[0399] The control unit 110 may report the calculated model performance (second embodiment).

[0400] The transceiver 120 may receive a request for model training, and the controller 110 may execute model training based on the request (third embodiment).

[0401] The control unit 110 may report performance information regarding the model training (third embodiment).

[0402] The control unit 110 may perform the model training based on at least one of a dataset for model training and a model for model training (third embodiment).

[0403] The control unit 110 may report on the trained model (third embodiment).

[0404] The transceiver 120 may receive a request for model transfer, and the controller 110 may control the start and end of model transfer based on the request (fourth and fifth embodiments).

[0405] The transceiver unit 120 may receive a request for information about a transferable model (fourth embodiment).

[0406] The control unit 110 may control the start of the model transfer based on at least one of information indicating which model is to be transferred and information indicating how the model is to be transferred (fifth embodiment).

[0407] The control unit 110 may control the end of the model transfer based on the reception of an instruction to end the model transfer (fifth embodiment).

[0408] The control unit 110 may control the end of the model transfer based on the transmission of an instruction to end the model transfer (fifth embodiment).

[0409] The transmitting / receiving unit 120 may receive a request for model registration, and the control unit 110 may execute model registration based on the request (sixth embodiment).

[0410] The control unit 110 may perform the model registration based on at least one of model distribution and model training (sixth embodiment).

[0411] The control unit 110 may report the result of the model registration (sixth embodiment).

[0412] The control unit 110 may control the discovery of a specific model from among the registered models based on a model discovery request (seventh embodiment).

[0413] (User Terminal) Fig. 21 is a diagram showing an example of the configuration of a user terminal according to one embodiment. The user terminal 20 includes a control unit 210, a transceiver unit 220, and a transceiver antenna 230. Note that the user terminal 20 may include one or more of each of the control unit 210, the transceiver unit 220, and the transceiver antenna 230.

[0414] In this example, the functional blocks of the characteristic parts of the present embodiment are mainly shown, and it may be assumed that the user terminal 20 also has other functional blocks necessary for wireless communication. Some of the processing of each unit described below may be omitted.

[0415] The control unit 210 performs overall control of the user terminal 20. The control unit 210 can be configured from a controller, a control circuit, etc., which are described based on common understanding in the technical field to which the present disclosure relates.

[0416] The control unit 210 may control signal generation, mapping, etc. The control unit 210 may control transmission and reception, measurement, etc. using the transceiver unit 220 and the transceiver antenna 230. The control unit 210 may generate data, control information, sequences, etc. to be transmitted as signals and transfer them to the transceiver unit 220.

[0417] The transceiver unit 220 may include a baseband unit 221, an RF unit 222, and a measurement unit 223. The baseband unit 221 may include a transmission processing unit 2211 and a reception processing unit 2212. The transceiver unit 220 may be configured with a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transceiver circuit, etc., which are described based on common understanding in the technical field related to the present disclosure.

[0418] The transmitting / receiving unit 220 may be configured as an integrated transmitting / receiving unit, or may be composed of a transmitting unit and a receiving unit. The transmitting unit may be composed of a transmission processing unit 2211 and an RF unit 222. The receiving unit may be composed of a reception processing unit 2212, an RF unit 222, and a measurement unit 223.

[0419] The transmitting / receiving antenna 230 can be configured from an antenna described based on common understanding in the technical field to which the present disclosure relates, such as an array antenna.

[0420] The transceiver 220 may receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transceiver 220 may transmit the above-mentioned uplink channel, uplink reference signal, etc.

[0421] The transceiver unit 220 may form at least one of the transmit beam and the receive beam using digital beamforming (e.g., precoding), analog beamforming (e.g., phase rotation), or the like.

[0422] The transceiver unit 220 (transmission processing unit 2211) may perform PDCP layer processing, RLC layer processing (e.g., RLC retransmission control), MAC layer processing (e.g., HARQ retransmission control), etc. on data, control information, etc. obtained from the control unit 210, and generate a bit string to be transmitted.

[0423] The transmitter / receiver unit 220 (transmission processing unit 2211) may perform transmission processing such as channel coding (which may include error correction coding), modulation, mapping, filtering, DFT processing (if necessary), IFFT processing, precoding, and digital-to-analog conversion on the bit string to be transmitted, and output a baseband signal.

[0424] Whether or not to apply DFT processing may be based on the setting of transform precoding. When transform precoding is enabled for a certain channel (e.g., PUSCH), the transceiver unit 220 (transmission processing unit 2211) may perform DFT processing as the transmission processing to transmit the channel using a DFT-s-OFDM waveform, and if not, it may not be necessary to perform DFT processing as the transmission processing.

