Communication control method and user device

Abstract

A communication control method according to one aspect of the present invention is used in a mobile communication system. The communication control method includes a step in which a first communication device transmits, to a second communication device, difference information pertaining to at least one of corresponding function information and a function condition. The corresponding function information indicates information relating to an artificial intelligence (AI) / machine learning (ML) model that can be handled by the first communication device, and the function condition indicates a condition for handling the function of the AI / ML model in the first communication 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 transmitting, by a first communication device, difference information of at least one of supported function information and functional conditions to a second communication device. Here, the supported function information indicates information on functions of an AI / ML model that can be supported by the first communication device, and the functional conditions indicate conditions for supporting the functions of the AI / ML model in the first communication device.

[0005] A user device according to a second aspect is a user device in a mobile communication system. The user device has a transmitter that transmits difference information of at least one of supported function information and functional conditions to a network device. Here, the supported function information indicates functions of an AI / ML model that the user device can support, and the functional conditions indicate conditions for the user device to support the functions of the AI / ML model.

[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 the type of function according to the first embodiment. Fig. 12(A) and Fig. 12(B) are diagrams showing examples of supported function information according to the first embodiment, and Fig. 12(C) is a diagram showing an example of difference information according to the first embodiment. Fig. 13 is a diagram showing an example of difference information according to the first embodiment. Fig. 14 is a diagram showing an operation example according to the first embodiment. Fig. 15 is a diagram showing an operation example according to the first embodiment. Fig. 16 is a diagram showing an operation example according to the first embodiment. Fig. 17 is a diagram showing a second operation example according to the second embodiment.

[0007] The present disclosure aims to improve the efficiency of communication of information related to functions.

[0008] The mobile communication system according to the 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] [First Embodiment] 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 the following description will be given using 5GS as an example, the mobile communication system may also be at least partially applied to an LTE (Long Term Evolution) system. The mobile communication system may also be at least partially applied to 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) and / or 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.

[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 contiguous PRBs (Physical Resource Blocks). 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 Orthogonal Frequency Division Multiplex (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 the UE 100 and the MAC layer of the network node 200 via a transport channel. The MAC layer of the network node 200 includes a scheduler. The scheduler determines the uplink and downlink transport format (transport block size, modulation and coding scheme (MCS)) and the resource blocks to be allocated to the 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, either unsupervised learning or reinforcement learning may 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 includes 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) (or a demodulation reference signal (DMRS)) transmitted using resources equal to or less than a certain amount. The input to the CSI generation inference unit 1010 may also 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 quantization unit 1011 may be merged into the CSI generation inference unit 1010, and the inverse quantization unit 2010 may also be merged into the CSI reconstruction inference unit 2011. Also, a pre-processing unit may be provided in front of the CSI generation inference unit 1010, and a post-processing unit may be provided 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] Note that a pre-processing unit may be provided in front of the CSI prediction model 102 , and a post-processing unit may be provided in the rear of 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] 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, or 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, either the UE-side model or the NW-side model may be applied. In the case of BM case 2, the AI / ML model 103 is present 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, as in BM case 1, 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, or 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 or 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) show examples of functional block configurations 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 or the NW 10. In the case of AI / ML-assisted positioning, there are three cases: one AI / ML model 105 is used for multiple inputs ( FIG. 9(B) ); the same AI / ML model is used for each of the multiple inputs ( FIG. 10(A) ); and 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 at each measurement point (TRP). The channel measurement 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 intermediate position measurements used for positioning. The intermediate position measurements may include identification of LOS or 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] (Types of Functions 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 feature group that is enabled by the AI / ML model. The process of identifying the function of an AI / ML model is called functional identification.

[0098] Currently, 3GPP is discussing the type of function. Using the type of function, for example, the following control is possible. That is, UE 100 notifies network 10 of the type of function of the AI / ML model. When network 10 determines, based on the type of function, that UE 100 can apply the function of the AI / ML model, it can instruct UE 100 to activate the function. On the other hand, when network 10 determines, based on the type of function, that UE 100 supports the function of the AI / ML model but does not hold an AI / ML model having the function and therefore is not available, it can also perform control such that it does not instruct UE 100 to activate the function. In this way, network 10 can instruct or not instruct activation of the function depending on the type of function, thereby enabling appropriate control of UE 100.

