Communication control method and user device

Abstract

A communication control method according to one aspect of the present invention is for a mobile communication system. The communication control method has a step in which a user device receives, from a network device, a first message including functionality use instruction information that instructs the use of a functionality of an artificial intelligence (AI) / machine learning (ML) model. In addition, the communication control method has a step in which the user device executes a prescribed operation in response to receiving the functionality use instruction information. The prescribed operation involves transmitting, to the network device, a second message including owned model information that indicates information related to an AI / ML model which corresponds to the functionality and which is owned by the user device.

Description

Communication control method and user device

[0001] The present disclosure relates to a communication control method and a user device.

[0002] In recent years, the Third Generation Partnership Project (3GPP) (registered trademark; the same applies hereinafter), a standardization project for mobile communication systems, has been studying the application of artificial intelligence (AI) technology, particularly machine learning (ML) technology, to wireless communication (air interface) of mobile communication systems.

[0003] 3GPP TR 38.843 V18.0.0 (2023-12)

[0004] A communication control method according to a first aspect is a communication control method in a mobile communication system. The communication control method includes a step of receiving, by a user equipment, a first message including function use instruction information instructing the user equipment to use a function of an AI / ML model from a network device. The communication control method also includes a step of performing a predetermined operation by the user equipment in response to receiving the function use instruction information. The predetermined operation is to transmit, to the network device, a second message including owned model information indicating information about an AI / ML model owned by the user equipment, which is an AI / ML model corresponding to the function.

[0005] A user device according to a second aspect is a user device in a mobile communication system. The user device has a receiving unit that receives a first message from a network device, the first message including function use instruction information instructing the use of a function of an AI / ML model. The user device also has a transmitting unit that executes a predetermined operation in response to receiving the function use instruction information. The predetermined operation is to transmit to the network device a second message including owned model information indicating information about an AI / ML model owned by the user device, the AI / ML model corresponding to the function.

[0006] FIG. 1 is a diagram illustrating an example of the configuration of a mobile communication system according to the first embodiment. FIG. 2 is a diagram illustrating an example of the configuration of a UE (user equipment) according to the first embodiment. FIG. 3 is a diagram illustrating an example of the configuration of a network node (base station) according to the first embodiment. FIG. 4 is a diagram illustrating an example of the configuration of a protocol stack according to the first embodiment. FIG. 5 is a diagram illustrating an example of the configuration of a protocol stack according to the first embodiment. FIG. 6 is a diagram illustrating an example of the configuration of functional blocks of AI / ML technology according to the first embodiment. FIG. 7(A) is a diagram illustrating an example of the configuration of functional blocks of a mobile communication system according to the first embodiment, and FIG. 7(B) is a diagram illustrating an example of the configuration of functional blocks of a UE according to the first embodiment. FIG. 8 is a diagram illustrating an example of the configuration of functional blocks of a mobile communication system according to the first embodiment. FIGS. 9(A) and 9(B) are diagrams illustrating an example of the configuration of functional blocks of a mobile communication system according to the first embodiment. FIGS. 10(A) and 10(B) are diagrams illustrating an example of the configuration of functional blocks of a mobile communication system according to the first embodiment. FIG. 11 is a diagram illustrating an example of operation according to the first embodiment. FIG. 12 is a diagram illustrating an example of operation according to the second embodiment.

[0007] An object of the present disclosure is to provide a communication control method and a user device that enable the user device to appropriately use an AI / ML model.

[0008] [First embodiment] A mobile communication system according to a first embodiment will be described with reference to the drawings. In the description of the drawings, the same or similar parts are denoted by the same or similar reference numerals.

[0009] (Configuration of mobile communication system) The configuration of a mobile communication system according to the first embodiment will be described. FIG. 1 is a diagram showing an example of the configuration of a mobile communication system 1 according to the first embodiment. The mobile communication system 1 conforms to the 5th Generation System (5GS) of the 3GPP standard. Although 5GS will be described below as an example, the mobile communication system may also be at least partially applied with an LTE (Long Term Evolution) system. The mobile communication system may also be at least partially applied with a sixth generation (6G) system or later system.

[0010] The mobile communication system 1 includes a network (NW) 10 and a user equipment (UE) 100. The UE 100 is a mobile communication device that performs wireless communication with the NW 10. The UE 100 may be any device used by a user, and may be, for example, a mobile phone terminal (including a smartphone), a tablet terminal, a notebook PC (Personal Computer), a communication module (including a communication card or chipset), a sensor or a device provided in a sensor, a vehicle or a device provided in a vehicle (Vehicle UE), or an aircraft or a device provided in an aircraft (Aerial UE).

[0011] The NW 10 includes a radio access network (RAN) 20 and a core network (CN) 30. When the mobile communication system is a 5th generation system (5GS), the RAN 20 is referred to as a Next Generation Radio Access Network (NG-RAN), and the CN 30 is referred to as a 5G Core Network (5GC).

[0012] The RAN 20 includes a plurality of network nodes 200 (network nodes 200a to 200c in the example of FIG. 1). The network nodes 200 are connected to each other via inter-network node interfaces. The network nodes 200 may be referred to as base stations in the RAN 20. When the network node 200 is a base station, the network node 200 may be configured (i.e., functionally divided) with a CU (Central Unit) and a DU (Distribution Unit), and the two units may be connected by a fronthaul interface. When the mobile communication system 1 is 5GS, the network node 200 is referred to as a gNB, the inter-network node interface is referred to as an Xn interface, and the fronthaul interface is referred to as an F1 interface.

[0013] When at least a part of the mobile communication system 1 is an LTE system, the network node 200 may be an evolved Node B (eNB) that is an LTE base station. When the mobile communication system 1 is a sixth-generation system or later, the network node 200 has a function of a base station and may be a device equivalent to a gNB or an eNB.

[0014] Each network node 200 manages one or more cells. The network node 200 performs wireless communication with the UE 100 that has established a connection with the network node 200's cell. Each network node 200 has a radio resource management (RRM) function, a user data (also simply referred to as "data") routing function, a measurement control function for mobility control and scheduling, and the like. The term "cell" is used as a term indicating the smallest unit of a wireless communication area. The term "cell" is also used as a term indicating a function or resource for performing wireless communication with the UE 100. One cell belongs to one carrier frequency. One downlink component carrier and one uplink component carrier may be associated with one cell. The bandwidth (system bandwidth) corresponding to one cell may be divided into multiple band parts (BWP: Bandwidth Parts). In the following, a gNB may be used as an example of the network node 200.

[0015] The CN 30 includes a CN (Core Network) device 300. The CN device 300 may include a C-plane device corresponding to the control plane (C-plane) and a U-plane device corresponding to the user plane (U-plane). The C-plane device performs various mobility controls and paging for the UE 100. The C-plane device communicates with the UE 100 using NAS (Non-Access Stratum) signaling. The U-plane device controls data forwarding. When the mobile communication system is 5GS, the C-plane device is called an AMF (Access and Mobility Management Function), the U-plane device is called a UPF (User Plane Function), and the interface between the network node 200 and the CN device 300 is called an NG interface.

