Communication method and user equipment

Abstract

A communication method according to one aspect of the present invention is carried out in a mobile communication system. This communication method comprises a step in which user equipment transmits, to a first network device, function identification information for identifying the function of an AI / ML model. The function identification information is expressed by at least one of sub-use case information indicating a sub-use case of the AI / ML model, region information indicating a region usable by the AI / ML model, and additional condition information indicating an additional condition added by the network side to the use condition of the AI / ML model.

Description

Communication Method and User Equipment

[0001] The present disclosure relates to a communication method and a user equipment used in a mobile communication system.

[0002] In recent years, in the 3GPP (Third Generation Partnership Project) (registered trademark, the same hereinafter), which is a standardization project for mobile communication systems, studies have been conducted on applying artificial intelligence (AI) technology, particularly machine learning (ML) technology, to the wireless communication (air interface) of mobile communication systems.

[0003] 3GPP TR 38.843 V18.0.0 (2023 - 12) 3GPP TS 38.000 V18.3.0 (2024 - 09) 3GPP TS 28.105 V19.0.0 (2024 - 09)

[0004] The communication method according to the first aspect is a communication method in a mobile communication system. The communication method includes a step in which a user equipment transmits function identification information for identifying the function of an AI / ML model to a first network device. Here, the function identification information is represented by at least any one of sub - use case information indicating a sub - use case of the AI / ML model, area information indicating an area where the AI / ML model can be used, and additional condition information indicating additional conditions added by the network side for the usage conditions of the AI / ML model.

[0005] The user equipment according to the second aspect is a user equipment in a mobile communication system. The user equipment includes a transmission unit that transmits function identification information for identifying the function of an AI / ML model to a first network device. Here, the function identification information is represented by at least any one of sub - use case information indicating a sub - use case of the AI / ML model, area information indicating an area where the AI / ML model can be used, and additional condition information indicating additional conditions added by the network side for the usage conditions of the AI / ML model.

[0006] Figure 1 is a diagram showing an example configuration of a mobile communication system according to the first embodiment. Figure 2 is a diagram showing an example configuration of a UE (User Equipment) according to the first embodiment. Figure 3 is a diagram showing an example configuration of a network node (base station) according to the first embodiment. Figure 4 is a diagram showing an example configuration of a protocol stack according to the first embodiment. Figure 5 is a diagram showing an example configuration of a protocol stack according to the first embodiment. Figure 6 is a diagram showing an example configuration of a functional block of AI / ML technology according to the first embodiment. Figure 7(A) is a diagram showing an example configuration of a functional block of a mobile communication system according to the first embodiment, and Figure 7(B) is a diagram showing an example configuration of a functional block of a UE according to the first embodiment. Figure 8 is a diagram showing an example configuration of a functional block of a mobile communication system according to the first embodiment. Figures 9(A) and 9(B) are diagrams showing examples configuration of functional blocks of a mobile communication system according to the first embodiment. Figures 10(A) and 10(B) are diagrams showing examples configuration of functional blocks of a mobile communication system according to the first embodiment. Figure 11 is a diagram showing an example of a function ID according to the first embodiment. Figure 12 is a diagram showing an example of operation according to the first embodiment. Figure 13 is a diagram showing an example of operation according to the second embodiment. Figure 14 is a diagram showing an example of a mapping table according to the third embodiment. Figure 15 is a diagram showing an example of operation according to the third embodiment.

[0007] This disclosure aims to enable appropriate processing of AI / ML model function IDs in user devices.

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

[0009] [First Embodiment] The configuration of the mobile communication system according to the first embodiment will now be described. Figure 1 is a diagram showing an example of the configuration of the 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. In the following description, 5GS will be used as an example, but the mobile communication system may also have an LTE (Long Term Evolution) system applied to it at least partially. The mobile communication system may also have a 6th generation (6G) system or later system applied to it at least partially.

[0010] The mobile communication system 1 comprises a network (NW) 10 and a user device (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, such as 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 installed on a sensor, a vehicle or a device installed on a vehicle (Vehicle UE), or an aircraft or a device installed on an aircraft (Aerial UE).

[0011] NW10 includes a radio access network (RAN) 20 and a core network (CN) 30. When the mobile communication system is a fifth-generation system (5GS), RAN20 is referred to as NG-RAN (Next Generation Radio Access Network) and CN30 is referred to as 5GC (5G Core Network).

[0012] RAN20 includes a plurality of network nodes 200 (network nodes 200a to 200c in the example of Figure 1). The network nodes 200 are interconnected via inter-network node interfaces. In RAN20, network nodes 200 are sometimes referred to as base stations. When a network node 200 is a base station, it consists of a CU (Central Unit) and a DU (Distribution Unit) (i.e., functionally divided), and the two units may be connected by a front-haul interface. When the mobile communication system 1 is 5GS, the network nodes 200 are referred to as gNBs, the inter-network node interfaces as Xn interfaces, and the front-haul interfaces as F1 interfaces.

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

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

[0015] CN30 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 UE100. The C-plane device communicates with the UE100 using NAS (Non-Access Stratum) signaling. The U-plane device controls data transfer. When the mobile communication system is 5GS, the C-plane device is called AMF (Access and Mobility Management Function), the U-plane device is called UPF (User Plane Function), and the interface between the network node 200 and the CN device 300 is called the NG interface.

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

[0017] Figure 2 shows an example configuration of UE100 (user device) according to the first embodiment. UE100 comprises a receiving unit 110, a transmitting unit 120, and a control unit 130. The receiving unit 110 and the transmitting unit 120 constitute a communication unit 140 that performs wireless communication with the network node 200. UE100 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 the radio signal received by the antenna into a baseband signal (received signal) and outputs it to the control unit 130.

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

[0020] The control unit 130 performs various control and processing operations in the UE 100. Such processing includes processing in each layer described later. 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 processing by the processor. The processor may include a baseband processor and a CPU (Central Processing Unit). The baseband processor performs modulation, demodulation, encoding, and decoding of baseband signals. The CPU executes programs stored in memory and performs various processing operations. Note that processing or operations performed in the UE 100 may also be performed in the control unit 130.

[0021] Figure 3 shows an example configuration of a 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 an 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 types of transmissions under the control of the control unit 230. The transmitting unit 210 includes an antenna and a transmitter. The transmitter converts the baseband signal (transmission signal) output by the control unit 230 into a wireless 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 the radio signal received by the antenna into a baseband signal (received signal) and outputs it to the control unit 230.

[0024] The control unit 230 performs various control and processing operations on the network node 200. Such processing includes processing at each layer described later. 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 for processing by the processor. The processor may include a baseband processor and a CPU. The baseband processor performs modulation, demodulation, encoding, and decoding of baseband signals. The CPU executes programs stored in memory and performs various processing operations. Processing or operations performed at the network node 200 may also be performed by the control unit 230.

[0025] The NW communication unit 240 is connected to an adjacent network node via the Xn interface, which is an inter-network node interface. The NW communication unit 240 is connected to the CN device 300 via the NG interface, which is an inter-network node-core network interface. The network node 200 may consist of a central unit (CU) and a distributed unit (DU) (i.e., functionally divided), and the two units may be connected by the F1 interface, which is a front-haul interface.