[0099] 11 is a diagram showing examples of function types according to the first embodiment. As shown in FIG. 11, the following are examples of function types.

[0100] Supported functionality: The supported functionality indicates, for example, the maximum functionality that can be theoretically supported. The supported functionality may indicate the functionality of the AI / ML model that the UE 100 can indicate using a signal indicating the capability of the UE 100 (capability signaling). The supported functionality may indicate that the UE 100 can support the functionality of the AI / ML model, but it is not necessarily required that the UE 100 hold an AI / ML model having the functionality.

[0101] Available functionality: The available functionality indicates, for example, a function that can be supported based on conditions of hardware and / or software. The available functionality may indicate a function of an AI / ML model that can be used in the UE 100.

[0102] Applicable functions: Applicable functions refer to, for example, functions that can actually be activated. Applicable functions may refer to at least one function of an applicable AI / ML model that is available for use. Applicable functions may also refer to functions of an AI / ML model that are applicable in the UE 100.

[0103] Configured functionality: The configured functionality indicates, for example, the functionality of the AI / ML model that the network 10 (or a network device) configures for the UE 100.

[0104] Activated functionality: Activated functionality refers to, for example, functionality of an AI / ML model that has already been activated.

[0105] As shown in FIG. 11, the supported functions have the widest range compared to the other function types, and the activated functions have the narrowest range compared to the other function types.

[0106] (Communication control method according to the first embodiment) For example, assume the following case. That is, the UE 100 transmits information about the functions of the AI / ML model to a network device. The information about the functions includes, for example, information indicating what functions the AI / ML model has in the UE 100. The information about the functions also includes information about the conditions for applying the functions.

[0107] In such a case, even if the network device receives information about the function, it may use only some of the information rather than all of the information to instruct UE 100 to activate the function of the AI / ML model.

[0108] For example, when "BM Case 1," which is a use case of beam management, is considered as one function, it is assumed that the conditions for applying the function are "maximum transmission power of 20 dBm or more" and "training on an OTT server as a training condition." In this case, the network device may instruct activation of the function by considering "maximum transmission power of 20 dBm or more" without considering "training on an OTT server as a training condition."

[0109] In this way, there is a case where all of the information on the functions transmitted from the UE 100 is not used, and the transmission from the UE 100 is wasted. On the other hand, when the amount of information on the functions exceeds a certain amount, the transmission of the information on the functions itself may become a load on the UE 100.

[0110] Similarly, even when information about a function is transmitted from a network device to the UE 100, the UE 100 may not use all of the information about the function to activate the AI / ML model having the function. Therefore, the radio resources used for the transmission may be wasted.

[0111] Therefore, the first embodiment aims to improve the communication efficiency of information related to functions.

[0112] Therefore, in the first embodiment, the UE 100 or the network device transmits difference information of previously transmitted information regarding the function. Specifically, the first communication device (e.g., the UE 100 or the network device) transmits difference information of at least one of the supported function information and the function conditions to the second communication device (e.g., the network device or the UE 100). Here, the supported function information indicates information regarding the functions of the AI / ML model that can be supported by the first communication device, and the function conditions indicate the conditions for supporting the functions of the AI / ML model in the first communication device.

[0113] As a result, for example, when transmitting information related to a function, the UE 100 or the network device can transmit difference information of information related to a function that has been previously transmitted. As a result, the UE 100 or the network device only needs to transmit difference information compared to a case in which all of the information related to the function is transmitted, and therefore, it is possible to improve the communication efficiency of the information related to the function.

[0114] In the first embodiment, supported function information and functional conditions are used as examples of information related to functions. Here, the supported function information and functional conditions will be described.

[0115] (Supported Function Information and Function Conditions) The UE 100 can transmit supported function information to the network device. The supported function information is, for example, information indicating functions that can be supported by the UE 100 (or the network device).

[0116] FIG. 12(A) is a diagram showing an example of supported function information according to the first embodiment. As shown in FIG. 12(A), the supported function information includes information indicating whether each function of the AI / ML model is supported in the UE 100. In the example shown in FIG. 12(A), the supported function information includes a function ID, a function type, and information indicating whether the function is supported. The function ID indicates function identification information that identifies the function of the AI / ML model. The function type may represent the name of the function. FIG. 12(A) shows that "BM-Case 1" (beam case 1) with function ID = 1 is supported, and that "BM-Case 2" (beam case 2) with function ID = 2 is also supported.