[0016] In the following description, the network node 200 and the CN device 300 may be referred to as a network device. The network device may be the network node 200. The network device may be the CN device 300.

[0017] 2 is a diagram showing an example of the configuration of a UE 100 (user equipment) according to the first embodiment. The UE 100 includes a receiving unit 110, a transmitting unit 120, and a control unit 130. The receiving unit 110 and the transmitting unit 120 configure a communication unit 140 that performs wireless communication with a network node 200. The UE 100 is an example of a communication device.

[0018] The receiving unit 110 performs various types of reception under the control of the control unit 130. The receiving unit 110 includes an antenna and a receiver. The receiver converts a radio signal received by the antenna into a baseband signal (received signal) and outputs the baseband signal to the control unit 130.

[0019] The transmitting unit 120 performs various transmissions under the control of the control unit 130. The transmitting unit 120 includes an antenna and a transmitter. The transmitter converts a baseband signal (transmission signal) output by the control unit 130 into a radio signal and transmits it from the antenna.

[0020] The control unit 130 performs various controls and processes in the UE 100. Such processes include processes of each layer described below. The control unit 130 includes at least one processor and at least one memory. The memory stores programs executed by the processor and information used in the processes by the processor. The processor may include a baseband processor and a CPU (Central Processing Unit). The baseband processor performs modulation / demodulation, encoding / decoding, etc. of baseband signals. The CPU executes programs stored in the memory to perform various processes. Note that the processes or operations performed in the UE 100 may be performed in the control unit 130.

[0021] 3 is a diagram showing an example of the configuration of the network node 200 according to the first embodiment. The network node 200 includes a transmitting unit 210, a receiving unit 220, a control unit 230, and a NW communication unit 240. The transmitting unit 210 and the receiving unit 220 constitute a communication unit 250 that performs wireless communication with the UE 100. The NW communication unit 240 constitutes a backhaul communication unit that communicates with the CN 30. The network node 200 is an example of a communication device.

[0022] The transmitting unit 210 performs various transmissions under the control of the control unit 230. The transmitting unit 210 includes an antenna and a transmitter. The transmitter converts a baseband signal (transmission signal) output by the control unit 230 into a radio signal and transmits it from the antenna.

[0023] The receiving unit 220 performs various types of reception under the control of the control unit 230. The receiving unit 220 includes an antenna and a receiver. The receiver converts a radio signal received by the antenna into a baseband signal (received signal) and outputs the baseband signal to the control unit 230.

[0024] The control unit 230 performs various controls and processes in the network node 200. Such processes include processes of each layer described below. The control unit 230 includes at least one processor and at least one memory. The memory stores programs executed by the processor and information used in the processes by the processor. The processor may include a baseband processor and a CPU. The baseband processor performs modulation / demodulation, encoding / decoding, etc. of baseband signals. The CPU executes programs stored in the memory to perform various processes. Note that the processes or operations performed in the network node 200 may be performed by the control unit 230.

[0025] The NW communication unit 240 is connected to adjacent network nodes via an Xn interface, which is an interface between network nodes. The NW communication unit 240 is connected to the CN device 300 via an NG interface, which is an interface between a network node and a core network. The network node 200 may be configured (i.e., functionally divided) with a central unit (CU) and a distributed unit (DU), and both units may be connected via an F1 interface, which is a fronthaul interface.

[0026] FIG. 4 is a diagram showing an example of the configuration of a protocol stack of a radio interface of a user plane that handles data.

[0027] The user plane air interface protocol includes a physical (PHY) layer, a medium access control (MAC) layer, a radio link control (RLC) layer, a packet data convergence protocol (PDCP) layer, and a service data adaptation protocol (SDAP) layer.

[0028] The PHY layer performs encoding / decoding, modulation / demodulation, antenna mapping / demapping, and resource mapping / demapping. Data and control information are transmitted between the PHY layer of the UE 100 and the PHY layer of the network node 200 via a physical channel. The PHY layer of the UE 100 receives downlink control information (DCI) transmitted on a physical downlink control channel (PDCCH) from the network node 200. Specifically, the UE 100 performs blind decoding of the PDCCH using a radio network temporary identifier (RNTI), and acquires successfully decoded DCI as DCI addressed to the UE. The DCI transmitted from the network node 200 has a CRC (Cyclic Redundancy Code) parity bit scrambled by the RNTI added thereto.

[0029] In NR, the UE 100 can use a bandwidth narrower than the system bandwidth (i.e., the cell bandwidth). The network node 200 configures the UE 100 with a bandwidth portion (BWP) consisting of consecutive PRBs. The UE 100 transmits and receives data and control signals in the active BWP. For example, up to four BWPs may be configurable for the UE 100. Each BWP may have a different subcarrier spacing. The BWPs may overlap in frequency. When multiple BWPs are configured for the UE 100, the network node 200 can specify which BWP to apply by controlling the downlink. This allows the network node 200 to dynamically adjust the UE bandwidth according to the amount of data traffic of the UE 100, etc., thereby reducing UE power consumption.

[0030] The network node 200 can configure, for example, up to three control resource sets (CORESETs) for each of up to four BWPs on the serving cell. A CORESET is a radio resource for control information to be received by the UE 100. Up to 12 or more CORESETs may be configured for the UE 100 on the serving cell. Each CORESET may have an index of 0 to 11 or more. A CORESET may consist of six resource blocks (PRBs) and one, two, or three consecutive OFDM symbols in the time domain.

[0031] The MAC layer performs data priority control, retransmission processing using Hybrid Automatic Repeat reQuest (HARQ), random access procedures, etc. Data and control information are transmitted between the MAC layer of UE 100 and the MAC layer of network node 200 via a transport channel. The MAC layer of network node 200 includes a scheduler. The scheduler determines the uplink and downlink transport format (transport block size, modulation and coding scheme (MCS)) and resource blocks to be allocated to UE 100.

[0032] The RLC layer transmits data to the RLC layer on the receiving side using the functions of the MAC layer and the PHY layer. Data and control information are transmitted between the RLC layer of the UE 100 and the RLC layer of the network node 200 via logical channels.

[0033] The PDCP layer performs header compression / decompression, encryption / decryption, and the like.

[0034] The SDAP layer maps IP flows, which are units for Quality of Service (QoS) control by the core network, to radio bearers, which are units for QoS control by the access stratum (AS). Note that if the RAN is connected to the EPC, SDAP may not be required.

[0035] FIG. 5 is a diagram showing the configuration of a protocol stack of a radio interface of a control plane that handles signaling (control signals).

[0036] The protocol stack of the radio interface of the control plane includes a radio resource control (RRC) layer and a non-access stratum (NAS) instead of the SDAP layer shown in FIG.