[0026] Figure 4 shows an example of the protocol stack configuration for a user plane wireless interface that handles data.

[0027] The user plane radio interface protocol comprises 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 UE100 and the PHY layer of network node 200 via a physical channel. The PHY layer of UE100 receives downlink control information (DCI) transmitted from network node 200 on the physical downlink control channel (PDCCH). Specifically, UE100 performs blind decoding of the PDCCH using a Radio Network Temporary Identifier (RNTI) and acquires the successfully decoded DCI as the DCI addressed to its own UE. The DCI transmitted from network node 200 has a CRC (Cyclic Redundancy Code) parity bit, which is scrambled by the RNTI, added to it.

[0029] In NR, UE100 can use a bandwidth narrower than the system bandwidth (i.e., the cell bandwidth). Network node 200 configures UE100 with a bandwidth portion (BWP) consisting of consecutive PRBs (Physical Resource Blocks). UE100 sends and receives data and control signals in the active BWP. For example, up to four BWPs may be configured for UE100. Each BWP may have a different subcarrier spacing. The frequencies of these BWPs may overlap. If multiple BWPs are configured for UE100, network node 200 can specify which BWP to apply by controlling the downlink. This allows network node 200 to dynamically adjust the UE bandwidth according to the amount of data traffic on UE100, 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 a serving cell. A CORESET is a radio resource for control information that the UE 100 should receive. The UE 100 may have up to twelve or more CORESETs configured on a serving cell. Each CORESET may have an index from 0 to 11 or more. A CORESET may consist of six resource blocks (PRBs) and one, two, or three consecutive OFDM (Orthogonal Frequency Division Multiplex) symbols in the time domain.

[0031] The MAC layer performs data priority control, retransmission processing using Hybrid ARQ (HARQ: Hybrid Automatic Repeat reQuest), and random access procedures. Data and control information are transmitted between the MAC layer of UE100 and the MAC layer of network node 200 via the transport channel. The MAC layer of network node 200 includes a scheduler. The scheduler determines the transport format for the up and down links (transport block size, modulation and coding scheme (MCS)) and the resource blocks to be allocated to UE100.

[0032] The RLC layer transmits data to the receiving RLC layer using the functions of the MAC layer and PHY layer. Data and control information are transmitted between the RLC layer of UE100 and the RLC layer of network node 200 via a logical channel.

[0033] The PDCP layer performs header compression / decompression, encryption / decryption, etc.

[0034] The SDAP layer maps IP flows, which are the units under which the core network performs QoS (Quality of Service) control, to wireless bearers, which are the units under which the access layer (AS: Access Stratum) performs QoS control. Note that if the RAN is connected to the EPC, the SDAP is not required.

[0035] Figure 5 shows the configuration of the protocol stack of the wireless interface of the control plane that handles signaling (control signals).

[0036] The protocol stack of the control plane's radio interface includes a Radio Resource Control (RRC) layer and a Non-Access Stratum (NAS) layer, instead of the SDAP layer shown in Figure 4.

[0037] RRC signaling for various settings is transmitted between the RRC layer of UE100 and the RRC layer of network node 200. The RRC layer controls the logical channel, transport channel, and physical channel in response to the establishment, re-establishment, and release of the wireless bearer. If there is a connection (RRC connection) between the RRC of UE100 and the RRC of network node 200, UE100 is in the RRC connected state. If there is no connection (RRC connection) between the RRC of UE100 and the RRC of network node 200, UE100 is in the RRC idle state. If the connection between the RRC of UE100 and the RRC of network node 200 is suspended, UE100 is in the RRC inactive state.

[0038] The NAS, located above the RRC layer, handles session management and mobility management, among other things. NAS signaling is transmitted between the UE100's NAS and the AMF's NAS. In addition to the wireless interface protocol, the UE100 also has an application layer, etc. Furthermore, the layer below the NAS is called the AS (Access Stratum).

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

[0040] The block configuration example of the functions 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 recording unit (Model Storage) A6.

[0041] The block configuration example of the functions shown in FIG. 6 represents a functional framework of general AI / ML technology. Therefore, depending on virtual use cases, some parts of the block configuration example (such as the model recording unit A6) may not be included in the block configuration example. Also, the block configuration example shown in FIG. 6 may be distributed and arranged between the UE 100 and the network device. Alternatively, for some functions of the block configuration example (such as the model training unit A2 or the model inference unit A3), they may be arranged in both the UE 100 and the network device.

[0042] The data collection unit A1 provides input data to the model training unit A2, the model inference unit A3, and the management unit A5. The input data includes training data (Training Data) for the model training unit A2, inference data (Inference Data) for the model inference unit A3, and monitoring data (Monitoring Data) for the management unit A5.

[0043] The training data becomes the data required for input when the AI / ML model performs learning. Also, the inference data becomes the data required for input when the AI / ML model performs inference. Furthermore, the monitoring data becomes the data required for input during the management of the AI / ML model.

[0044] Note that data collection (Data collection) may be, for example, a process of collecting data in a network node, a management entity, or the UE 100 for performing learning of an AI / ML model, management of an AI / ML model, and inference of 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 training is a process of training an AI / ML model based on the relationship between inputs and outputs to obtain a trained AI / ML model for inference. For example, considering y = ax + b, the process of optimizing a (slope) and b (intercept) by providing inputs (x) and outputs (y) (i.e., providing training data) can be AI / ML model training.

[0047] Generally, machine learning includes supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a method that uses correct data for training data. Unsupervised learning is a method that does not use correct data for training data. For example, in unsupervised learning, feature points are memorized from a large amount of training data, and the correct judgment (estimation of the range) is made. Reinforcement learning is a method of learning a method that assigns a score to the output result and maximizes the score. Hereinafter, supervised learning will be described, but as machine learning, unsupervised learning or reinforcement learning may be applied.

[0048] The model learning unit A2 outputs the trained AI / ML model obtained by AI / ML model training to the model recording unit A6, and the model learning unit A2 also outputs the updated AI / ML model obtained by retraining the trained AI / ML model to the model recording unit A6.

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

[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 a trained AI / ML model (or an updated AI / ML model) to obtain inference output data. For example, considering 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. Here, there are various modeling methods (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, management instructions include selecting an AI / ML model, activating (deactivating) an AI / ML model, switching between AI / ML models, and fallback (performing inference without using an AI / ML model). The model inference unit A3 performs model inference according to the management instructions.

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

[0053] In the following text, an AI / ML model that is being trained (or updated) may be referred to as a "Training AI / ML model" (or "Updating AI / ML Model"). Also, in the following text, if there is no distinction between an AI / ML model being trained (or updated) and an AI / ML model that has been trained (or updated), it may simply be 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. Based on monitoring data and inference output data, the management unit A5 can also perform actions to ensure appropriate inference operation. Therefore, the management unit A5 outputs a 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 output to the Model Inference Unit A3. Furthermore, the management unit A5 outputs management instructions to the Model Inference Unit A3, supervising operations on the AI / ML model. Furthermore, the management unit A5 can output performance feedback and retraining requests to the model learning unit A2, which can then retrain the AI / ML model (i.e., update the trained AI / ML model).