[0117] As shown in FIG. 11 , there are five types of functions. "Functionalities" shown in FIG. 12(A) may be "Supported functions". In this case, "Supported" shown in FIG. 12(A) indicates that the function is supported in the UE 100. Furthermore, "Functionalities" shown in FIG. 12(A) may be "Available functions". In this case, "Supported" shown in FIG. 12(A) indicates that the function is available in the UE 100. Furthermore, "Functionalities" in FIG. 12(A) may be "Applicable functions". In this case, "Supported" indicates that the function is applicable in the UE 100. Furthermore, "Functionalities" in FIG. 12(A) may be "configured functions (Configured functions)". In this case, "supported" may indicate that the function is configured in UE100. Furthermore, "Functionalities" in FIG. 12(A) may be "activated functions (Activated functions)" in UE100. In this case, "supported" indicates that the function is activated in UE100.

[0118] Assuming a situation in which a message relating to each of the five types of functions is sent, the communication direction of the message relating to each function is, for example, as follows:

[0119] Supported functions: from UE 100 to network 10 Available functions: from UE 100 to network 10 Applicable functions: from UE 100 to network 10 Configured functions: from network 10 to UE 100 Activated functions: from UE 100 to network 10 It is assumed that a message regarding each function is transmitted from UE 100 to a network device, except that a message regarding a function configured in UE 100 is transmitted from the network device to UE 100. If the message regarding each function includes the supported function information shown in Figure 12 (A), the supported function information is also transmitted from UE100 to the network device, or from the network device to UE100, in the same way as the message regarding the configured function.

[0120] In the following, messages related to functions may be referred to as "function messages." There are five types of function messages as described above. Details will be explained in the operation example.

[0121] On the other hand, the functional conditions indicate, for example, conditions for supporting the functions of the AI / ML model. The functional conditions may be usage conditions for using the functions of the AI / ML model. Alternatively, the functional conditions may be additional information (additional condition) that is additionally added to the usage conditions. The functional conditions may be applied in the UE 100. The functional conditions may be applied in a network device. Examples of the functional conditions include "transmission power," "antenna height," and "CPU power." The functional conditions may be conditions necessary for using the functions of the AI / ML model.

[0122] 12B is a diagram showing an example of a function condition according to the first embodiment. As shown in FIG. 12B, the function condition is linked to the corresponding function information. The function condition may include multiple conditions. In this case, the function information includes a function ID, and a function condition is included for each function ID, so that a function condition can be set for each function.

[0123] The function conditions can also be transmitted together with the corresponding function information in a function message.

[0124] (Difference Information) As described above, when transmitting supported function information and function conditions, the UE 100 or the network device can transmit difference information between the previously transmitted supported function information and function condition functions. Specifically, when the UE 100 or the network device transmits a first function message including the supported function information and function conditions and then transmits a second function message, the UE 100 or the network device can transmit difference information between the supported function information and function conditions included in the first function message by including it in the second function message. Specifically, the following occurs.

[0125] First, as an example of difference information for the supported function information, for example, the following is assumed: That is, assume a case in which the UE 100 transmits a first function message including "BM Case 1" as the supported function information, and then the UE 100 wants to transmit "BM Case 1" and "BM Case 2" as the supported function information.

[0126] In this case, when UE 100 wants to transmit "BM Case 2," which is a difference from "BM Case 1" transmitted in the first function message, UE 100 can transmit a second function message including difference information "1" (difference present) in the supported function information and the difference "BM Case 2." Since the difference information is "1," the network device can determine that "BM Case 2" received in the second function message and "BM Case 1" received in the first function message are functions that UE 100 can support.

[0127] On the other hand, if UE 100 wants to transmit the second function message including both "BM Case 1" and "BM Case 2" in the supported function information, it can set the difference information of the supported function information to "0" (no difference). The second function message includes two pieces of supported function information and difference information = "0". The difference information of the supported function information = "0" indicates that all supported supported function information is to be transmitted, rather than the difference in the supported function information.