[0037] RRC signaling for various settings is transmitted between the RRC layer of the UE 100 and the RRC layer of the network node 200. The RRC layer controls logical channels, transport channels, and physical channels in accordance with the establishment, re-establishment, and release of radio bearers. When there is a connection (RRC connection) between the RRC of the UE 100 and the RRC of the network node 200, the UE 100 is in an RRC connected state. When there is no connection (RRC connection) between the RRC of the UE 100 and the RRC of the network node 200, the UE 100 is in an RRC idle state. When the connection between the RRC of the UE 100 and the RRC of the network node 200 is suspended, the UE 100 is in an RRC inactive state.

[0038] The NAS, which is located above the RRC layer, performs session management, mobility management, etc. NAS signaling is transmitted between the NAS of the UE 100 and the NAS of the AMF. Note that the UE 100 has an application layer in addition to the radio interface protocol. Also, the layer below the NAS is called an Access Stratum (AS).

[0039] (AI / ML Technology) Next, an AI / ML (Artificial Intelligence / Machine Learning) technology according to an embodiment will be described. Fig. 6 is a diagram showing an example of the configuration of functional blocks of the AI / ML technology in the mobile communication system 1 according to the first embodiment.

[0040] The functional block configuration example shown in FIG. 6 includes a data collection unit (Data Collection) A1, a model training unit (Model Training) A2, a model inference unit (Inference) A3, a management unit (Management) A5, and a model storage unit (Model Storage) A6.

[0041] The functional block configuration example shown in FIG. 6 represents a functional framework of a general AI / ML technology. Therefore, depending on a hypothetical use case, some of the functional block configuration example (e.g., the model recording unit A6, etc.) may not be included in the functional block configuration example. The functional block configuration example shown in FIG. 6 may also be distributed between the UE 100 and the network device. Alternatively, some functions of the functional block configuration example (e.g., the model learning unit A2 or the model inference unit A3, etc.) may be located in both the UE 100 and the network device.

[0042] The data collection unit A1 provides input data to the model learning unit A2, the model inference unit A3, and the management unit A5. The input data includes training data for the model learning unit A2, inference data for the model inference unit A3, and monitoring data for the management unit A5.

[0043] The training data is data required as input when the AI / ML model is learning. The inference data is data required as input when the AI / ML model is inferring. The monitoring data is data required as input when the AI / ML model is managing.

[0044] In addition, data collection may refer to the process of collecting data at a network node, a management entity, or a UE 100, for example, to train an AI / ML model, manage an AI / ML model, and perform inference on an AI / ML model.

[0045] The model learning unit A2 performs AI / ML model training, AI / ML model validation, and AI / ML model testing.

[0046] AI / ML model learning is the process of learning an AI / ML model from input / output relationships to obtain a trained AI / ML model to be used for inference. For example, considering y = ax + b, AI / ML model learning may be the process of optimizing a (slope) and b (intercept) by providing input (x) and output (y) (i.e., providing learning data).

[0047] Generally, machine learning includes supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a method that uses correct answer data as training data. Unsupervised learning is a method that does not use correct answer data as training data. For example, unsupervised learning memorizes feature points from a large amount of training data and determines the correct answer (estimates the range). Reinforcement learning is a method that assigns a score to the output result and learns how to maximize the score. Although supervised learning will be described below, unsupervised learning may also be applied as machine learning. Reinforcement learning may also be applied as machine learning.

[0048] The model learning unit A2 outputs a trained AI / ML model (Trained Model) obtained by AI / ML model learning to the model recording unit A6. The model learning unit A2 also outputs an updated AI / ML model (Updated Model) obtained by relearning the trained AI / ML model to the model recording unit A6.

[0049] In the following, AI / ML model learning may be referred to as "model learning" or "learning."

[0050] The model inference unit A3 performs AI / ML model inference. Specifically, the model inference unit A3 applies the inference data provided by the data collection unit A1 to the trained AI / ML model (or updated AI / ML model) to obtain inference output data. For example, in the equation y = ax + b, x corresponds to the inference data and y corresponds to the inference output data. Note that "y = ax + b" is an AI / ML model. A model with optimized slope and intercept, for example, "y = 5x + 3," is a trained AI / ML model. There are various model approaches, including linear regression analysis, neural networks, and decision tree analysis. The above "y = ax + b" can also be considered a type of linear regression analysis.

[0051] The model inference unit A3 outputs inference output data to the management unit A5. The model inference unit A3 also receives management instructions from the management unit A5. For example, the management instructions include selection of an AI / ML model, activation (deactivation) of an AI / ML model, switching of an AI / ML model, and fallback (performing inference without using an AI / ML model). The model inference unit A3 performs model inference in accordance with the management instructions.

[0052] Note that AI / ML model inference is, for example, a process of obtaining a set of outputs from a set of inputs using a trained AI / ML model (or an updated AI / ML model). Alternatively, model inference may be a process of obtaining inference output data from inference data using a trained AI / ML model (or an updated AI / ML model). Hereinafter, AI / ML model inference may be referred to as "model inference" or "inference."

[0053] In the following, an AI / ML model that is currently being trained (or updated) may be referred to as a training AI / ML model (or an updating AI / ML model). In the following, when there is no need to distinguish between a training (or updating) AI / ML model and a trained (or updated) AI / ML model, they may be simply referred to as an "AI / ML model."

[0054] The management unit A5 supervises operations on the AI / ML model (selection, activation, deactivation, switching, fallback, etc.). The management unit A5 also supervises monitoring of the AI / ML model. The management unit A5 can also perform operations to ensure appropriate inference operations based on monitoring data and inference output data. To this end, the management unit A5 outputs a model transfer and / or model delivery request (Model Transfer / Delivery Request) to the model recording unit A6, and causes the trained (or updated) AI / ML model recorded in the model recording unit A6 to be output to the model inference unit A3. The management unit A5 also outputs management instructions to the model inference unit A3 and supervises operations on the AI / ML model. Furthermore, the management unit A5 can output performance feedback and a re-learning request to the model learning unit A2, causing the model learning unit A2 to re-learn the AI / ML model (i.e., update the learned AI / ML model).

[0055] (Use Cases) Next, use cases to which the AI / ML technology is applied will be described. For example, there are the following three use cases to which the AI / ML technology is applied.

[0056] (X1.1) "CSI (Channel State Information) Feedback Enhancement"

[0057] (X1.2) "Beam management"

[0058] (X1.3) “Positioning accuracy enhancement”

[0059] (X1.1) CSI Feedback Enhancement "CSI Feedback Enhancement" represents a use case in which, for example, AI / ML techniques are applied to CSI fed back from the UE 100 to the network node 200. The CSI is information about a channel state in a downlink between the UE 100 and the network node 200. The CSI includes at least one of a Channel Quality Indicator (CQI), a Precoding Matrix Indicator (PMI), and a Rank Indicator (RI). The network node 200 performs, for example, downlink scheduling based on the CSI feedback from the UE 100.

[0060] The use case of "Improved CSI Feedback" has two sub-use cases: CSI compression in the frequency domain and CSI prediction in the time domain.