[0055] (Use Cases) Next, we will explain use cases in which AI / ML technology is applied. For example, there are three use cases in which AI / ML technology is applied:

[0056] ・(X1.1) “CSI (Channel State Information) feedback enhancement”; ・(X1.2) “Beam management”; - (X1.3) "Positioning accuracy enhancement".

[0057] (X1.1) CSI Feedback Improvement "CSI feedback improvement" describes a use case where AI / ML technology is applied to the CSI that is fed back from UE100 to network node 200. CSI is information about the channel status in the downlink between UE100 and network node 200. CSI includes at least one of the following: Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), and Rank Indicator (RI). Network node 200 performs, for example, downlink scheduling based on the CSI feedback from UE100.

[0058] In the "CSI Feedback Improvement" use case, there are two sub-use cases: CSI compression in the frequency domain and CSI prediction in the time domain.

[0059] (X1.1.1) Sub-use case: CSI compression In CSI compression, the CSI inferred using the trained AI / ML model in UE100 is compressed in UE100. The compressed CSI is transmitted from UE100 to network node 200.

[0060] Figure 7(A) is a diagram showing an example of the configuration of a functional block in the mobile communication system 1 when CSI compression is used. As shown in Figure 7(A), the UE 100 has a CSI generation unit 101, and the network node 200 has a CSI reconstruction unit 201.

[0061] 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) from the input using a trained AI / ML model. The input to the CSI generation inference unit 1010 may be, for example, a partial (or punctured) CSI. The partial CSI may be a CSI measured using a CSI reference signal (CSI-RS) (or demodulation reference signal (DMRS)) transmitted using a certain amount of resources or less. Alternatively, the input to the CSI generation inference unit 1010 may be a CSI reference signal. When a partial CSI is input, the output of the CSI generation inference unit 1010 will be a CSI with a larger number of CSIs than the partial CSI. Furthermore, the output of the CSI generation inference unit 1010 becomes a CSI when a CSI reference signal is input. Hereinafter, the output of the CSI generation inference unit 1010 will be referred to as the full CSI. The quantization unit 1011 quantizes the full CSI. Compression is performed through quantization. The quantization unit 1011 transmits the quantized full CSI to the network node 200 as CSI feedback.

[0062] The CSI reconstruction unit 201 includes an inverse quantization unit 2010 and a CSI reconstruction inference unit 2011. The inverse quantization unit 2010 receives CSI feedback, inverse quantizes the quantized full CSI, and outputs the full CSI. The CSI reconstruction inference unit 2011 uses a trained AI / ML model to infer the reconstructed full CSI from the output of the inverse quantization unit 2010 (full CSI). The reconstructed full CSI is output from the CSI reconstruction unit 201.

[0063] Furthermore, the quantization unit 1011 may be merged with the CSI generation inference unit 1010, and the inverse quantization unit 2010 may also be merged with the CSI reconstruction inference unit 2011. In addition, a pre-processing unit may be provided before the CSI generation inference unit 1010, and a post-processing unit may be provided after the CSI reconstruction inference unit 2011.

[0064] As shown in Figure 7(A), the sub-use case of CSI compression is based on a two-sided model in which inference is performed using a trained AI / ML model on both the UE100 side and the network node 200 side.

[0065] (X.1.1.2) Sub-use case: CSI prediction In CSI prediction, a trained AI / ML model is used to infer (predict) future CSIs from the history of past CSIs.

[0066] Figure 7(B) shows an example of the configuration of a functional block in UE100 when CSI prediction is used. CSI prediction is based on the UE-side model in UE100, where inference is performed using a pre-trained AI / ML model.

[0067] As shown in Figure 7(B), UE100 has a CSI prediction model 102. The CSI prediction model 102 has a trained AI / ML model that takes past CSI history as input and infers future CSI (predicted CSI). The CSI prediction model 102 may also be a CSI prediction inference unit. UE100 (CSI prediction model 102) transmits the predicted CSI as CSI feedback to the network node 200.

[0068] Furthermore, the CSI prediction model 102 may have a pre-processing unit before it, or a post-processing unit after it.

[0069] (X1.2) "Beam Management" In beam management use cases, there are 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 temporal direction. Spatial-domain downlink beam prediction is called "BM Case 1" (BM-Case 1), and temporal downlink beam prediction is called "BM Case 2" (BM-Case 2).

[0070] Figure 8 shows an example of the configuration of a functional block in the mobile communication system 1 when BM case 1 is used. In the case of BM case 1, either a UE side model in which inference is performed at UE 100 or an NW side model in which inference is performed on the network side may be applied. Therefore, as shown in Figure 8, the AI / ML model 103 that performs inference may reside at UE 100 or at NW 10 (including the network node 200 or CN device 300).

[0071] In BM Case 1, the input to the AI / ML model 103 is the measured value for each beam included in beamset B. On the other hand, the output from the AI / ML model 103 (inference output data) is the probability that each (downstream) beam included in (predicted) beamset A will be the top beam. Beamset A and beamset B may be different. Alternatively, beamset B may be a subset of beamset A. The measured values ​​of beamset B, which are the input to the AI / ML model 103, may be represented by RSRP (Reference Signal Received Power).

[0072] Figure 8 also shows an example configuration 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 as well, the AI / ML model 103 is located in either UE 100 or NW 10.

[0073] In BM Case 2, the input to the AI / ML model 103 is the history of measurements for each beam included in beamset B. The previously measured measurements for each beam are input to the AI / ML model 103. On the other hand, the output from the AI / ML model 103 (inference output data) is the probability that each (downstream) beam included in (predicted) beamset A will be the top beam, similar to BM Case 1. Beamset A and beamset B may be different, and beamset B may be a subset of beamset A. Also, in BM Case 2, beamset A and beamset B may be the same.

[0074] In both BM Case 1 and BM Case 2, the UE side model allows UE100 to report prediction results to NW10. Also, in both BM Case 1 and BM Case 2, the NW side model can predict the top beam based on the measured values ​​for each beam included in beamset B reported by UE100.

[0075] (X1.3) "Positioning Accuracy Enhancement" In the use case for improving positioning accuracy, there are two sub-use cases: Direct AI / ML positioning, which directly infers the position of UE100 using a trained AI / ML model, and AI / ML assisted positioning, which infers intermediate position measurements. In the latter, AI / ML assisted positioning, the position of UE100 is measured or inferred in the LMF (Location Management Function) using intermediate position measurements. Intermediate position measurements can serve as assisting information for measuring or inferring the position of UE100 in the LMF. The LMF may have a pre-trained AI / ML model, and uses this pre-trained AI / ML model to infer the position of UE100.

[0076] (X1.3.1) Sub-use case: Direct AI / ML positioning Figure 9(A) is a diagram showing an example of the configuration of a functional block in the mobile communication system 1 when direct AI / ML positioning is used. In the case of direct AI / ML positioning, the UE side model and the network side model are applied. Therefore, the AI / ML model 104 used for inference may reside in the UE 100 or in the NW 10.