[0425] The transceiver unit 220 (RF unit 222) may perform modulation, filtering, amplification, etc. on the baseband signal to a radio frequency band, and transmit the radio frequency band signal via the transceiver antenna 230.

[0426] On the other hand, the transceiver unit 220 (RF unit 222) may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency band signal received by the transceiver antenna 230.

[0427] The transceiver unit 220 (reception processing unit 2212) may apply reception processing such as analog-to-digital conversion, FFT processing, IDFT processing (if necessary), filtering, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal, and acquire user data, etc.

[0428] The transceiver 220 (measurement unit 223) may perform measurements on the received signal. For example, the measurement unit 223 may perform RRM measurements, CSI measurements, etc. based on the received signal. The measurement unit 223 may measure received power (e.g., RSRP), received quality (e.g., RSRQ, SINR, SNR), signal strength (e.g., RSSI), propagation path information (e.g., CSI), etc. The measurement results may be output to the control unit 210.

[0429] The measurement unit 223 may derive channel measurements for CSI calculation based on the channel measurement resources. The channel measurement resources may be, for example, non-zero power (NZP) CSI-RS resources. The measurement unit 223 may also derive interference measurements for CSI calculation based on the interference measurement resources. The interference measurement resources may be at least one of an NZP CSI-RS resource for interference measurement, a CSI-Interference Measurement (IM) resource, etc. Note that CSI-IM may be referred to as CSI-Interference Management (IM) or may be interchangeably read as Zero Power (ZP) CSI-RS. Note that in the present disclosure, CSI-RS, NZP CSI-RS, ZP CSI-RS, CSI-IM, CSI-SSB, etc. may be interchangeably read as interchangeable.

[0430] The transmitting unit and receiving unit of the user terminal 20 in the present disclosure may be configured by at least one of the transmitting / receiving unit 220 and the transmitting / receiving antenna 230.

[0431] The transceiver 220 may receive a request for model verification, and the controller 210 may calculate model performance based on the request (first and second embodiments).

[0432] The control unit 210 may report capability information related to the model verification (first embodiment).

[0433] The control unit 210 may calculate the model performance based on at least one of a dataset for model validation and a model for model validation (first embodiment).

[0434] The control unit 210 may report the calculated model performance (second embodiment).

[0435] The transceiver 220 may receive a request for model training, and the controller 210 may execute model training based on the request (third embodiment).

[0436] The control unit 210 may report performance information regarding the model training (third embodiment).

[0437] The control unit 210 may perform the model training based on at least one of a dataset for model training and a model for model training (third embodiment).

[0438] The control unit 210 may also report on the trained model (third embodiment).

[0439] The transceiver unit 220 may receive a request for model transfer, and the control unit 210 may control the start and end of model transfer based on the request (fourth and fifth embodiments).

[0440] The transceiver unit 220 may receive a request for information about a transferable model (fourth embodiment).

[0441] The control unit 210 may control the start of the model transfer based on at least one of information indicating which model is to be transferred and information indicating how the model is to be transferred (fifth embodiment).

[0442] The control unit 210 may control the end of the model transfer based on the reception of an instruction to end the model transfer (fifth embodiment).

[0443] The control unit 210 may control the end of the model transfer based on the transmission of an instruction to end the model transfer (fifth embodiment).

[0444] The transmitting / receiving unit 220 may receive a request for model registration, and the control unit 210 may execute model registration based on the request (sixth embodiment).

[0445] The control unit 210 may perform the model registration based on at least one of model distribution and model training (sixth embodiment).

[0446] The control unit 210 may report the result of the model registration (sixth embodiment).

[0447] The control unit 210 may control the discovery of a specific model from among the registered models based on a model discovery request (seventh embodiment).

[0448] (Hardware Configuration) Note that the block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wires, wirelessly, etc.) and these multiple devices. The functional block may be realized by combining software with the single device or the multiple devices.

[0449] Here, the functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, deeming, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission may be called a transmitting unit, transmitter, etc. As described above, the implementation method of each is not particularly limited.

[0450] For example, a base station, a user terminal, etc. according to an embodiment of the present disclosure may function as a computer that performs processing of the wireless communication method of the present disclosure. Fig. 22 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment. The above-described base station 10 and user terminal 20 may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0451] In the present disclosure, the terms apparatus, circuit, device, section, unit, etc. may be used interchangeably. The hardware configurations of the base station 10 and the user terminal 20 may be configured to include one or more of the devices shown in the drawings, or may be configured to exclude some of the devices.