[0128] In this way, the difference information indicates either whether the corresponding function information included in the second function message indicates a difference from the corresponding function information included in the first function message, or whether the corresponding function information included in the second function message indicates all of the corresponding corresponding function information.

[0129] Secondly, as an example of difference information for a functional condition, for example, the following is assumed: That is, it is assumed that the UE 100 transmits a first functional message including "transmission power" as a functional condition, and then the UE 100 wants to transmit "transmission power" and "antenna height" as functional conditions.

[0130] In this case, when UE 100 wants to transmit the difference "antenna height" from the "transmission power" transmitted in the first function message, UE 100 may transmit a second function message including "1" (difference present) as the difference information of the function condition and the difference "antenna height." The network device can recognize that the "antenna height" received in the second function message and the "transmission power" received in the first function message are the function conditions.

[0131] On the other hand, when UE 100 wants to transmit two functional conditions, "transmission power" and "antenna height," in the second functional message, it may set "0" (no difference) as the functional condition difference information. The second functional message includes two functional conditions and difference information = "0." The functional condition difference information = "0" indicates that all compatible functional conditions are to be transmitted, rather than the difference between the functional conditions.

[0132] In this way, the difference information may either indicate the difference between the functional conditions included in the second functional message and the functional conditions included in the first functional message, or indicate all the corresponding functional conditions included in the second functional message.

[0133] (Structural Example of Difference Information) Next, a structural example of difference information according to the first embodiment will be described.

[0134] First, the difference information may be included in function identification information (function ID). FIG. 12(C) is a diagram illustrating an example of difference information according to the first embodiment. As illustrated in FIG. 12(C), the difference information may be included in the bit string of the function ID. The difference information may be included in any bit in the bit string of the function ID. The function ID may be included in the supported function information (FIG. 12(A)). In this case, the difference information may represent difference information for supported function information including the function ID. The difference information may represent difference information for a function condition (FIG. 12(B)) linked to the supported function information. The difference information may represent difference information for the supported function information and the function condition.

[0135] Second, the difference information may be included in meta information of the supported function information. FIG. 13 is a diagram illustrating an example of difference information according to the first embodiment. In this case, the difference information may also represent difference information for the supported function information linked to the meta information. The difference information may represent difference information for a functional condition linked to the supported function information. The difference information may represent difference information for the supported function information and the functional condition. As illustrated in FIG. 13 , the meta information is linked to the supported function information. The meta information includes information other than the information included in the supported function information and the functional condition (e.g., the learning date and learning location of the AI / ML model having the function). The meta information is transmitted together with the supported function condition and the functional condition in a function message.

[0136] Third, the second capability message may be a message transmitted immediately after the first capability message. That is, the first capability message transmitted immediately before the second capability message may be the target of the differential information. For example, when the UE 100 transmits a capability message (first capability message) regarding supported capabilities and then transmits a capability message (second capability message) regarding available capabilities, the differential information is included in the capability message regarding the available capabilities. In this case, the target of the differential information is the capability message regarding the supported capabilities.

[0137] Fourth, the second capability message and the first capability message may have the same type of capability. That is, the first capability message having the same type as the second capability message may be the target of the difference information. For example, when UE 100 transmits a capability message regarding "supported capabilities" including difference information, the difference information targets a previously transmitted capability message regarding the same type of "supported capabilities."

[0138] (Example of Operation According to First Embodiment) Next, an example of operation according to the first embodiment will be described.

[0139] 14 to 16 are diagrams showing an example of operation according to the first embodiment. In this example of operation, the network node 200 will be described as an example of a network device. Note that the difference information will be described as "1" indicating that a difference is to be transmitted, and "0" indicating that there is no difference and all corresponding information is to be transmitted. However, the meanings of "1" and "0" may be reversed. Also, as shown in FIGS. 14 to 16, messages for each function are transmitted and received, and the description will be given assuming that all of these function messages include supported function information and function conditions.

[0140] As shown in Fig. 14 , in step S10, the transmitter 120 of the UE 100 transmits a function message regarding supported functions (hereinafter, may be referred to as a "supported function message") to the network node 200. The supported function message is a message for notifying the UE 100 of functions supported by the UE 100. Since the supported function message is the first message to be transmitted, the differential information indicates that the supported function information is "0" (transmit all) and the function condition is also "0" (transmit all).