[0077] In the case of direct AI / ML positioning, the input to the AI / ML model 104 is the measured value at each measurement point (TRP: Transmission and / or Reception Point). The measured value can be, for example, a Channel Impulse Response (CIR), a Power Delay Profile (PDP), or a fingerprint. For example, both CIR and PDP represent the delay time for a signal at a specific frequency, but CIR represents the instantaneous delay time, while PDP represents the statistical delay time. On the other hand, the output from the AI / ML model 104 (inference output data) is the position information of the UE 100. The position information may also be represented by a fingerprint. The fingerprint represents, for example, the measurement information for the cell of the UE 100.

[0078] (X1.3.2) Sub-use case: AI / ML assisted positioning Figures 9(B) to 10(B) are diagrams showing examples 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, the UE side model and the network side model are also applied. Therefore, the AI / ML model 105 used for inference may reside in the UE 100 or in the NW 10. In the case of AI / ML assisted positioning, there are cases in which one AI / ML model 105 is used for multiple inputs (Figure 9(B)), cases in which the same AI / ML model is used for each of the multiple inputs (Figure 10(A)), and cases in which different AI / ML models are used for each of the multiple inputs (Figure 10(B)).

[0079] In either case, the input to the AI / ML model 105 is the channel measurement value at each measurement point (TRP). The channel measurement value may be CIR, PDP, or fingerprint, similar to direct AI / ML positioning. On the other hand, the output from the AI / ML model 105 (inference output data) is, in either case, an intermediate position measurement value used for position measurement. The intermediate position measurement value may be LOS or NLOS identification, measurement timing and / or measurement angle, or likelihood of measurement.

[0080] (LCM) Currently, 3GPP is discussing the Life Cycle Management (LCM) of AI / ML models.

[0081] The LCM of an AI / ML model may specifically include at least one of the following actions:

[0082] • (L1) Data collection; • (L2) Model training; • (L3) Identification; • (L4) Model delivery / transfer; • (L5) Model inference operation; • (L6) Selection, activation, deactivation, switching, and fallback operations; • (L7) Monitoring; • (L8) Model updating; • (L9) UE capability capability).

[0083] On the network side, for example, by controlling each operation of the LCM, it becomes possible to properly manage everything from the generation of AI / ML models to their deletion (or disposal). Fallback refers to 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."

[0084] 3GPP specifies two types of LCM: function-based LCM and model ID-based LCM.

[0085] A function-based LCM may be an operation performed on a function of an AI / ML model. Specifically, a function-based LCM may perform one of the following operations on a function of an AI / ML model: activation, deactivation, switching, or fallback. The network side can, for example, use 3GPP signaling (RRC messages, MAC CE, or DCI) to instruct the LCM operation on a function of an AI / ML model. In a function-based LCM, UE100 may have one AI / ML model for one function. UE100 may have multiple AI / ML models for a single function.

[0086] On the other hand, a model ID-based LCM may be an operation performed on individual AI / ML models using the model ID. The model ID is identification information used to distinguish one AI / ML model from other AI / ML models. Specifically, a model ID-based LCM may use the model ID to perform one of the following operations on each AI / ML model: activation, deactivation, switching, or selection.

[0087] (Communication method according to the first embodiment) Next, the communication method according to the first embodiment will be described.

[0088] As mentioned above, with regard to LCM operation for AI / ML models, there are cases where LCM operation is instructed or performed on a function-based (function unit) basis. For example, a network device may instruct an AI / ML model that has the function of "acquiring location information" to activate, or UE100 may perform inference on the AI / ML model that has the function of "acquiring location information" in accordance with that instruction.

[0089] Currently, 3GPP is discussing at what level the functions of AI / ML models should be identified, specifically, at what level the function identification information (function ID) should be represented. For example, it is possible to represent function IDs at the sub-use case level of the AI / ML model. In other words, it is possible to use function IDs to identify each sub-use case.

[0090] Therefore, the objective of the first embodiment is to enable the UE100 to appropriately process the function ID of the AI / ML model.

[0091] Therefore, in the first embodiment, the function ID of the AI / ML model is represented by at least one of the following: sub-use case information, domain information, and additional condition information.

[0092] Specifically, the user device (e.g., UE100) transmits function identification information that identifies the function of the AI / ML model to the first network device (e.g., network node200). Here, the function identification information is represented by at least one of the following: sub-use case information indicating a sub-use case of the AI / ML model, area information indicating the area in which the AI / ML model can be used, and additional condition information indicating additional conditions that the network side adds to the usage conditions of the AI / ML model.

[0093] This allows, for example, UE100 to identify use cases from function IDs, identify usable areas from function IDs, and identify additional conditions from function IDs, enabling appropriate processing using function IDs.

[0094] The specific information that will be used to represent (or what information will be included in) the function ID is as follows:

[0095] Firstly, the function ID may include sub-use case information for the AI / ML model. This allows, for example, the AI / ML model to be identified on a sub-use case basis for each function ID. Examples of sub-use cases include the following:

[0096] - CSI compression; - CSI prediction; - BM Case 1 (BM-Case 1); - BM Case 2 (BM-Case 2); - Direct AI / ML positioning; - AI / ML assisted positioning.

[0097] Sub-use case information may be represented as identification information that identifies each sub-use case. This identification information may be represented in bit representation. Alternatively, this identification information may be represented in byte representation, where 8 bits equal 1 byte. In the case of byte representation, each byte may be represented as a hexadecimal number from "0" to "F". In other words, the function ID is represented as identification information that identifies the sub-use case.

[0098] Secondly, the function ID may include domain information. This allows, for example, the domain in which the AI / ML model having that function ID is used to be identified from the function ID. Examples of such domains include the following:

[0099] PLMN (Public Land Mobile Network); RA (Registration Area); TA (Tracking Area); gNB; Physical cell; Location information (latitude and longitude, etc.).

[0100] Area information may be represented as identification information that identifies each area. This identification information may be represented in bit representation. Alternatively, this identification information may be represented in byte representation, where 8 bits equal 1 byte. In the case of byte representation, each byte may be represented as a hexadecimal number from "0" to "F". In other words, the function ID may be represented as identification information that identifies the area.

[0101] Thirdly, the function ID may include additional condition information. This allows, for example, the network to identify additional conditions that it adds to an AI / ML model having that function ID. Examples of additional conditions include the following:

[0102] - Conditions for Set A or Set B used in beam management for sub-use cases; - Temporal conditions (e.g., AI / ML model usable from 9:00 to 17:00); - Geographical conditions (e.g., AI / ML model usable within range X); - Travel speed (e.g., AI / ML model usable up to 5 km / h); Additional condition information may be represented as identification information to identify each additional condition. This identification information may be displayed in bit representation. Alternatively, this identification information may be displayed in byte representation, where 8 bits equal 1 byte. In the case of byte representation, each byte may be represented as a hexadecimal number from "0" to "F". The identification information to identify the additional conditions may also be an Associated ID. That is, the additional condition information may be represented by an Associated ID. In this case, the Function ID may be represented as the Associated ID.

[0103] The above examples illustrate cases where the function ID is represented by one of three pieces of information: sub-use case information, domain information, and additional condition information. The function ID may also be represented by a combination of these three pieces of information, including sub-use case information (or, the function ID may include a combination of these three pieces of information, including sub-use case information).