[0452] For example, although only one processor 1001 is shown, there may be multiple processors. Furthermore, processing may be performed by one processor, or processing may be performed by two or more processors simultaneously, serially, or in other ways. Furthermore, processor 1001 may be implemented by one or more chips.

[0453] Each function in the base station 10 and the user terminal 20 is realized, for example, by loading specified software (programs) onto hardware such as a processor 1001 and a memory 1002, causing the processor 1001 to perform calculations, control communication via the communication device 1004, and control at least one of reading and writing data in the memory 1002 and the storage 1003.

[0454] The processor 1001, for example, runs an operating system to control the entire computer. The processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, at least a part of the above-mentioned control unit 110 (210), transceiver unit 120 (220), etc. may be realized by the processor 1001.

[0455] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the control unit 110 (210) may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and the other functional blocks may be implemented in a similar manner.

[0456] The memory 1002 is a computer-readable recording medium and may be configured by at least one of, for example, Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically EEPROM (EEPROM), Random Access Memory (RAM), or other suitable storage medium. The memory 1002 may also be referred to as a register, cache, main memory, etc. The memory 1002 may store executable programs (program codes), software modules, etc. for implementing a wireless communication method according to an embodiment of the present disclosure.

[0457] Storage 1003 is a computer-readable recording medium and may be composed of at least one of, for example, a flexible disk, a floppy disk, a magneto-optical disk (e.g., a compact disc (e.g., a Compact Disc ROM (CD-ROM)), a digital versatile disc, a Blu-ray disc), a removable disk, a hard disk drive, a smart card, a flash memory device (e.g., a card, a stick, a key drive), a magnetic stripe, a database, a server, or other suitable storage medium. Storage 1003 may also be referred to as an auxiliary storage device.

[0458] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the above-mentioned transmission / reception unit 120 (220), transmission / reception antenna 130 (230), etc. may be realized by the communication device 1004. The transmission / reception unit 120 (220) may be implemented as a transmission unit 120a (220a) and a reception unit 120b (220b) that are physically or logically separated.

[0459] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, a light emitting diode (LED) lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0460] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0461] Furthermore, the base station 10 and the user terminal 20 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized using this hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0462] In addition, the devices included in the core network 30 (for example, network nodes that provide NF) may also be realized by the above-mentioned functional block / hardware configuration.

[0463] (Modifications) Note that terms described in the present disclosure and terms necessary for understanding the present disclosure may be replaced with terms having the same or similar meanings. For example, a channel, a symbol, and a signal (signal or signaling) may be interchangeable. A signal may also be a message. A reference signal may be abbreviated as RS, and may also be called a pilot, pilot signal, etc. depending on the applicable standard. A component carrier (CC) may also be called a cell, frequency carrier, carrier frequency, etc.

[0464] A radio frame may be composed of one or more periods (frames) in the time domain. Each of the one or more periods (frames) constituting a radio frame may be called a subframe. Furthermore, a subframe may be composed of one or more slots in the time domain. A subframe may have a fixed time length (e.g., 1 ms) that is independent of numerology.

[0465] Here, the numerology may be a communication parameter applied to at least one of transmission and reception of a signal or channel, and may indicate at least one of, for example, Subcarrier Spacing (SCS), bandwidth, symbol length, cyclic prefix length, Transmission Time Interval (TTI), number of symbols per TTI, radio frame structure, specific filtering performed by a transceiver in the frequency domain, and specific windowing performed by a transceiver in the time domain.

[0466] A slot may be composed of one or more symbols (such as an Orthogonal Frequency Division Multiplexing (OFDM) symbol or a Single Carrier Frequency Division Multiple Access (SC-FDMA) symbol) in the time domain. A slot may also be a time unit based on numerology.

[0467] A slot may include multiple minislots. Each minislot may consist of one or multiple symbols in the time domain. A minislot may also be called a subslot. A minislot may consist of fewer symbols than a slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a minislot may be called PDSCH (PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using a minislot may be called PDSCH (PUSCH) mapping type B.

[0468] A radio frame, a subframe, a slot, a minislot, and a symbol all represent time units for transmitting signals. The radio frame, the subframe, the slot, the minislot, and the symbol may be referred to by other names corresponding to the radio frame, the subframe, the slot, the minislot, and the symbol. Note that the time units such as a frame, a subframe, a slot, a minislot, and a symbol in the present disclosure may be interchangeable.