[0141] Note that various function messages are transmitted and received between the UE 100 and the network node 200, and these function messages may be RRC messages, MAC CEs, or new messages newly defined for the AI / ML model.

[0142] The receiving unit 220 of the network node 200 receives the supported capabilities message.

[0143] In step S11, the transmitter 210 of the network node 200 transmits a function message regarding the set function (hereinafter may be referred to as a "set function message") to the UE 100. The set function message is a message used to set a function for the UE 100. The function ID included in the set function message may be selected from the function IDs included in the supported function message (step S10). Since this function message is also the first function message transmitted from the network node 200, the difference information included in this set function message is such that the supported function information is "0" (transmit all) and the function condition is also "0" (transmit all). The receiver 110 of the UE 100 receives this set function message.

[0144] In step S12, the transmitter 120 of the UE 100 transmits a capability message regarding available capabilities (hereinafter, may be referred to as an "available capability message") to the network node 200. The available capability message is a message used to notify the UE 100 of available capabilities. The UE 100 may select available capabilities from the functions set in the set capability message (step S11). In the example shown in FIG. 14, the supported capability information and capability conditions included in the available capability message transmit all corresponding supported capability information and capability conditions without transmitting any differences. Therefore, the difference information is such that the supported capability information is "0" (all transmitted) and the capability conditions are also "0" (all transmitted). The receiver 220 of the network node 200 receives the available capability message.

[0145] In step S13, a change in functional conditions occurs in the UE 100. For example, a change occurs in the CPU resources in the UE 100.

[0146] In step S14, transmitter 120 of UE 100 transmits a supported function message in response to the change in the functional condition in step S13. The supported function message includes a difference in the functional condition (for example, "CPU resource" or a functional condition "CPU power" related to "CPU resource") and difference information "1" (difference present) about the functional condition.

[0147] First, the function message to be the target of the difference in function conditions may be the immediately preceding available function message (step S12). That is, the difference with respect to the function conditions included in the available function message is included in the function conditions of the supported function message.

[0148] Second, the function messages that are the subject of the difference in function conditions may be the same type of supported function message (step S10). That is, the difference in function conditions included in the supported function message transmitted in step S10 will be included in the function conditions of the supported function message transmitted in step S14.

[0149] In the example shown in FIG. 14, all of the supported function information is transmitted, and therefore the difference information of the supported function information is "0" (all transmitted).

[0150] The receiving unit 220 of the network node 200 receives the supported capabilities message.

[0151] In step S15, a change in functional conditions occurs in the network node 200. For example, the frequency used in the network node 200 changes.

[0152] In step S16, the transmitter 210 of the network node 200 transmits a set function message to the UE 100. In response to the change in the function condition in step S15, the transmitter 210 transmits a set function message including a difference in the function condition (for example, "frequency change" or a function condition "used frequency" related to the "frequency change") and difference information "1" (difference present) for the function condition. The function message that is the subject of the difference information is the set function message transmitted immediately before in step S11. In this case, the type of the function message transmitted in step S16 and the type of the function message transmitted in step S11 are the same. The receiver 110 of the UE 100 receives the set function message.

[0153] In step S17, the transmitter 120 of the UE 100 transmits an available function message. In the example shown in Fig. 14, since both the supported function information and the function conditions are transmitted, the difference information of the supported function information is "0" (all transmitted), and the difference information of the function conditions is also "0" (all transmitted). The receiver 220 of the network node 200 receives the available function message.

[0154] The processing from step S10 to step S17 is the ability notification phase.

[0155] In step S20 (FIG. 15), the OTT server transfers a model to the UE 100. Based on the function ID included in the available function message transmitted in step S17, the OTT server may transfer an AI / ML model having the function to the UE 100. Therefore, the NW communication unit 240 of the network node 200 may transmit the function ID included in the available function message to the OTT server. The UE 100 receives the AI / ML model (model ID="a").

[0156] In step S21, the OTT server transmits the transfer model information of the AI / ML model transferred in step S20 to the network node 200. The transfer model information includes the model ID = "a" of the AI / ML model. The NW communication unit 240 of the network node 200 receives the transfer model information.