[0104] Here, we assume that the function ID is represented only by sub-use case information, that is, the functions of the AI / ML model are represented on a sub-use case basis. In the case of a sub-use case basis, the functions indicated by the function ID may include functions that can actually be used and functions that cannot be used. Furthermore, there will be usage conditions (or additional conditions) for the AI / ML model that possesses the function, and there may be a certain number of usage conditions or more.

[0105] Therefore, the function ID according to the first embodiment is represented by a combination of three pieces of information, including sub-use case information. This makes it possible to include more detailed information within the sub-use case, such as which areas the function ID can be used in and under what usage conditions it can be used. Consequently, the UE 100 can process the function ID appropriately. Specifically, the function ID in the case of a combination is as follows.

[0106] Firstly, the function ID can include a combination of sub-use case information and domain information. This allows, for example, the identification of sub-use cases for an AI / ML model having the function ID, as well as the identification of domains in which the AI / ML model can be used. In this case, the function ID will include a combination of identification information representing each sub-use case and identification information representing each domain.

[0107] Secondly, the function ID may include a combination of sub-use case information and additional condition information. This allows, for example, the identification of sub-use cases for an AI / ML model having the function ID, as well as the identification of additional conditions for that AI / ML model. In this case, the function ID will include a combination of identification information representing each sub-use case and identification information representing each additional condition.

[0108] Thirdly, the function ID can include a combination of sub-use case information, domain information, and additional condition information. This allows, for example, the identification of a combination of sub-use cases for an AI / ML model having the function ID, the domains in which the AI / ML model can be used, and additional conditions for the AI / ML model. In this case, the function ID is a combination of identification information representing each sub-use case, identification information representing each domain, and identification information representing each additional condition.

[0109] In this way, by representing the function ID with a combination that includes sub-use case information, it becomes possible to specify the usage conditions for each sub-use case. This reduces the number of usage conditions compared to when the function ID is represented solely by sub-use case information.

[0110] Here, let's consider the case where the function ID is represented by a combination of three pieces of information. In this case, the function ID may be represented by one piece of information, and the other two pieces of information may not be represented as the function ID. For example, the function ID may be represented by only the sub-use case information, and the domain information and additional condition information may not be included in the function ID. In this case, the information not included in the function ID may be represented by a don't care bit (here, any bit may be included, and multiple values ​​may be targeted). For example, if the function ID includes sub-use case information but does not include domain information and additional condition information, the function ID is represented as (sub-use case information, domain information, additional condition information) = (01, xx, xx) (each piece of information is represented by 2 bytes) = (01xxxx). Here, "xx" represents the don't care bit (or don't care byte). In this way, when any of the three pieces of information are used in the function ID, the information that is not used can be represented by the don't care bit.

[0111] Figure 11 is a diagram showing an example of a function ID according to the first embodiment.

[0112] In Figure 11, "major items," "medium items," and "minor items" are shown. "Major items" represent sub-use case information, "medium items" represent domain information, and "minor items" represent additional condition information.

[0113] Furthermore, Figure 11 shows an example where each piece of information (each item) included in the function ID is represented by two bytes, and each byte is represented in hexadecimal.

[0114] Furthermore, Figure 11 shows that there are two types of function IDs: "direct specification" and "DC (don't care) specification." "Direct specification" represents a function ID that does not have a don't care bit and includes all three pieces of information. On the other hand, "DC specification" represents a function ID that has a don't care bit. A function ID may include a bit (or byte) that distinguishes between "direct specification" and "DC specification." In the example shown in Figure 11, one bit is used for identification.

[0115] Figure 11 shows an example of "direct specification" where the function ID is (011011). It also shows an example of "DC specification" where the function ID is (030FFF). In the case of "DC specification," in each item (2-byte representation), the bits that are don't care bits are represented as "1," and the function ID is represented by the sum of these bits (decimal) and displayed in hexadecimal. In the "DC specification" example shown in Figure 11, "sub-item" = "FF" indicates that all 16 bits (= 2 bytes) of the sub-item are don't care bits. Also, "intermediate item" = "0F" indicates that of the 16 bits of the intermediate item, "00000000 11111111" = (the latter 8 bits are don't care bits). The fact that some bits in each item are don't care bits indicates that the specific information for each item represented by those bits (for example, RA and TAI for medium items, or temporal conditions for minor items) is not don't care (i.e., it is not specifically designated; any value is acceptable, and multiple values ​​may apply). Note that in the example shown in Figure 11, the entire function ID is shown as an example of "direct specification" or "DC specification," but "direct specification" or "DC specification" may also be specified for each item (large, medium, and minor items).

[0116] Furthermore, the functions of AI / ML models can be classified as follows:

[0117] Supported functions: Supported functions are those that UE100 can indicate using UE Capability Information messages. Functions of the AI / ML model that UE100 can report to network devices using UE Capability Information messages can be considered supported functions.

[0118] Applicable functions: Applicable functions are those that enable UE100 to perform inference using AI / ML models. A function that allows UE100 to perform AI / ML model inference upon instruction from a network device is considered an applicable function.

[0119] Activated functions: Activated functions are those in UE100 that perform inference using AI / ML models. In UE100, any function of the AI / ML model in which inference is performed can be considered an activated function.

[0120] In the first embodiment, the UE100 side model is used as an example. Furthermore, in the first embodiment, the above classification of functions is used.

[0121] (Example of operation according to the first embodiment) Next, an example of operation according to the first embodiment will be described.

[0122] Figure 12 is a diagram illustrating an example of operation according to the first embodiment. In Figure 12, an example of a network node 200 is shown as a network device.

[0123] In the following explanation, messages or information are transmitted from the network node 200 to the UE 100, and these may be transmitted using RRC messages, MAC CE, or DCI (Downlink Control Information). Alternatively, they may be transmitted using AI / ML layer messages newly introduced for AI / ML models. Similarly, messages or information are transmitted from the UE 100 to the network node 200, and these may also be transmitted using RRC messages, MAC CE, UCI (Uplink Control Information), or new AI / ML layer messages. In all cases, the explanation will be omitted below.

[0124] As shown in Figure 12, in step S10, the NW communication unit 240 of the network node 200 receives a trained AI / ML model (hereinafter sometimes referred to as "AI / ML model") from the OTT (Over The Top) server. The NW communication unit 240 may receive multiple AI / ML models. The NW communication unit 240 may also receive the function ID of the AI / ML model. The NW communication unit 240 may also receive usage conditions from the OTT server indicating the conditions for using the AI / ML model. The usage conditions received from the OTT server may be represented by identification information that identifies the usage conditions. The NW communication unit 240 may also receive additional conditions for the AI / ML model from the OTT server. The additional conditions may be represented by an associated ID.

[0125] In step S11, the transmitting unit 210 of the network node 200 (for example, the first network node) forwards the received AI / ML model to the UE 100. The transmitting unit 210 may also forward at least one of the function ID, usage conditions, and additional conditions to the UE 100. The receiving unit 110 of the UE 100 receives the AI / ML model.

[0126] In step S12, the UE100 holds multiple AI / ML models by transferring the AI / ML models (step S11).