[0469] For example, one subframe may be referred to as a TTI, or multiple consecutive subframes may be referred to as a TTI, or one slot or one minislot may be referred to as a TTI. That is, at least one of the subframe and the TTI may be a subframe (1 ms) in existing LTE, a period shorter than 1 ms (for example, 1-13 symbols), or a period longer than 1 ms. Note that the unit representing the TTI may be called a slot, minislot, etc. instead of a subframe.

[0470] Here, TTI refers to, for example, the smallest time unit for scheduling in wireless communication. For example, in an LTE system, a base station performs scheduling to allocate radio resources (such as frequency bandwidth and transmission power that can be used by each user terminal) to each user terminal in TTI units. Note that the definition of TTI is not limited to this.

[0471] The TTI may be a transmission time unit for a channel-encoded data packet (transport block), a code block, a code word, etc., or may be a processing unit for scheduling, link adaptation, etc. When a TTI is given, the time interval (e.g., the number of symbols) to which a transport block, a code block, a code word, etc. is actually mapped may be shorter than the TTI.

[0472] When one slot or one minislot is called a TTI, one or more TTIs (i.e., one or more slots or one or more minislots) may be the minimum time unit for scheduling. Also, the number of slots (minislots) constituting the minimum time unit for scheduling may be controlled.

[0473] A TTI having a time length of 1 ms may be called a regular TTI (TTI in 3GPP Rel. 8-12), normal TTI, long TTI, regular subframe, normal subframe, long subframe, slot, etc. A TTI shorter than a regular TTI may be called a shortened TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, minislot, subslot, slot, etc.

[0474] In addition, a long TTI (e.g., a normal TTI, a subframe, etc.) may be interpreted as a TTI having a time length of more than 1 ms, and a short TTI (e.g., a shortened TTI, etc.) may be interpreted as a TTI having a TTI length shorter than the TTI length of a long TTI and greater than or equal to 1 ms.

[0475] A resource block (RB) is a resource allocation unit in the time domain and the frequency domain, and may include one or more consecutive subcarriers in the frequency domain. The number of subcarriers included in an RB may be the same regardless of numerology, for example, 12. The number of subcarriers included in an RB may be determined based on numerology.

[0476] In addition, an RB may include one or more symbols in the time domain and may have a length of one slot, one minislot, one subframe, or one TTI, each of which may be composed of one or more resource blocks.

[0477] In addition, one or more RBs may be referred to as a physical resource block (PRB), a sub-carrier group (SCG), a resource element group (REG), a PRB pair, an RB pair, etc.

[0478] Furthermore, a resource block may be composed of one or more resource elements (REs). For example, one RE may be a radio resource region of one subcarrier and one symbol.

[0479] A Bandwidth Part (BWP), which may also be referred to as a partial bandwidth, may represent a subset of contiguous common resource blocks (RBs) for a given numerology on a given carrier, where the common RBs may be identified by their index relative to a Common Reference Point of the carrier. PRBs may be defined in a BWP and numbered within the BWP.

[0480] The BWP may include a UL BWP (BWP for UL) and a DL BWP (BWP for DL). One or more BWPs may be configured for a UE within one carrier.

[0481] At least one of the configured BWPs may be active, and the UE may not expect to transmit or receive a given signal / channel outside the active BWP. Note that the terms "cell," "carrier," etc. in this disclosure may be read as "BWP."

[0482] The above-described structures of radio frames, subframes, slots, minislots, symbols, etc. are merely examples. For example, the number of subframes included in a radio frame, the number of slots per subframe or radio frame, the number of minislots included in a slot, the number of symbols and RBs included in a slot or minislot, the number of subcarriers included in an RB, the number of symbols in a TTI, the symbol length, the cyclic prefix (CP) length, etc. may be changed in various ways.

[0483] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by a predetermined index.

[0484] The names used for parameters and the like in this disclosure are not intended to be limiting in any way. Furthermore, the mathematical expressions and the like using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0485] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0486] Furthermore, information, signals, etc. may be output from a higher layer to a lower layer and / or from a lower layer to a higher layer. Information, signals, etc. may be input / output via multiple network nodes.

[0487] Input and output information, signals, etc. may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information, signals, etc. may be overwritten, updated, or added. Output information, signals, etc. may be deleted. Input information, signals, etc. may be transmitted to another device.

[0488] With respect to any information (e.g., variables, constants, parameters) described in the present disclosure, even if not specifically stated in the above embodiments, any first device (e.g., UE / base station) may notify any second device (e.g., base station / UE) of information indicating / specifying (or relating to) the value of the any information.

[0489] The notification of information is not limited to the aspects / embodiments described in the present disclosure, and may be performed using other methods. For example, the notification of information in the present disclosure may be performed by physical layer signaling (e.g., Downlink Control Information (DCI) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB) and System Information Block (SIB)), Medium Access Control (MAC) signaling), other signals, or a combination thereof.