[0061] (X1.1.1) Sub-Use Case: CSI Compression In CSI compression, CSI inferred in the UE 100 using a trained AI / ML model is compressed in the UE 100. The compressed CSI is transmitted from the UE 100 to the network node 200.

[0062] 7A is a diagram illustrating an example of a functional block configuration in a mobile communication system 1 when CSI compression is used. As shown in FIG. 7A, the UE 100 includes a CSI generating unit 101, and the network node 200 includes a CSI reconstructing unit 201.

[0063] The CSI generation unit 101 has a CSI generation inference unit 1010 and a quantization unit 1011. The CSI generation inference unit 1010 infers CSI (inference output data) using a trained AI / ML model for input. The input to the CSI generation inference unit 1010 may be, for example, partial (or punctured) CSI. The partial CSI may be CSI measured using a CSI reference signal (CSI-RS: Channel State Information-Reference Signal) (or a demodulation reference signal (DMRS: Demodulation Reference Signal)) transmitted using resources equal to or less than a certain amount. Furthermore, the input to the CSI generation inference unit 1010 may be a CSI reference signal. When partial CSI is input, the output of the CSI generation inference unit 1010 becomes CSI with a larger number of CSIs than the partial CSI. Furthermore, when a CSI reference signal is input, the output of the CSI generation inference unit 1010 becomes CSI. Hereinafter, the output of the CSI generation inference unit 1010 is referred to as full CSI. The quantization unit 1011 quantizes the full CSI. Compression is performed by quantization. The quantization unit 1011 transmits the quantized full CSI to the network node 200 as CSI feedback.

[0064] The CSI reconstruction unit 201 includes an inverse quantization unit 2010 and an inference unit for CSI reconstruction 2011. The inverse quantization unit 2010 receives CSI feedback, inverse quantizes the quantized full CSI, and outputs the full CSI. The inference unit for CSI reconstruction 2011 infers the reconstructed full CSI from the output (full CSI) of the inverse quantization unit 2010 using a trained AI / ML model. The reconstructed full CSI is output from the CSI reconstruction unit 201.

[0065] The quantizer 1011 may be merged into the CSI generation inference unit 1010, and the inverse quantizer 2010 may also be merged into the CSI reconstruction inference unit 2011. The quantizer 1011 may have a pre-processing unit in front of the CSI generation inference unit 1010. The quantizer 1011 may have a post-processing unit in back of the CSI reconstruction inference unit 2011.

[0066] As shown in Figure 7 (A), the CSI compression sub-use case is based on a two-sided model in which inference is performed using a trained AI / ML model on both the UE 100 side and the network node 200 side.

[0067] (X.1.1.2) Sub-use case: CSI prediction In CSI prediction, a trained AI / ML model is used to infer (predict) future CSI from the history of past CSI.

[0068] 7B is a diagram illustrating a configuration example of a functional block in the UE 100 when CSI prediction is used. The CSI prediction is based on a UE-sided model in which inference is performed in the UE 100 using a trained AI / ML model.

[0069] As shown in Fig. 7(B), the UE 100 has a CSI prediction model 102. The CSI prediction model 102 has a trained AI / ML model that uses a past history of CSI as an input and infers future CSI (predicted CSI). The CSI prediction model 102 may be a CSI prediction inference unit. The UE 100 (CSI prediction model 102) transmits the predicted CSI to the network node 200 as CSI feedback.

[0070] The CSI prediction model 102 may have a pre-processing unit in a stage preceding the CSI prediction model 102. The CSI prediction model 102 may have a post-processing unit in a stage following the CSI prediction model 102.

[0071] (X1.2) Beam management The beam management use case has two sub-use cases: spatial-domain downlink beam prediction, which performs beam prediction in the spatial direction, and temporal downlink beam prediction, which performs beam prediction in the time direction. The spatial-domain downlink beam prediction is called "BM Case 1" (BM-Case 1), and the temporal downlink beam prediction is called "BM Case 2" (BM-Case 2).

[0072] FIG. 8 is a diagram showing an example of the configuration of functional blocks in the mobile communication system 1 when BM Case 1 is used. In the case of BM Case 1, a UE-side model in which inference is performed in the UE 100 may be applied, or a NW-side model in which inference is performed on the network side may be applied. Therefore, as shown in FIG. 8, the AI / ML model 103 that performs the inference may be present in the UE 100. The AI / ML model 103 that performs the inference may be present in the NW 10 (or in the network node 200 or CN device 300 included in the NW 10).

[0073] In BM case 1, the input to the AI / ML model 103 is the measurement value for each beam included in beam set B. On the other hand, the output (inference output data) from the AI / ML model 103 is the probability that each (downstream) beam included in (predicted) beam set A will be the top beam. Beam set A and beam set B may be different. Alternatively, beam set B may be a subset of beam set A. The measurement value of beam set B, which is input to the AI / ML model 103, may be expressed in RSRP (Reference Signal Received Power).

[0074] 8 also shows a configuration example of the mobile communication system 1 in the case of BM case 2. In the case of BM case 2, the UE-side model may also be applied. In the case of BM case 2, the NW-side model may also be applied. In the case of BM case 2, the AI / ML model 103 exists in the UE 100 or the NW 10.

[0075] In BM case 2, the input to the AI / ML model 103 is the history of measurement values ​​for each beam included in beam set B. Measurement values ​​measured in the past for each beam are input to the AI / ML model 103. On the other hand, the output (inference output data) from the AI / ML model 103 is the probability that each (downstream) beam included in (predicted) beam set A will be the top beam, as in BM case 1. Beam set A and beam set B may be different. Beam set B may be a subset of beam set A. Also, in BM case 2, beam set A and beam set B may be the same.

[0076] In both BM Case 1 and BM Case 2, the UE-side model allows UE 100 to report the prediction results to NW 10. Also, in both BM Case 1 and BM Case 2, the NW-side model allows the top beam to be predicted based on measurements for each beam included in beam set B reported from UE 100.

[0077] (X1.3) "Positioning accuracy enhancement" In the use case of positioning accuracy enhancement, there are two sub-use cases: direct AI / ML positioning, which directly infers the position of UE 100 using a learned AI / ML model, and AI / ML assisted positioning, which infers intermediate position measurements. In the latter AI / ML assisted positioning, the position of UE 100 is measured or inferred in an LMF (Location Management Function) using intermediate position measurements. The intermediate position measurements can be assist information for measuring or inferring the position of UE 100 in the LMF. The LMF may have a trained AI / ML model and uses the trained AI / ML model to infer the location of the UE 100.

[0078] (X1.3.1) Sub-use case: Direct AI / ML positioning Figure 9(A) is a diagram showing an example of the configuration of functional blocks in the mobile communication system 1 when direct AI / ML positioning is used. In the case of direct AI / ML positioning, a UE-side model and a network-side model are applied. Therefore, the AI / ML model 104 used for inference may be present in the UE 100. The AI / ML model 104 used for inference may be present in the NW 10.