[0157] In step S22, the transmitter 120 of the UE 100 transmits a message regarding applicable functions (hereinafter, may be referred to as an "applicable function message") to the network node 200. The applicable function message is a message for notifying the UE 100 of functions that are applicable. The UE 100 may transmit the applicable function message to notify whether the AI / ML model received by model transfer is applicable or not. In the example shown in FIG. 15, with regard to the supported function information and the function conditions, all of them are transmitted, not just the differences, and therefore the difference information is all "0" (all transmitted).

[0158] In step S23, the OTT server transfers the model to the UE 100. The AI / ML model transmitted in step S23 is an AI / ML model (model ID = "b") different from that transmitted in step S20. The receiving unit 110 of the UE 100 receives the AI / ML model.

[0159] In step S24, the OTT server transmits the transfer model information (including model ID="b") of the AI / ML model transferred in step S23 to the network node 200. The receiving unit 220 of the network node 200 receives the transfer model information.

[0160] In step S25, the new function is enabled in the UE 100. This may be achieved in response to the AI / ML model received in the model transfer in step S23.

[0161] In step S26, transmitter 120 of UE 100 transmits an applicable capability message to network node 200. UE 100 wants to transmit a difference (=new capability) from the supported capability information included in the previously transmitted capability message, and so transmits an applicable capability message including difference information = "1" (difference present) in the supported capability information and the difference. The capability message that is the target of the difference information is the applicable capability message transmitted immediately before (step S22). The applicable capability message is also of the same type ("applicable capability") as the applicable capability message transmitted in step S26.

[0162] Steps S20 to S26 constitute the model transfer phase.

[0163] In step S30 (FIG. 16), the control unit 230 of the network node 200 decides to activate the AI / ML model #1.

[0164] In step S31, the transmitting unit 210 of the network node 200 transmits an activate instruction message to the UE 100. The activate instruction message is a message for instructing the UE 100 to activate. The activate instruction message includes the function ID (="A") of the AI / ML model #1 whose activation was determined in step S30. The receiving unit 110 of the UE 100 receives the activate instruction message. The control unit 130 of the UE 100 activates the AI / ML model #1 having the function ID=A that has been instructed to be activated.

[0165] In step S32, the transmitter 120 of the UE 100 transmits a message regarding the activated function (hereinafter, may be referred to as an "activate function message") to the network node 200. The activate function message is a message for notifying the function that has been activated in the UE 100. In the example of Fig. 16, the UE 100 wishes to transmit all of the supported function information and function conditions, and therefore transmits an activate function message in which the difference information of the supported function information and function conditions is "0" (transmit all), and which includes the supported function information and the function conditions.

[0166] In step S33, the control unit 230 of the network node 200 determines to activate the AI / ML model #2 (function ID="B").

[0167] In step S34, the transmitting unit 210 of the network node 200 transmits an activate instruction message including function ID="B" to the UE 100. The receiving unit 110 of the UE 100 receives the activate instruction message. The control unit 130 of the UE 100 activates the AI / ML model #2 having function ID="B".

[0168] In step S35, the transmitter 120 of the UE 100 transmits an activate function message to the network node 200. In the example of Fig. 16, function ID = "B" is a new function, different from the function previously acquired by the UE 100. Therefore, the UE 100 transmits an activate function message including difference information "1" (difference present) of the supported function information and the difference (= supported function information including the new function). Note that, since all function conditions are transmitted, the difference information of the function conditions is "0" (all transmitted).

[0169] Second Embodiment Next, a second embodiment will be described, focusing on the differences from the first embodiment.

[0170] In the first embodiment, the function message that is the subject of the difference information is either the function message that was sent immediately before or a function message with the same function. In the second embodiment, an example is described in which the function message that is the subject of the difference information is any message. That is, the function message that is the subject of the difference information may not be the one immediately before the function message to be sent, but may be a function message of a different type from the function message to be sent.

[0171] Therefore, in the second embodiment, a function message includes message identification information (message ID) for distinguishing it from other function messages, and a function message to be transmitted includes the message ID of a function message that is a reference source of difference information.

[0172] For example, if UE100 wants to send the difference between the corresponding function information contained in the supported function message and the activated function message, the activated function message should include the message ID of the supported function message and the difference information = "1" (difference exists).

[0173] As a result, for example, the UE 100 or the network device can refer to an arbitrary function message and transmit a difference between at least one of the supported function information and the function conditions included in the function message. Since the difference is transmitted, it is possible to improve the communication efficiency of information related to functions, as in the first embodiment.