[0490] Note that the physical layer signaling may be referred to as Layer 1 / Layer 2 (L1 / L2) control information (L1 / L2 control signal), L1 control information (L1 control signal), etc. Furthermore, the RRC signaling may be referred to as an RRC message, such as an RRC Connection Setup message or an RRC Connection Reconfiguration message. Furthermore, the MAC signaling may be notified using, for example, a MAC Control Element (CE).

[0491] Furthermore, notification of specified information (e.g., notification that "it is X") is not limited to explicit notification, but may be made implicitly (e.g., by not notifying the specified information or by notifying other information).

[0492] The determination may be made by a value represented by one bit (0 or 1), by a Boolean value represented by true or false, or by a comparison of numerical values ​​(e.g., comparison with a predetermined value).

[0493] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0494] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), these wired and / or wireless technologies are included within the definition of transmission media.

[0495] As used in this disclosure, the terms "system" and "network" may be used interchangeably. A "network" may refer to devices included in the network (e.g., base stations).

[0496] In this disclosure, terms such as "precoding," "precoder," "weight (precoding weight)," "Quasi-Co-Location (QCL)," "Transmission Configuration Indication state (TCI state)," "spatial relation," "spatial domain filter," "transmit power," "phase rotation," "antenna port," "layer," "number of layers," "rank," "resource," "resource set," "beam," "beam width," "beam angle," "antenna," "antenna element," "panel," "UE panel," "transmitting entity," "receiving entity," etc. may be used interchangeably.

[0497] In the present disclosure, the term "antenna port" may be interchangeably read as an antenna port for any signal / channel (e.g., a demodulation reference signal (DMRS) port). In the present disclosure, the term "resource" may be interchangeably read as a resource for any signal / channel (e.g., a reference signal resource, an SRS resource, etc.). The resource may include time / frequency / code / space / power resources. Furthermore, the spatial domain transmission filter may include at least one of a spatial domain transmission filter and a spatial domain reception filter.

[0498] The group may include, for example, at least one of a spatial relationship group, a Code Division Multiplexing (CDM) group, a Reference Signal (RS) group, a Control Resource Set (CORESET) group, a PUCCH group, an antenna port group (e.g., a DMRS port group), a layer group, a resource group, a beam group, an antenna group, a panel group, and the like.

[0499] In addition, in the present disclosure, beam, SRS Resource Indicator (SRI), CORESET, CORESET pool, PDSCH, PUSCH, codeword (CW), transport block (TB), RS, etc. may be read as interchangeable terms.

[0500] In addition, in the present disclosure, the terms TCI state, downlink TCI state (DL TCI state), uplink TCI state (UL TCI state), unified TCI state, common TCI state, joint TCI state, etc. may be read interchangeably.

[0501] Furthermore, in the present disclosure, terms such as "QCL," "QCL assumption," "QCL relationship," "QCL type information," "QCL property / properties," "specific QCL type (e.g., Type A, Type D) property," and "specific QCL type (e.g., Type A, Type D)" may be interchangeable.

[0502] In the present disclosure, terms such as index, identifier (ID), indicator, indication, and resource ID may be interchangeable. In the present disclosure, terms such as sequence, list, set, group, cluster, and subset may be interchangeable.

[0503] Furthermore, the spatial relationship information identifier (ID) (TCI state ID) and the spatial relationship information (TCI state) may be interchangeable. The "spatial relationship information (TCI state)" may be interchangeable with "set of spatial relationship information (TCI state)", "one or more pieces of spatial relationship information", etc. The TCI state and the TCI may be interchangeable. The spatial relationship information and the spatial relationship may be interchangeable.

[0504] In the present disclosure, terms such as "base station (BS)," "radio base station," "fixed station," "NodeB," "eNB (eNodeB)," "gNB (gNodeB)," "access point," "transmission point (TP)," "reception point (RP)," "transmission / reception point (TRP)," "panel," "cell," "sector," "cell group," "carrier," "component carrier," etc. may be used interchangeably. Base stations may also be referred to by terms such as macrocell, small cell, femtocell, picocell, etc.

[0505] A base station can accommodate one or more (e.g., three) cells. When a base station accommodates multiple cells, the overall coverage area of ​​the base station can be partitioned into multiple smaller areas, and each smaller area can be provided with communication service by a base station subsystem (e.g., a small indoor base station (Remote Radio Head (RRH))). The terms "cell" or "sector" refer to part or all of the coverage area of ​​a base station and / or base station subsystem that provides communication service within that coverage.