[0079] In the case of direct AI / ML positioning, the input to the AI / ML model 104 is a measurement value at each measurement point (TRP: Transmission and / or Reception Point). The measurement value may be, for example, a channel impulse response (CIR), a power delay profile (PDP), or a fingerprint. For example, both the CIR and the PDP represent the delay time for a signal at a specific frequency, but the CIR represents the instantaneous delay time, and the PDP represents the statistical delay time. Meanwhile, the output (inference output data) from the AI / ML model 104 is the location information of the UE 100. The location information may be represented by a fingerprint. The fingerprint represents, for example, measurement information for the cell of the UE 100.

[0080] (X1.3.2) Sub-Use Case: AI / ML-Assisted Positioning FIGS. 9(B) to 10(B) are diagrams illustrating an example of the configuration of functional blocks in the mobile communication system 1 when AI / ML-assisted positioning is used. In the case of AI / ML-assisted positioning, a UE-side model and a network-side model are also applied. Therefore, the AI / ML model 105 used for inference may reside in the UE 100. The AI / ML model 105 used for inference may reside in the NW 10. In the case of AI / ML-assisted positioning, there are cases where one AI / ML model 105 is used for multiple inputs (FIG. 9(B)), where the same AI / ML model is used for each of the multiple inputs (FIG. 10(A)), and where different AI / ML models are used for each of the multiple inputs (FIG. 10(B)).

[0081] In either case, the input to the AI / ML model 105 is a channel measurement value at each measurement point (TRP). The channel measurement value may be a CIR, PDP, or fingerprint, as in direct AI / ML positioning. Meanwhile, the output (inference output data) from the AI / ML model 105 is an intermediate position measurement value used for position measurement. The intermediate position measurement value may be an identification of line of sight (LOS) or non-line of sight (NLOS), measurement timing and / or measurement angle, or likelihood associated with the measurement.

[0082] (LCM) Currently, 3GPP is discussing LCM (Life Cycle Management) of the AI / ML model.

[0083] Specifically, the LCM of the AI / ML model may include at least one of the following operations.

[0084] (L1) Data collection

[0085] (L2) Model training

[0086] (L3) Identification

[0087] (L4) Model delivery / transfer

[0088] (L5) Model inference operation

[0089] (L6) Selection, Activation, Deactivation, Switching, and Fallback Operations

[0090] (L7) Monitoring

[0091] (L8) Model updating

[0092] (L9) UE capability

[0093] On the network side, for example, by controlling each operation of the LCM, it becomes possible to appropriately manage the process from the generation of an AI / ML model to the deletion (or disposal) of the AI / ML model. Note that fallback means switching from an AI / ML model to a model that does not use an AI / ML model. A model that does not use an AI / ML model is sometimes called a "legacy model."

[0094] Regarding LCM, 3GPP defines functionality-based LCM and model ID-based LCM.

[0095] The function-based LCM may be an operation performed on a function of the AI / ML model. Specifically, the function-based LCM may be any of an activation, deactivation, switching, and fallback operation performed on a function of the AI / ML model. The network side can instruct the LCM operation on the function of the AI / ML model, for example, by using 3GPP signaling (RRC message, MAC CE, or DCI). In the function-based LCM, the UE 100 may have one AI / ML model for one function. The UE 100 may have multiple AI / ML models for one function.

[0096] On the other hand, model ID-based LCM may be an operation performed on an individual AI / ML model using a model ID. The model ID is identification information for distinguishing an AI / ML model from other AI / ML models. Specifically, model ID-based LCM may be an operation of activating, deactivating, switching, or selecting an AI / ML model using the model ID.

[0097] (Communication control method according to the first embodiment) Each AI / ML model has functionality as an AI / ML model. When an AI / ML model realizes a certain function by executing inference in the AI / ML model, the function may represent a function in the AI / ML model. The function of the AI / ML model may represent a feature or a group of features that are enabled by the AI / ML model.

[0098] Furthermore, multiple AI / ML models may exist for one function. For example, assume that one function is BM Case 1 (a case in which beam prediction is performed in the time direction), which is a sub-use case of "beam management." In this case, there may be two AI / ML models having the function of BM Case 1: an AI / ML model for Yokohama City and an AI / ML model for Naka Ward, Yokohama City.

[0099] In such a case, for example, when the UE 100 is instructed by the network device to activate a function called BM Case 1, the UE 100 does not know which AI / ML model to activate, the AI / ML model for Yokohama City or the AI / ML model for Naka Ward, Yokohama City. Therefore, the UE 100 may not be able to appropriately use the AI / ML model for inference.

[0100] Therefore, the first embodiment aims to enable the UE 100 to appropriately use the AI / ML model.

[0101] Therefore, in the first embodiment, when the UE 100 receives an instruction to activate a function from the network device, the UE 100 transmits information about the AI / ML model that the UE 100 owns to the network device.

[0102] Specifically, first, a user device (e.g., UE 100) receives a first message from a network device, the first message including function use instruction information instructing the use of a function of the AI / ML model. Second, in response to receiving the function use instruction information, the user device transmits a second message to the network device, the second message including owned model information indicating information about the AI / ML model owned by the user device.

[0103] As a result, for example, the network device can grasp what AI / ML model the UE 100 holds for the function, and can instruct the AI / ML model to be used to the UE 100. Therefore, the UE 100 can perform inference using the AI / ML model instructed by the network device, and can appropriately use the AI / ML model.

[0104] (Example of Operation According to First Embodiment) Next, an example of operation according to the first embodiment will be described.

[0105] Fig. 11 is a diagram illustrating an example of operation according to the first embodiment. In Fig. 11, the network node 200 is used as an example of the network device. In Fig. 11, an OTT (Over-The-Top) server is a server device outside the NW 10.

[0106] In step S10, the receiving unit 110 of the UE 100 receives an AI / ML model (trained AI / ML model) from the OTT server. Additional information may be transmitted together with the AI / ML model. The additional information includes "Functionality #1" as a function ID indicating the function of the AI / ML model and "Model #1" as a model ID of the AI / ML model. By receiving the AI / ML model and the additional information, the UE 100 can determine that the received AI / ML model has "Functionality #1" as a function ID and "Model #1" as a model ID. The AI / ML model and the additional information may be transmitted using a message conforming to the TCP / IP (Transmission Control Protocol / Internet Protocol) protocol.

[0107] In step S11, the receiving unit 110 of the UE 100 receives an AI / ML model (trained AI / ML model) from the OTT server. The AI / ML model is an AI / ML model having "Functionality #1" as a function ID and "Model #2" as a model ID. The additional information includes the function ID and the model ID. The AI / ML model received in step S10 and the AI / ML model received in step S11 have the same function but are different AI / ML models. The receiving unit 110 receives the AI / ML model and the additional information.