[0127] In step S13, the transmission unit 210 of the network node 200 transmits a UE Capability Query message. The UE Capability Query message may be a message to inquire about the capabilities of the AI / ML model held by the UE. The UE Capability Query message may include information to inquire whether the functionality of the AI / ML model held by the UE 100 is applicable.

[0128] In the first embodiment, the UE capability query message includes granularity specification information indicating the granularity of the function ID. The granularity of the function ID is information indicating at what level the function ID is specified. Specifically, at least one of sub-use case information (major item), domain information (medium item), and additional condition information (minor item) (or a combination of sub-use case information, domain information, and additional condition information, including sub-use case information) is specified. The granularity specification information is configuration information that the network device sets what information to include as the function ID. The UE 100 will use the function ID represented according to the configuration information. If granularity specification information is not included, the UE 100 may represent the function ID using any item (or a combination of each item including the major item). The UE capability query message may also include (configuration) information indicating which of the major, medium, and minor items included in the function ID will be the don't care bit. The receiving unit 110 of the UE 100 receives the UE capability query message.

[0129] In step S14, the transmission unit 120 of UE 100 transmits a UE Capability Information message to the network node 200. The UE Capability Information message is a response message to a UE Capability Inquiry message. The UE Capability Information message includes Applicable functions for the AI / ML model it holds. Supported functions may be included in the UE Capability Information message instead of applicable functions. Applicable functions are indicated by function IDs. Function IDs may be represented by items specified (or set) by granularity specification information (step S13). Function IDs may also be represented by items specified (or set) by the network node 200 as Don't Care bits. The control unit 130 of UE 100 may set items included in the function ID and items to be marked as "don't care" according to the specifications (or settings) from the network node 200. The receiving unit 220 of the network node 200 receives the UE capability information message.

[0130] In step S15, the transmitting unit 210 of the network node 200 sends an AI / ML configuration message to the UE 100. The AI / ML configuration message may be a message instructing LCM operation for the function of the AI / ML model. For example, the transmitting unit 210 can specify a function ID in the AI / ML configuration message and instruct the UE 100 to activate the AI / ML model having that function ID. The function ID may also be represented by a specified item (step S13). The AI / ML configuration message may also be an RRC reconfiguration message. The receiving unit 110 of the UE 100 receives the AI / ML configuration message.

[0131] In step S16, the control unit 130 of the UE 100, upon receiving the AI / ML setting message, starts inference of an AI / ML model having the function specified by the function ID.

[0132] In step S17, the transmission unit 120 of the UE 100 transmits an AI / ML Configuration Complete message. The AI / ML Configuration Complete message is a response message to the AI / ML Configuration message (step S15). The AI / ML Configuration Complete message includes the response result to the activation instructed in step S15. In the example in Figure 12, the response result may include an indication that inference of the AI / ML model for the function ID is being performed.

[0133] [Second Embodiment] Next, a second embodiment will be described. The second embodiment will be described focusing on the differences from the first embodiment.

[0134] For example, the configuration of the function ID described in the first embodiment can also be used between network nodes 200. Specifically, if UE 100 changes a network node 200 (or the cell it houses) by performing a handover or an RRC re-selection procedure, the function ID may be exchanged between network nodes 200. The second embodiment describes an example in which the function ID is exchanged between network nodes.

[0135] Specifically, firstly, a first network node (e.g., network node 200-1) receives function identification information (e.g., function ID) from a user device (e.g., UE100). Secondly, when the user device is executing a handover procedure or a cell reselection procedure, the first network node transmits the function identification information to a second network node (e.g., network node 200-2).

[0136] As a result, for example, network node 200-2 can receive data from network node 200-1 while executing a handover procedure or a cell reselection procedure. Therefore, it is no longer necessary to obtain the function ID from UE100 after the procedure is completed, and the function ID can be obtained efficiently. The configuration of the function ID is the same as in the first embodiment.

[0137] (Example of operation according to the second embodiment) Next, an example of operation according to the second embodiment will be described.

[0138] Figure 13 is a diagram illustrating an example of operation according to the second embodiment. In Figure 13, network nodes 200-1 and 200-2 are used as examples of network devices.

[0139] In Figure 13, steps S20 to S26, excluding step S22, are the same as steps S10 to S17 in the first embodiment (Figure 12).

[0140] In step S22, the receiving unit 220 of network node 200-2 receives model information from the OTT server. The model information represents information about the AI / ML model transmitted by the OTT server in step S20. The model information may include the function ID of the AI / ML model. The model information may include the usage conditions of the AI / ML model. Alternatively, the model information may include additional conditions for the AI / ML model. The additional conditions may be represented by an associated ID. The model information may be transmitted to network node 200-1 in step S20, or to UE 100 in step S21.

[0141] In step S27, the transmitter 120 of UE100 sends a Measurement Report message to network node 200-1 in response to triggering an event indicated by the Measurement Configuration (ReportConfigNR). The Measurement Report message may serve as a trigger message for starting the handover procedure. The receiver 220 of network node 200-1 receives the Measurement Report message.

[0142] In step S28, the NW communication unit 240 of network node 200-1, upon receiving the measurement report message (step S27), sends a Handover Request message to network node 200-2 which houses the adjacent cell. The Handover Request message is an example of an Xn message. The Handover Request message may include the UE-ID of the UE 100 that sent the measurement report message, and status information regarding the status of the AI / ML model's functionality held by the UE 100. This status information may be the response result included in the AI / ML setup completion message (step S26). This response result may include the function ID of the target AI / ML model. Alternatively, this status information may include the function ID of the applicable AI / ML model's functionality included in the UE capability information message (step S24). In the example in Figure 13, the status information for function #1 (=function ID) is "function #1 = OK", indicating that inference is in progress (or activation is in progress). The control unit 230 of network node 200-1 may set a don't care bit for the function ID. Specifically, the control unit 230 of network node 200-1 may use the function ID received from UE 100 (step S26) as is. The control unit 230 may also set an unspecified item as a don't care bit by confirming that there is an unspecified item in the function ID.

[0143] In step S29, the control unit 230 of network node 200-2 decides to perform a handover in response to receiving a handover request message (step S28). Then, the transmission unit 210 of network node 200-2 sends a Handover Request Acknowledge message to network node 200-1.

[0144] In step S30, the transmitting unit 210 of the network node 200-1 sends a handover message (RRC Reconfiguration message) to the UE 100 in response to receiving a handover request permission message (step S29). The receiving unit 110 of the UE 100 receives the handover message.

[0145] In step S31, the transmitting unit 120 of UE100 sends a handover completion message (RRC Reconfiguration Complete) to network node 200-2 and starts the connection to network node 200-2 (step S31).

[0146] (Another example of operation according to the second embodiment) In the second embodiment, the handover procedure was used as an example for explanation, but in the case of a cell reselection procedure, for example, it would be as follows.

[0147] In other words, upon receiving the AI / ML configuration completion message (step S26) from UE100, network node 200-1 sends an Xn message containing the UE-ID and status information to the adjacent network node (for example, network node 200-2). As a result, if the cell reselection procedure is performed in UE100 and the adjacent network node (cell) becomes the target network node (cell) for cell reselection, the adjacent network node can obtain the function ID from the status information, since it has already received the status information.