[0174] (Example of Operation According to Second Embodiment) Next, an example of operation according to the second embodiment will be described.

[0175] FIG. 17 is a diagram illustrating an example of operation according to the second embodiment.

[0176] 17, in steps S40 to S42, a supported function message, a configured function message, and an available function message are transmitted. Each function message includes a message ID. The process is the same as in the first embodiment except that each function message includes a message ID.

[0177] In step S43, a change in the functional condition occurs in the UE 100. Step S43 may also be the same as step S13 in the first embodiment.

[0178] In step S44, transmitter 120 of UE 100 transmits a supported function message. The supported function message includes a difference from the supported function information and functional conditions included in the supported function message transmitted in step S40, and difference information "1" (difference present) for the supported function information and functional conditions. The supported function message includes message ID = "1" indicating that it is the supported function message (step S40) as the reference function message that is the target of the difference information.

[0179] The message ID is included in the function message. Specifically, the message ID may be included in the supported function information or in the meta information.

[0180] In the example of FIG. 17, the corresponding function information and the function conditions indicate the same referenced function message, but the corresponding function information and the function conditions may indicate different referenced function messages.

[0181] In step S45, the OTT server performs model transfer and transmits the AI / ML model with model ID="a" to the UE 100, and in step S46, transmits transfer model information to the network node 200.

[0182] In step S47, the transmitter 120 of the UE 100 transmits an applicable function message to the network node. Since the AI / ML model received in the model transfer (step S45) causes the supported function information to differ from the previous supported function information, the UE 100 transmits the difference from the supported function information transmitted in the available function message (step S42). In this case, the applicable function message includes the message ID of the available function message = "3", the difference from the supported function information transmitted in the available function message, and the difference information of the supported function information = "1" (difference present). Note that the difference information of the function conditions is set to "0" (transmit all), and all of the corresponding function conditions are transmitted.

[0183] In step S48, the transmitter 210 of the network node 200 decides to activate the AI / ML model #1. Then, in step S49, the transmitter 210 transmits an activate instruction message including the function ID="A" of the AI / ML model #1. In response to receiving the activate instruction message, the control unit 130 of the UE 100 activates the AI / ML model #1 having the function ID="A".

[0184] In step S50, transmitter 120 of UE 100 transmits an activate function message to network node 200. The activated function of AI / ML model #1 is a new function for UE 100. Therefore, the activate function message includes difference information of supported function information as "1" (difference exists), message ID = "4" (supported function message (step S44)), and the difference from the supported function information included in the supported function message. Note that all corresponding function conditions are transmitted with difference information = "0" (all different).

[0185] In step S51, in the UE 100, a part of the functional conditions is changed.

[0186] In step S52, transmitter 120 of UE 100 transmits an applicable function message to network node 200 in response to a change in some of the function conditions. In this case, the applicable function message includes difference information of supported function information = "1" (difference exists), message ID = "6" (the difference information applies to the activate function message (step S50)), and the difference from the supported function information included in the message. Furthermore, the applicable function message includes difference information of function conditions = "1" (difference exists), message ID = "6" (the difference information applies to the activate function message (step S50)), and the difference from the function conditions included in the message.

[0187] In the first and second embodiments, examples have been described in which the function message includes function support information and function conditions, but the function message does not necessarily include the function conditions. In this case, the function message includes supported function information, and the difference information is difference information regarding the supported function information.

[0188] In addition, in the first and second embodiments, the network node 200 has been described as an example of a network device, but a device other than the network node 200 (for example, an LMF (Location Management Function), an AMF, a UPF, etc.) may be used as the network device. For example, when an AMF is used as the network device, a NAS message is used between the UE 100 and the AMF.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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 and / 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: System on a Chip).

[0194] 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 / or 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 the described functions or that 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.

[0195] 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.

[0196] 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.

[0197] This application claims priority from Japanese Patent Application No. 2024-132043 (filed August 8, 2024), the entire contents of which are incorporated herein by reference.

[0198] (Additional Notes) The above can be summarized as in the additional notes, but the additional notes do not limit the embodiments.