[0108] In step S12, the receiving unit 220 of the network node 200 receives model information from the OTT server. The model information includes model IDs of the two AI / ML models (steps S10 and S11) transmitted by the OTT server to the UE 100. The model information may include function IDs of the two AI / ML models transmitted by the OTT server to the UE 100. The model information may be transmitted using a message conforming to the TCP / IP protocol.

[0109] In step S13, the control unit 230 of the network node 200 determines to use the AI / ML model of "Functionality #1".

[0110] In step S14, the transmitting unit 210 of the network node 200 transmits function use instruction information to the UE 100. The function use instruction information is information instructing the use of a function of the AI / ML model. The function use instruction information includes a function ID indicating the function to be used. Use based on the function use instruction information may be an instruction to activate the AI / ML model having the function (or function ID). In the example of FIG. 11 , the transmitting unit 210 transmits function use instruction information including "Functionality #1" as the function ID. The transmitting unit 210 of the network node 200 may transmit the function use instruction information by including it in a control message (e.g., a first message).

[0111] The control message may be an RRC message, which is signaling of the RRC layer (i.e., layer 3). The control message may be a MAC Control Element (CE), which is signaling of the MAC layer (i.e., layer 2). The control message may be Downlink Control Information (DCI), which is signaling of the PHY layer (i.e., layer 1). The downlink signaling may be UE-specific signaling. The downlink signaling may be broadcast signaling. The control message may be a message in a control layer (e.g., an AI / ML layer) specialized for artificial intelligence or machine learning. The receiving unit 110 of the UE 100 receives the control message including the function usage instruction information.

[0112] In step S15, in response to receiving the function use instruction information, the transmitter 120 of the UE 100 transmits owned model information. The owned model information indicates information about an AI / ML model that corresponds to the function instructed by the function use instruction information and that is owned by the UE 100.

[0113] First, the owned model information may indicate information on all AI / ML models that correspond to the function indicated by the function use instruction information and that are owned by the UE 100. In the example shown in Fig. 11 , the UE 100 holds two AI / ML models having the function ("Functionality #1") in steps S10 and S11. Therefore, the transmitter 120 of the UE 100 transmits, to the network node 200, owned model information that includes the model IDs ("Model #1" and "Model #2") of the two AI / ML models that correspond to "Functionality #1."

[0114] Second, the owned model information may indicate information about an AI / ML model corresponding to the function indicated by the function use instruction information, which is the AI / ML model with the best performance among all AI / ML models owned by the UE 100. For example, in the example of FIG. 11 , if the AI / ML model "Model #1" has better performance than the AI / ML model "Model #2," owned model information including "Model #1" as the model ID is transmitted. Whether the performance is good or not may be determined by the control unit 130 of the UE 100 based on the past history of the UE 100. Alternatively, whether the performance is good or not may be determined by the control unit 130 of the UE 100 based on application conditions indicating conditions under which the AI / ML model is applied. The application conditions may be transmitted in advance from the network node 200 to the UE 100 using a control message.

[0115] Thirdly, the model notified by the owned model information may be dependent on the implementation of the UE 100 .

[0116] Fourth, if the UE 100 does not hold an AI / ML model corresponding to a function instructed by the function usage instruction information, the owned model information may include a statement to that effect (that the UE 100 does not hold the AI / ML model) and a reason (Cause). Alternatively, even if the UE 100 holds an AI / ML model corresponding to a function instructed by the function usage instruction information, if the UE 100 does not want to use the model due to a resource condition (such as insufficient CPU power), the owned model information may include a statement to that effect and a reason. The resource condition may be an execution condition required by a network device when the UE 100 executes the AI / ML model. The resource condition may be included in a control message transmitted from the network node 200.

[0117] Fifth, the ownership model information may be transmitted in a control message (for example, the second message). The receiving unit 220 of the network node 200 receives the control message including the ownership model information.

[0118] In step S16 , in response to receiving the owned model information, the transmission unit 210 of the network node 200 transmits model use instruction information to the UE 100 .

[0119] The model usage instruction information is information that instructs the use of an AI / ML model. The model usage instruction information includes a model ID (e.g., "Model #1") of the AI / ML model to be used. The model usage instruction information may include multiple model IDs. In this case, the UE 100 may select an AI / ML model of any model ID from the multiple model IDs and use it for inference. The model usage instruction information may be transmitted in a control message (e.g., a third message). The receiver 110 of the UE 100 receives the control message including the model usage instruction information.

[0120] In step S17, the control unit 130 of the UE 100 starts inference using the AI / ML model instructed by the model use instruction information.

[0121] (Another Operation Example 1 According to First Embodiment) In the first embodiment, an example has been described in which the OTT server performs model transfer (steps S10 and S11) and transmission of model information (step S12).

[0122] For example, instead of the OTT server, the network node 200 may perform the model transfer and the transmission of the model information. In this case, for example, the network node 200 may perform the model transfer and the transmission of the model information, and may also perform the processes of steps S13 to S16. In this case, the model transfer and the transmission of the model information may be performed using a control message.

[0123] Also, for example, instead of an OTT server, the CN device 300 may perform model transfer and transmission of model information. If the CN device 300 is an AMF, model transfer and transmission of model information may be performed using a NAS message. If the CN device 300 is a UPF, model transfer and transmission of model information may be performed using a U-plane message. If the CN device 300 is an LMF, model transfer and transmission of model information may be performed using an LPP (LTE Positioning Protocol) message.

[0124] (Another Operation Example 2 According to First Embodiment) In the first embodiment, an example has been described in which the network node 200 performs the processes of steps S13 to S16. For example, the CN device 300 may perform the processes of steps S13 to S16 instead of the network node 200. In this case, steps S14 to S16 may be performed using a NAS message if the CN device 300 is an AMF, a U-plane message if the CN device 300 is a UPF, or an LPP message if the CN device 300 is an LMF.

[0125] Second Embodiment Next, a second embodiment will be described, focusing on the differences from the first embodiment.

[0126] In the first embodiment, an example in which the network node 200 transmits function use instruction information including a function ID (step S14 in FIG. 11 ) will be described. In the second embodiment, an example in which the network device transmits function use instruction information including conditions for identifying an AI / ML model will be described. The UE 100 will perform model inference by using (or activating) an AI / ML model according to the conditions.

[0127] Specifically, first, a user device (e.g., UE 100) receives a first message from a network device, the first message including function use instruction information instructing the use of a function of an AI / ML model. Here, the function use instruction information includes a model condition indicating a condition for identifying an AI / ML model to be used in the user device. Second, in response to receiving the function use instruction information, the user device identifies an AI / ML model corresponding to the function based on the model condition, and performs inference using the identified AI / ML model.

[0128] As described above, in the second embodiment, even when the UE 100 receives function use instruction information from a network device, the UE 100 can identify an AI / ML model corresponding to the function instructed in the function use instruction information based on the model condition. Therefore, the UE 100 can appropriately perform inference using the AI / ML model. Furthermore, in the second embodiment, the UE 100 does not transmit the model ID (i.e., owned model information) of the AI / ML model it holds to the network device (step S15 in FIG. 11 ), thereby reducing the processing load of the UE 100.