[0148] (Another example of operation according to the second embodiment) In the second embodiment, network node 200-1 and network node 200-2 were used as examples for the description, but for example, network node 200-1 may be replaced with CU#1, and network node 200-2 may be replaced with CU#2.

[0149] [Third Embodiment] Next, a third embodiment will be described. In the third embodiment, the differences from the first and second embodiments will be the main focus of the description.

[0150] Regarding the function ID described in the first embodiment, there are also cases where it is a combination that includes all three items. In this case, the function ID may be represented by 6 bytes (2 bytes for each item) = 48 bits.

[0151] Therefore, in the third embodiment, a local value is introduced for the function ID. The local value is represented by fewer bits than the function ID described in the first embodiment. The local value is used between the network node 200 and the UE 100. Since the function ID using the local value has fewer bits than the function ID described in the first embodiment, the UE 100 can improve the efficiency of processing.

[0152] In the following, the function ID described in the first embodiment may be referred to as the "global value."

[0153] Figure 14 is a diagram showing an example of a mapping table according to the third embodiment. The mapping table shows the correspondence (mapping) between local values ​​and global values ​​for a function ID. However, in the example shown in Figure 14, the local and global values ​​are displayed in byte form, and each byte is represented in hexadecimal form.

[0154] As shown in Figure 14, the mapping table includes "Specification Type". "Specification Type" can be "Direct Specification", "Range Specification", or "DC Specification".

[0155] "Direct specification" represents a relationship where a local value directly corresponds to a global value. In the example in Figure 14, one local value "0001" corresponds to one global value "0101FF". Also in the example in Figure 14, one local value "0002" corresponds to three global values ​​("010101", "010102", and "010103"). Thus, the correspondence between global values ​​and local values ​​is not limited to a one-to-one relationship, but can also be a one-to-many relationship. In a one-to-many relationship, when converting from a local value to a global value, any of the multiple global values ​​may be used.

[0156] "Range specification" represents a case where one local value corresponds to a global value within a specified range. In Figure 14, the global values ​​from "010100" to "0101FF" correspond to the local value "0003". "Range specification" also represents a one-to-many relationship.

[0157] "DC designation" represents the correspondence when a global value includes a don't care bit. In the example in Figure 14, the local value corresponding to the global value "01010F" (with a sub-item being a don't care bit) is "0004". In the case of "DC designation," not only one-to-one relationships but also one-to-many relationships may exist.

[0158] Such a mapping table may be generated by a network device and sent to UE100. UE100 can then use this mapping table to obtain function IDs obtained by converting global values ​​to local values.

[0159] (Example of operation according to the third embodiment) Next, an example of operation according to the third embodiment will be described.

[0160] Figure 15 is a diagram illustrating an example of operation according to the third embodiment. Figure 15 also illustrates an example of operation using a network node 200 as the network device.

[0161] In steps S40 and S41, the AI / ML model is transmitted from the OTT server to the UE100. Steps S40 and S41 may be the same processes as steps S10 and S11 of the first embodiment. However, the function ID used in steps S40 and S41 may be a global value.

[0162] In step S42, the control unit 230 of the network node 200 creates a mapping table (mapping information). The control unit 230 may create the mapping table based on the model information of the AI / ML model previously received from the OTT server.

[0163] In step S43, the transmission unit 210 of the network node 200 sends a UE capability query message to the UE 100. The UE capability query message includes the mapping table created in step S42. The UE capability query message also includes type information representing the type (global value or local value) of the function ID to be reported in response to the UE capability query message. In the example shown in Figure 15, "local" is specified as the type information, so the UE 100 will indicate the function ID as a local value. If the type information is "global", the UE 100 will indicate the function ID as a global value. The UE capability query message may also include granularity specification information, as in the first embodiment.

[0164] In step S44, the transmitting unit 120 of UE 100 transmits a UE capability information message to the network node 200 in response to receiving a UE capability inquiry message. The UE capability information message includes a function ID indicating the applicable functions of the AI / ML model applicable to UE 100. The UE capability information message also includes type information for the function ID (in the example in Figure 15, "local"). This type information is the same as the type specified in the UE capability inquiry message. However, if type information is not specified in the UE capability inquiry message (step S43), UE 100 may represent the function ID with any type. The receiving unit 220 of the network node 200 receives the UE capability information message.

[0165] In step S45, the transmission unit 210 of the network node 200 sends an AI / ML configuration information message to the UE 100. In the example in Figure 15, the AI / ML configuration information message specifies the function ID of the function of the AI / ML model to be activated. Since a local value was specified by the UE capability inquiry message (step S43), the function ID of the function of the AI / ML model to be activated may also be represented by a local value.

[0166] In step S46, the control unit 130 of the UE 100, upon receiving the AI / ML setting message, starts inference of an AI / ML model having the function indicated by the function ID. The control unit 130 may use a mapping table to convert a local function ID to a global function ID and then start inference of an AI / ML model having that function ID.

[0167] In step S47, the transmission unit 120 of UE 100 sends an AI / ML configuration completion message to the network node 200, which includes status information of the function of the AI / ML model to be activated. If the AI / ML configuration completion message includes the function ID of the target function, the function ID may be represented by a local value.

[0168] [Other Embodiments] In the first to third embodiments, a network node 200 was described as an example of a network device. For example, the network device may be a CN device 300. If the CN device is an AMF, the network node 200 may be replaced with the AMF in Figures 12, 13, and 15. In this case, NAS messages may be used for messages between the AMF and the UE 100.

[0169] Alternatively, the network device may be an LMF. In this case, the network node 200 in Figures 12, 13, and 15 may be replaced with an LMF. LPP messages using LPP (LTE Positioning Protocol) may be used between the LMF and the UE 100.

[0170] Alternatively, the network device may be an OTT server. In this case, the network node 200 in Figures 12, 13, and 15 may be replaced with an OTT server. Messages between the OTT server and the UE 100 may use IP messages via the IP (Internet Protocol) protocol.

[0171] Furthermore, although the first to third embodiments described function-based examples, the model-based approach is also possible. In this case, the "applicable function" in the first to third embodiments can be replaced with "applicable model." Instead of processing being performed on a function ID basis in the UE 100 and network nodes 200 (200-1 and 200-2), processing will be performed on a model ID basis.

[0172] The above-described operation flows can be performed not only independently, but also in combination of two or more operation flows. For example, some steps of one operation flow may be added to another operation flow, or some steps of one operation flow may be replaced with some steps of another operation flow. It is not necessary to execute all steps in each flow; only some steps may be executed. Furthermore, the order of steps in each flow may be changed as appropriate.

[0173] In the embodiments and examples described above, an example in which the base station is an NR base station (gNB) was described, but the base station may also be an LTE base station (eNB) or a 6G base station. Furthermore, 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 an IAB node. Furthermore, UE100 may be an MT (Mobile Termination) of an IAB node. That is, UE100 may be a terminal function unit (a type of communication module) for the base station to control a relay device that performs signal relay. Such a terminal function unit is referred to as an MT. Examples of multi-transmission architectures (MTs) include IAB-MT, NCR (Network Controlled Repeater)-MT, and RIS (Reconfigurable Intelligent Surface)-MT.