[0199] (Supplementary Note 1) A communication control method in a mobile communication system, comprising a step in which a first communication device transmits difference information on at least one of supported function information and function conditions to a second communication device, wherein the supported function information indicates information on functions of an AI / ML model that can be supported by the first communication device, and the function conditions indicate conditions for the first communication device to support the functions of the AI / ML model.

[0200] (Supplementary Note 2) The communication control method according to Supplementary Note 1, wherein the transmitting step includes the steps of: the first communication device transmitting a first function message to the second communication device, the first function message including the supported function information and the function conditions; and the first communication device, after transmitting the first function message, transmitting a second function message to the second communication device, the second function message including the supported function information and the difference information for the function conditions included in the first function message.

[0201] (Supplementary Note 3) The communication control method described in Supplementary Note 1 or Supplementary Note 2, wherein the difference information is either that the corresponding function information and function conditions included in the second function message indicate differences from the corresponding function information and function conditions included in the first function message, respectively, or that the corresponding function information and function conditions included in the second function message indicate all corresponding corresponding function information and function conditions, respectively.

[0202] (Supplementary Note 4) The communication control method according to any one of Supplementary Note 1 to Supplementary Note 3, wherein the difference information is included in either function identification information or meta information included in the second function message.

[0203] (Supplementary Note 5) The communication control method according to any one of Supplementary Note 1 to Supplementary Note 4, wherein the second function message is a message transmitted from the first communication device immediately after the first function message.

[0204] (Supplementary Note 6) The communication control method according to any one of Supplementary Note 1 to Supplementary Note 5, wherein the first function message and the second function message are function messages of the same function type.

[0205] (Supplementary Note 7) The communication control method according to any one of Supplementary Note 1 to Supplementary Note 6, wherein the first function message and the second function message are function messages of different function types.

[0206] (Supplementary Note 8) The communication control method according to any one of Supplementary Notes 1 to 7, wherein the first function message and the second function message include message identification information for identifying the function messages, and the difference information includes the message identification information of the first function message.

[0207] (Supplementary Note 9) A user device in a mobile communication system, comprising: a transmitter that transmits difference information of at least one of supported function information and function conditions to a network device, wherein the supported function information indicates functions of an AI / ML model that can be supported by the user device, and the function conditions indicate conditions for the user device to support the functions of the AI / ML model.

[0208] 1: Mobile communication system 20: RAN 30: CN 100: UE 110: Receiving unit 120: Transmitting unit 130: Control unit 200: Network node 210: Transmitting unit 220: Receiving unit 230: Control unit 240: NW communication unit 300: CN device

Claims

A communication control method in a mobile communication system, comprising: The first communication device transmits difference information of at least one of supported function information and functional conditions to the second communication device; the supported function information indicates information on functions of an AI (Artificial Intelligence) / ML (Machine Learning) model that can be supported by the first communication device, The functional condition indicates a condition for the first communication device to support a function of the AI / ML model. Communication control method. 　 The transmitting step includes: The first communication device transmits a first function message including the supported function information and the function condition to the second communication device; and after the first communication device transmits the first function message, transmitting a second function message to the second communication device, the second function message including the corresponding function information included in the first function message and the difference information for the function condition. The communication control method according to claim 1. 　 The difference information is Whether the corresponding function information and the function conditions included in the second function message indicate differences from the corresponding function information and the function conditions included in the first function message, respectively; and The corresponding function information and the corresponding function conditions included in the second function message indicate all the corresponding corresponding function information and the corresponding function conditions, respectively. The communication control method according to claim 2. 　 The difference information is included in either function identification information or meta information included in the second function message. The communication control method according to claim 3. 　 The second capability message is a message transmitted from the first communication device immediately after the first capability message. The communication control method according to claim 2. 　 The first function message and the second function message are function messages having the same function type. The communication control method according to claim 2. 　 The first function message and the second function message are function messages having different function types. The communication control method according to claim 2. 　 the first function message and the second function message include message identification information that identifies the function message; The difference information includes the message identification information of the first function message. The communication control method according to claim 7. 　 A user equipment in a mobile communication system, a transmitting unit that transmits difference information of at least one of supported function information and functional conditions to the network device; The supported function information indicates functions of an AI / ML model that can be supported by the user device, The functional conditions indicate conditions for supporting the functions of the AI / ML model in the user device. User equipment.

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