[0129] (Example of Operation According to Second Embodiment) Next, an example of operation according to the second embodiment will be described.

[0130] 12 is a diagram illustrating an example of operation according to the second embodiment. In FIG. 12, the same processes as those in the first embodiment are denoted by the same reference numerals. In FIG. 12, steps S10 to S13 are the same as those in the first embodiment.

[0131] In step S20, the transmitter 210 of the network node 200 transmits function use instruction information to the UE 100. The function use instruction information according to the second embodiment includes a model condition in addition to the function ID of the function to be used. The model condition indicates, for example, a condition for identifying the AI / ML model to be used in the UE 100.

[0132] First, the model condition may include a model ID that identifies an AI / ML model. That is, the AI / ML model represented by the model ID becomes the AI / ML model to be used in the UE 100. The model condition may include multiple model IDs. In this case, the UE 100 may select an AI / ML model with an arbitrary model ID from the multiple model IDs and use it for inference.

[0133] Second, the model condition may include performance index information indicating a performance index of the AI / ML model. The performance index may be expressed by an index such as a key performance indicator (KPI) or a normalized mean square error (MMSE). In this case, the UE 100 may determine whether to use the AI / ML model based on the performance index. The model condition may include a threshold value along with the performance index information. In this case, an AI / ML model whose index represented by the performance index is equal to or greater than the threshold value may be the model to be used. If there are multiple AI / ML models whose index is equal to or greater than the threshold value, the UE 100 may select an arbitrary AI / ML model from the multiple AI / ML models. The threshold value may be included in a control message (e.g., system information (SIB)) and transmitted in advance from the network node 200 to the UE 100.

[0134] Third, the model conditions may include information indicating that the model conditions are up to the UE 100 (up to UE).

[0135] The function use instruction information including the model condition may be transmitted by using a control message (for example, a first message). The receiving unit 110 of the UE 100 receives the function use instruction information including the model condition.

[0136] In step S21, the control unit 130 of the UE 100 determines an AI / ML model to be used from among the AI / ML models corresponding to the function ID included in the function use instruction information, based on the model conditions. In the example of Fig. 12, the control unit 130 determines the AI / ML model to be used by specifying one of the two AI / ML models (the AI / ML model "Model #1" and the AI / ML model "Model #2") corresponding to the function ID "Functionality #1" included in the function use instruction information.

[0137] In step S22, the transmitter 120 of the UE 100 transmits usage model information to the network node 200. The usage model information includes information (e.g., a model ID) related to the AI / ML model used for inference. In the example of Fig. 12, usage model information including "Model #1" is transmitted as information related to the AI / ML model used for inference. The transmitter 120 may transmit a control message (e.g., a fourth message) including the usage model information to the network node 200.

[0138] In step S23, the control unit 130 of the UE 100 performs inference using the AI / ML model determined to be used in step S21.

[0139] The second embodiment may represent an example of proactive model transfer. Proactive model transfer is, for example, a technique in which an AI / ML model is downloaded in advance to the UE 100, and model switching is performed when a change occurs in the scenario, setting, or location. This is because, as shown in FIG. 12 , an AI / ML model is downloaded in advance in steps S10 and S11, and an AI / ML model to be used is designated in step S20.

[0140] (Another Operation Example 1 According to the Second Embodiment) In the second embodiment, the function use instruction information (step S20) has been described as the same type of information as in the first embodiment. The information including the model condition and the function ID of the function to be used may be new information different from the function use instruction information according to the first embodiment.

[0141] (Another operation example 2 according to the second embodiment) In the second embodiment, model transfer (steps S10 and S11) and transmission of model information (step S12) may be performed by the network node 200 or the CN device 300 instead of the OTT server, as in the first embodiment.

[0142] (Another Operation Example 3 According to Second Embodiment) In the second embodiment, an example has been described in which the network node 200 performs the processes of steps S20 and S22. For example, the CN device 300 may perform the processes of steps S20 and S22 instead of the network node 200. In this case, in steps S20 and S22, the processes may be performed using a NAS message if the CN device 300 is an AMF, a U-plane message if the CN device 300 is a UPF, or an LPP message if the CN device 300 is an LMF.

[0143] [Other Embodiments] In the first and second embodiments described above, examples have been described in which the use is "activation." For example, the use may be an LCM operation other than "activation." For example, the "use" indicated as the function use instruction information (step S14) in the first embodiment may be "selection," "switching," "deactivation," or "fallback." In this case, the "use" described in the first embodiment can be implemented by being read as any of "selection," "switching," "deactivation," or "fallback." The same applies to the "use" indicated in the function use instruction information (step S20) in the second embodiment. In the second embodiment, the "use" described in the second embodiment can be implemented by being read as any of "selection," "switching," "deactivation," or "fallback."

[0144] In the first embodiment described above, supervised learning has been mainly described, but the present invention is not limited to this. For example, unsupervised learning or reinforcement learning may be applied to the first embodiment.

[0145] The above-described operational flows are not limited to being implemented independently, but can be implemented by combining two or more operational flows. For example, some steps of one operational flow may be added to another operational flow, or some steps of one operational flow may be replaced with some steps of another operational flow. In each flow, it is not necessary to execute all steps, and only some steps may be executed. Furthermore, the order of steps in each flow may be changed as appropriate.

[0146] In the above embodiment, an example in which the base station is an NR base station (gNB) has been described, but the base station may be an LTE base station (eNB) or a 6G base station. The base station may also be a relay node such as an IAB (Integrated Access and Backhaul) node. The base station may also be a DU of the IAB node. The UE 100 may also be an MT (Mobile Termination) of the IAB node. That is, the UE 100 may be a terminal function unit (a type of communication module) for the base station to control a relay that relays signals. Such a terminal function unit is referred to as an MT. Examples of MTs include, in addition to IAB-MT, NCR (Network Controlled Repeater)-MT and RIS (Reconfigurable Intelligent Surface)-MT.

[0147] The term "network node" primarily refers to a base station, but may also refer to a core network device or a part of a base station (CU, DU, or RU). A network node may also be configured by a combination of at least a part of a core network device and at least a part of a base station.

[0148] A program may be provided that causes a computer to execute each process according to the above-described embodiments. The program may be recorded on a computer-readable medium. The computer-readable medium can be used to install the program on a computer. Here, the computer-readable medium on which the program is recorded may be a non-transitory storage medium. The non-transitory storage medium is not particularly limited, and may be, for example, a storage medium such as a CD-ROM or a DVD-ROM. Furthermore, circuits that execute each process performed by the device according to the above-described embodiments may be integrated, and at least a part of the device may be configured as a semiconductor integrated circuit (chipset, SoC).