[0506] In the present disclosure, a base station transmitting information to a terminal may be interpreted as the base station instructing the terminal to control / operate based on the information.

[0507] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," etc. may be used interchangeably.

[0508] A mobile station may also be referred to as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

[0509] At least one of the base station and the mobile station may be called a transmitting device, a receiving device, a wireless communication device, etc. Note that at least one of the base station and the mobile station may be a device mounted on a moving object, the moving object itself, etc.

[0510] The mobile body is a movable object that can move at any speed and naturally includes cases where the mobile body is stationary. Examples of the mobile body include, but are not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcars, rickshaws, ships and other watercraft, airplanes, rockets, satellites, drones, multicopters, quadcopters, balloons, and objects mounted thereon. The mobile body may also be a mobile body that moves autonomously based on an operation command.

[0511] The mobile object may be a vehicle (e.g., a car, an airplane, etc.), an unmanned mobile object (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned). Note that at least one of the base station and the mobile station may also include devices that do not necessarily move during communication operations. For example, at least one of the base station and the mobile station may be an Internet of Things (IoT) device such as a sensor.

[0512] 23 is a diagram showing an example of a vehicle according to an embodiment. The vehicle 40 includes a drive unit 41, a steering unit 42, an accelerator pedal 43, a brake pedal 44, a shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, an electronic control unit 49, various sensors (including a current sensor 50, an RPM sensor 51, an air pressure sensor 52, a vehicle speed sensor 53, an acceleration sensor 54, an accelerator pedal sensor 55, a brake pedal sensor 56, a shift lever sensor 57, and an object detection sensor 58), an information service unit 59, and a communication module 60.

[0513] The drive unit 41 is configured with at least one of an engine, a motor, and a hybrid of an engine and a motor, for example. The steering unit 42 includes at least a steering wheel (also called a handle) and is configured to steer at least one of the front wheels 46 and the rear wheels 47 based on the operation of the steering wheel operated by a user.

[0514] The electronic control unit 49 is composed of a microprocessor 61, memory (ROM, RAM) 62, and a communication port (for example, an input / output (IO) port) 63. Signals are input to the electronic control unit 49 from various sensors 50-58 provided in the vehicle. The electronic control unit 49 may also be called an Electronic Control Unit (ECU).

[0515] The signals from the various sensors 50-58 include a current signal from a current sensor 50 that senses the current of the motor, a rotation speed signal of the front wheels 46 / rear wheels 47 obtained by a rotation speed sensor 51, an air pressure signal of the front wheels 46 / rear wheels 47 obtained by an air pressure sensor 52, a vehicle speed signal obtained by a vehicle speed sensor 53, an acceleration signal obtained by an acceleration sensor 54, a depression amount signal of the accelerator pedal 43 obtained by an accelerator pedal sensor 55, a depression amount signal of the brake pedal 44 obtained by a brake pedal sensor 56, an operation signal of the shift lever 45 obtained by a shift lever sensor 57, and a detection signal for detecting obstacles, vehicles, pedestrians, etc. obtained by an object detection sensor 58.

[0516] The information service unit 59 is composed of various devices, such as a car navigation system, an audio system, speakers, a display, a television, and a radio, for providing (outputting) various information such as driving information, traffic information, and entertainment information, and one or more ECUs for controlling these devices. The information service unit 59 uses information acquired from external devices via the communication module 60 or the like to provide various information / services (e.g., multimedia information / multimedia services) to the occupants of the vehicle 40.

[0517] The information service unit 59 may include input devices (e.g., keyboards, mice, microphones, switches, buttons, sensors, touch panels, etc.) that accept input from the outside, and may also include output devices (e.g., displays, speakers, LED lamps, touch panels, etc.) that output to the outside.

[0518] The driving assistance system unit 64 includes various devices for providing functions to prevent accidents and reduce the driver's driving burden, such as millimeter-wave radar, Light Detection and Ranging (LiDAR), cameras, positioning locators (e.g., Global Navigation Satellite System (GNSS)), map information (e.g., High Definition (HD) maps, Autonomous Vehicle (AV) maps), gyro systems (e.g., Inertial Measurement Units (IMUs), Inertial Navigation Systems (INSs)), artificial intelligence (AI) chips, and AI processors, as well as one or more ECUs that control these devices. The driving assistance system unit 64 also transmits and receives various information via the communication module 60 to realize driving assistance functions or autonomous driving functions.