[0149] The functions performed by the apparatus according to the above-described embodiments may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), a central processing unit (CPU), conventional circuits, and / or combinations thereof, programmed to perform the described functions. A processor includes transistors and other circuits and is considered to be circuitry or processing circuitry. A processor may also be a programmed processor that executes a program stored in a memory. In this disclosure, circuitry, units, and means refer to hardware that is programmed to perform or executes the described functions. The hardware may be any hardware disclosed herein or any hardware known to be programmed or capable of performing the described functions. If the hardware is a processor, the circuitry, means, or unit is a combination of the hardware and software used to configure the hardware and / or processor.

[0150] As used in this disclosure, the terms "based on" and "depending on / in response to" do not mean "based only on" or "depending only on," unless expressly stated otherwise. The term "based on" means both "based only on" and "based at least in part on." Similarly, the term "depending on" means both "depending only on" and "depending at least in part on." The terms "include," "comprise," and variations thereof do not mean including only the listed items, but may mean including only the listed items or may include additional items in addition to the listed items. Additionally, the term "or," as used in this disclosure, is not intended to mean an exclusive or. Furthermore, any reference to elements using designations such as "first," "second," etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used herein 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 therein or that the first element must precede the second element in some way. In this disclosure, where articles are added by translation, such as a, an, and the in English, these articles shall include the plural unless the context clearly indicates otherwise.

[0151] Although the embodiments have been described in detail above with reference to the drawings, the specific configuration is not limited to the above, and various design changes can be made within the scope of the gist. Furthermore, the embodiments, operation examples, and processes can be combined as appropriate within the scope of not causing any contradiction.

[0152] This application claims priority from Japanese Patent Application No. 2024-129133 (filed August 5, 2024), the entire contents of which are incorporated herein by reference.

[0153] (Additional Notes) The above can be summarized as in the additional notes, but the additional notes do not limit the embodiments.

[0154] (Supplementary Note 1) A communication control method in a mobile communication system, comprising: a step in which a user device receives a first message from a network device, the first message including function use instruction information instructing the user device to use a function of an AI / ML model; and a step in which the user device executes a predetermined operation in response to receiving the function use instruction information, wherein the predetermined operation is to transmit to the network device a second message including owned model information indicating information about the AI / ML model owned by the user device, the second message being an AI / ML model corresponding to the function.

[0155] (Supplementary Note 2) The communication control method according to Supplementary Note 1, wherein the owned model information is any one of information on all AI / ML models corresponding to the function that are owned by the user device, information on the AI / ML model that corresponds to the function and has the best performance among all AI / ML models owned by the user device, and information on the AI / ML model determined according to the user device.

[0156] (Supplementary Note 3) The communication control method according to Supplementary Note 1 or Supplementary Note 2, wherein the function use instruction information includes function identification information indicating the function to be used.

[0157] (Supplementary Note 4) A communication control method according to any one of Supplementary Notes 1 to 3, further comprising the steps of: the user device receiving from the network device a third message including model usage instruction information instructing the use of the AI / ML model; and the user device performing inference using the instructed AI / ML model in response to receiving the model usage instruction information, wherein the model usage instruction information includes model identification information of the AI / ML model to be used.

[0158] (Supplementary Note 5) A communication control method according to any one of Supplementary Notes 1 to 4, wherein, when the function use instruction information includes a model condition indicating a condition for identifying the AI / ML model to be used in the user device, the predetermined operation is to identify the AI / ML model corresponding to the function based on the model condition without transmitting the second message, and to perform inference using the identified AI / ML model.

[0159] (Supplementary Note 6) The communication control method according to any one of Supplementary Note 1 to Supplementary Note 5, wherein the model condition includes any one of model identification information that identifies the AI / ML model, performance index information that indicates a performance index of the AI / ML model, and information indicating that it depends on the user device.

[0160] (Supplementary Note 7) The communication control method according to any one of Supplementary Note 1 to Supplementary Note 6, further comprising a step in which the user device transmits to the network device a fourth message including usage model information regarding the AI / ML model used for the inference.

[0161] (Supplementary Note 8) A user device in a mobile communication system, comprising: a receiving unit that receives from a network device a first message including function use instruction information instructing the use of a function of an AI / ML model; and a transmitting unit that executes a predetermined operation in response to receiving the function use instruction information, wherein the predetermined operation is to transmit to the network device a second message including owned model information indicating information about the AI / ML model owned by the user device, the second message being an AI / ML model corresponding to the function.

[0162] 1: Mobile communication system 10: NW 20: RAN 30: CN 100: UE 101: CSI generation unit 102: CSI prediction model 103: AI / ML model 104: AI / ML model 105: AI / ML model 110: Receiving unit 120: Transmitting unit 130: Control unit 140: Communication unit 200: Network node 201: CSI reconstruction unit 210: Transmitting unit 220: Receiving unit 230: Control unit 240: NW communication unit 250: Communication unit 300: CN device 1010: Inference unit for CSI generation 1011: Quantization unit 2010: Inverse quantization unit 2011: Inference unit for CSI reconstruction A1 A2: Data collection unit A3: Model learning unit A5: Management unit A6: Model recording unit

Claims

1. A communication control method in a mobile communication system, comprising: a user device receiving from a network device a first message including function use instruction information instructing the user device to use a function of an AI (Artificial Intelligence) / ML (Machine Learning) model; and the user device executing a predetermined operation in response to receiving the function use instruction information, wherein the predetermined operation is to transmit to the network device a second message including owned model information indicating information about the AI / ML model owned by the user device, the AI / ML model corresponding to the function.

2. The communication control method according to claim 1, wherein the owned model information is any one of information regarding all AI / ML models corresponding to the function that are owned by the user device, information regarding the AI / ML model that has the best performance among all AI / ML models corresponding to the function that are owned by the user device, and information regarding the AI / ML model determined according to the user device.

3. A communication control method according to claim 1, wherein said function use instruction information includes function identification information indicating said function to be used.

4. The communication control method according to claim 1, further comprising: the user device receiving from the network device a third message including model usage instruction information instructing the use of the AI / ML model; and the user device performing inference using the instructed AI / ML model in response to receiving the model usage instruction information, wherein the model usage instruction information includes model identification information of the AI / ML model to be used.

5. A communication control method according to claim 1, wherein, when the function use instruction information includes a model condition indicating a condition for identifying the AI / ML model to be used in the user device, the predetermined operation is to identify the AI / ML model corresponding to the function based on the model condition without transmitting the second message, and to perform inference using the identified AI / ML model.

6. A communication control method according to claim 5, wherein the model conditions include any one of model identification information that identifies the AI / ML model, performance index information that indicates a performance index of the AI / ML model, and information indicating that it depends on the user device.

7. The communication control method according to claim 5, further comprising the user device transmitting to the network device a fourth message including usage model information regarding the AI / ML model used for the inference.

8. A user device in a mobile communication system, comprising: a receiving unit that receives from a network device a first message including function use instruction information instructing the use of a function of an AI / ML model; and a transmitting unit that executes a predetermined operation in response to receiving the function use instruction information, wherein the predetermined operation is to send to the network device a second message including owned model information indicating information about the AI / ML model owned by the user device, the AI / ML model corresponding to the function.

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