[0519] The communication module 60 can communicate with the microprocessor 61 and components of the vehicle 40 via the communication port 63. For example, the communication module 60 transmits and receives data (information) via the communication port 63 to and from the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, the microprocessor 61 and memory (ROM, RAM) 62 in the electronic control unit 49, and the various sensors 50-58, which are provided in the vehicle 40.

[0520] The communication module 60 is a communication device that can be controlled by the microprocessor 61 of the electronic control unit 49 and can communicate with an external device. For example, it transmits and receives various information to and from the external device via wireless communication. The communication module 60 may be located either inside or outside the electronic control unit 49. The external device may be, for example, the base station 10 or the user terminal 20 described above. Furthermore, the communication module 60 may be, for example, at least one of the base station 10 and the user terminal 20 described above (or may function as at least one of the base station 10 and the user terminal 20).

[0521] The communication module 60 may transmit at least one of signals from the above-mentioned various sensors 50-58 input to the electronic control unit 49, information obtained based on the signals, and information based on input from the outside (user) obtained via the information service unit 59 to an external device via wireless communication. The electronic control unit 49, the various sensors 50-58, the information service unit 59, etc. may be referred to as input units that accept input. For example, the PUSCH transmitted by the communication module 60 may include information based on the above-mentioned input.

[0522] The communication module 60 receives various information (traffic information, traffic signal information, vehicle distance information, etc.) transmitted from an external device and displays it on an information service unit 59 provided in the vehicle. The information service unit 59 may also be called an output unit that outputs information (for example, outputs information to a device such as a display or speaker based on the PDSCH received by the communication module 60 (or data / information decoded from the PDSCH)).

[0523] Furthermore, the communication module 60 stores various information received from external devices in a memory 62 that can be used by the microprocessor 61. Based on the information stored in the memory 62, the microprocessor 61 may control the drive unit 41, steering unit 42, accelerator pedal 43, brake pedal 44, shift lever 45, left and right front wheels 46, left and right rear wheels 47, axles 48, various sensors 50-58, and the like provided in the vehicle 40.

[0524] Furthermore, a base station in the present disclosure may be read as a user terminal. For example, the aspects / embodiments of the present disclosure may be applied to a configuration in which communication between a base station and a user terminal is replaced with communication between multiple user terminals (which may be called, for example, Device-to-Device (D2D) or Vehicle-to-Everything (V2X)). In this case, the user terminal 20 may be configured to have the functions of the base station 10 described above. Furthermore, terms such as "uplink" and "downlink" may be read as terms corresponding to terminal-to-terminal communication (for example, "sidelink"). For example, terms such as an uplink channel and a downlink channel may be read as a sidelink channel.

[0525] Similarly, the user terminal in the present disclosure may be read as a base station, in which case the base station 10 may be configured to have the functions of the user terminal 20 described above.

[0526] In the present disclosure, an operation described as being performed by a base station may be performed by its upper node in some cases. It is apparent that in a network including one or more network nodes having a base station, various operations performed for communication with a terminal may be performed by the base station, one or more network nodes other than the base station (such as, but not limited to, a Mobility Management Entity (MME), a Serving-Gateway (S-GW), etc.), or a combination thereof.

[0527] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Furthermore, the order of the processing procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0528] Each aspect / embodiment described in the present disclosure may be a technology other than Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 6th generation mobile communication system (6G), xth generation mobile communication system (xG (x is, for example, an integer or decimal number)), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.17 (WiMAX (registered trademark)), IEEE 802.19 (WiMAX (registered trademark)), IEEE 802.20 (WiMAX (registered trademark)), IEEE 802.21 (Wi-Fi (registered trademark)), IEEE 802.22 (WiMAX (registered trademark)), IEEE 802.23 (WiMAX (registered trademark)), IEEE 802.24 (WiMAX (registered trademark)), IEEE 802.25 (WiMAX (registered trademark)), IEEE 802.26 (WiMAX (registered trademark)), IEEE 802.27 (WiMAX (registered trademark)), IEEE 802.28 (WiMAX (registered trademark)), IEEE 802.29 (WiMAX (registered trademark)), IEEE 802.30 (WiMAX (registered trademark)), IEEE 802.31 (Wi-Fi (registered trademark)), IEEE 802.32 (WiMAX (registered trademark)), IEEE 802.33 (WiMAX (registered trademark)), IEEE 802. The present invention may be applied to systems that use IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), or other suitable wireless communication methods, or to next-generation systems that are expanded, modified, created, or defined based on these. Furthermore, the present invention may be applied to a combination of multiple systems (e.g., a combination of LTE or LTE-A and 5G).

[0529] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0530] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0531] The term "determining" as used in this disclosure may encompass a wide variety of actions. For example, "determining" may be considered to be judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry (e.g., looking up in a table, database, or another data structure), ascertaining, etc.

[0532] Additionally, "determining" may be considered to be "determining" receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), etc.

[0533] Furthermore, "determination" may be considered to be "determining" resolving, selecting, choosing, establishing, comparing, etc. In other words, "determination" may be considered to be "determining" some kind of action. In the present disclosure, "determination" may be read interchangeably with the above-mentioned actions.

[0534] Furthermore, in this disclosure, "determine / determining" may be interchangeably read as "assume / assuming," "expect / expecting," "consider / considering," etc. Furthermore, in this disclosure, "does not expect to do..." may be interchangeably read as "assumes not to do...."

[0535] In the present disclosure, "expect" may be interchangeably read as "be expected." For example, "expect(s) ..." ("..." may be expressed, for example, as a that clause, a to-infinitive, etc.) may be interchangeably read as "be expected ..." or "do ... (if the above "..." is a to-infinitive, a verb with "to")," etc. "does not expect ..." may be interchangeably read as "be not expected ..." or "does not ... (if the above "..." is a to-infinitive, a verb with "to")," etc. Furthermore, "An apparatus A is not expected ..." may be interchangeably read as "an apparatus B other than apparatus A does not expect ... from apparatus A" (for example, if apparatus A is a UE, apparatus B may be a base station).

[0536] The "maximum transmit power" in this disclosure may mean the maximum value of transmit power, the nominal UE maximum transmit power, or the rated UE maximum transmit power.

[0537] As used in this disclosure, the terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access."

[0538] In this disclosure, when two elements are connected, they may be considered to be "connected" or "coupled" to one another using one or more wires, cables, printed electrical connections, etc., as well as using electromagnetic energy having wavelengths in the radio frequency range, microwave range, light (both visible and invisible) range, etc., as some non-limiting and non-exhaustive examples.

[0539] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0540] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0541] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0542] In the present disclosure, terms such as "less than or equal to," "less than," "greater than," "more than," "equal to," etc. may be interchangeable. Furthermore, in the present disclosure, terms meaning "good," "bad," "big," "small," "high," "low," "fast," "slow," "wide," "narrow," etc. may be interchangeable, not limited to the positive, comparative, and superlative. Furthermore, in the present disclosure, terms meaning "good," "bad," "big," "small," "high," "low," "fast," "slow," "wide," "narrow," etc. may be interchangeable, not limited to the positive, comparative, and superlative, as expressions with "i-th" (i is an arbitrary integer) attached (for example, "highest" may be interchangeable with "i-th highest").

[0543] In this disclosure, the terms "of," "for," "regarding," "related to," "associated with," etc. may be read interchangeably.

[0544] In the present disclosure, terms such as "when A, B," "if A, (then) B," "B upon A," "B in response to A," "B based on A," "B during / while A," "B before A," "B at (the same time as) / on A," "B after A," "B since A," and "B until A" may be interchangeable. Note that A, B, and the like herein may be replaced with appropriate expressions such as nouns, gerunds, and regular sentences, depending on the context. Note that the time difference between A and B may be approximately zero (immediately after or immediately before). A time offset may also be applied to the time at which A occurs. For example, "A" may be interchangeable with "before / after a time offset at which A occurs." The time offset (eg, one or more symbols / slots) may be predefined or may be specified by the UE based on signaled information.

[0545] In the present disclosure, timing, time, duration, time instance, any time unit (e.g., slot, subslot, symbol, subframe), period, occasion, resource, etc. may be read interchangeably.

[0546] Although the invention according to the present disclosure has been described in detail above, it is clear to those skilled in the art that the invention according to the present disclosure is not limited to the embodiments described in the present disclosure. The description of the present disclosure is for illustrative purposes only and does not impose any limiting meaning on the invention according to the present disclosure.

Claims

1. A network element having: a receiving unit that receives a request for model transfer; and a control unit that controls the start and end of model transfer based on the request.

2. The network element of claim 1, wherein the receiver receives a request for information about a transferable model.

3. The network element of claim 1, wherein the control unit controls the initiation of the model transfer based on at least one of information indicating which model is to be transferred and information indicating how the model is to be transferred.

4. The network element according to claim 1, wherein the control unit controls the termination of the model transfer based on receipt of an instruction to terminate the model transfer.

5. The network element according to claim 1, wherein the control unit controls the termination of the model transfer based on the transmission of an instruction to terminate the model transfer.

6. A wireless communication method for a network element, comprising: receiving a request for a model transfer; and controlling the initiation and termination of the model transfer based on the request.

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