[0174] Furthermore, 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). Additionally, a network node may consist of a combination of at least a part of the core network device and at least a part of a base station.

[0175] A program may be provided that causes a computer to execute each process performed by the UE 100 or the network node 200. The program may be recorded on a computer-readable medium. Using a computer-readable medium, it is possible to install the program on a computer. Here, the computer-readable medium on which the program is recorded may be a non-transient recording medium. The non-transient recording medium is not particularly limited, but may be a recording medium such as a CD-ROM and / or DVD-ROM. Alternatively, the circuits that execute each process performed by the UE 100 or the network node 200 may be integrated, and at least a part of the UE 100 or the network node 200 may be configured as a semiconductor integrated circuit (chipset, SoC: System on a chip).

[0176] The functions realized by the above-described communication device (UE100 or network node 200, etc.) may be implemented in a circuit or processing circuit, including a general-purpose processor, application-specific processor, integrated circuit, ASICs (Application Specific Integrated Circuits), CPU (a Central Processing Unit), conventional circuitry, and / or a combination thereof, programmed to realize the described functions. The processor includes transistors and / or other circuits and is considered a circuit or processing circuit. The processor may also be a programmed processor that executes a program stored in memory. In this specification, circuit, unit, and means are hardware programmed to perform or execute the functions described herein. Such hardware may be any hardware disclosed herein, or any hardware known to be programmed to perform or execute the functions described herein. If such hardware is a processor that is considered to be of the type of circuit, such circuit, means, or unit is a combination of hardware and software used to constitute such hardware and / or processor.

[0177] The phrases “based on” and “depending on / in response to” as used in this disclosure do not mean “based solely on” or “in response solely” unless otherwise specified. “Based on” means both “based solely on” and “at least partially on.” Similarly, “depending” means both “at least partially on” and “at least partially on.” The terms “include,” “comprise,” and variations thereof do not mean that they include only the listed items, but may include only the listed items or may include additional items in addition to the listed items. Furthermore, the term “or” as used in this disclosure is not intended to mean exclusive OR. Additionally, 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 way to distinguish between two or more elements. Therefore, references to the first and second elements do not imply that only two elements may be adopted therein, or that the first element must precede the second element in any way. In this disclosure, where articles are added by translation, such as a, an, and the in English, these articles shall be plural unless it is clearly indicated from the context that they are not.

[0178] Although the embodiments have been described in detail above with reference to the drawings, the specific configuration is not limited to those described above, and various design changes can be made without departing from the gist of the invention.

[0179] This application claims priority to Japanese Patent Application No. 2024-195265 (filed November 7, 2024), the entirety of which is incorporated into the specification of this application.

[0180] (Note) The above embodiments can be summarized as shown in the note, but the note does not limit the embodiments.

[0181] (Note 1) A communication method in a mobile communication system, comprising the step of a user device transmitting function identification information that identifies the function of an AI / ML model to a first network device, wherein the function identification information is represented by at least one of sub-use case information indicating a sub-use case of the AI / ML model, area information indicating an area in which the AI / ML model can be used, and additional condition information indicating additional conditions that the network side adds to the usage conditions of the AI / ML model.

[0182] (Note 2) The communication method described in Note 1, wherein the function identification information is represented by a combination of the sub-use case information, the area information and the additional condition information, including the sub-use case information.

[0183] (Note 3) The communication method according to Note 1 or Note 2, further comprising the step of the user device receiving from the network device setting information indicating which of the sub-use case information, the area information and the additional condition information the function identification information is represented by, and the transmission step comprising the user device transmitting the function identification information represented according to the setting information.

[0184] (Note 4) The communication method described in any one of Notes 1 to 3, wherein the function identification information includes information in which the sub-use case information, the area information, and the additional condition information are each represented in bit representation.

[0185] (Note 5) The communication method described in any of Notes 1 to 4, wherein any of the sub-use case information, the area information, and the additional condition information that were not included in the function identification information are represented as a don't care bit.

[0186] (Note 6) The communication method according to any one of Notes 1 to 5, further comprising the steps of: a first network node, which is the first network device, receiving the function identification information from the user device; and the first network node transmitting the function identification information to a second network node when the user device is executing a handover procedure or a cell reselection procedure.

[0187] (Note 7) The communication method according to any one of Notes 1 to 6, further comprising the steps of: the user device receiving from the first network device a mapping table in which the function identification information, in which the sub-use case information, the area information, and the additional condition information are each represented in bit representation, is converted into a local value with a small number of bits, and the transmission step includes the user device transmitting the local value as the function identification information to the first network device based on the mapping table.

[0188] (Note 8) A user device in a mobile communication system, comprising a transmitting unit that transmits function identification information identifying the functions of an AI / ML model to a first network device, wherein the function identification information is represented by at least one of sub-use case information indicating sub-use cases of the AI / ML model, area information indicating areas in which the AI / ML model can be used, and additional condition information indicating additional conditions that the network side adds to the usage conditions of the AI / ML model.

[0189] 1: Mobile communication system 20: RAN 30: CN 100: UE 101: CSI generation unit 102: CSI prediction model 103, 104, 105: AI / ML model 110: Receiving unit 120: Transmitting unit 130: Control unit 200: Network node 201: CSI reconstruction unit 210: Transmitting unit 220: Receiving unit 230: Control unit 240: NW communication unit 300: CN device A1: Data acquisition unit A2: Model learning unit A3: Model inference unit A5: Management unit A6: Model recording unit

Claims

1. A communication method in a mobile communication system, comprising: a user device transmitting function identification information to a first network device that identifies the function of an AI / ML (Artificial Intelligence / Machine Learning) model, wherein the function identification information is represented by at least one of: sub-use case information indicating a sub-use case of the AI / ML model; area information indicating an area in which the AI / ML model can be used; and additional condition information indicating additional conditions that the network side adds to the usage conditions of the AI / ML model.

2. The communication method according to claim 1, wherein the function identification information is represented by a combination of the sub-use case information, the area information, and the additional condition information, including the sub-use case information.

3. The communication method according to claim 2, further comprising: the user device receiving from the network device configuration information indicating which of the sub-use case information, the area information, and the additional condition information the function identification information is represented by; and the transmission includes the user device transmitting the function identification information represented according to the configuration information.

4. The communication method according to claim 2, wherein the function identification information includes information in which the sub-use case information, the area information, and the additional condition information are each represented in bit representation.

5. The communication method according to claim 4, wherein any of the sub-use case information, the area information, and the additional condition information that is not included in the function identification information is represented as a don't care bit.

6. The communication method according to claim 1, further comprising: a first network node, which is the first network device, receiving the function identification information from the user device; and the first network node transmitting the function identification information to a second network node when the user device is executing a handover procedure or a cell reselection procedure.

7. The communication method according to claim 1, further comprising: the user device receiving from the first network device a mapping table in which the function identification information, in which the sub-use case information, the area information, and the additional condition information are each represented in bit representation, is converted into a local value with a small number of bits, wherein the transmission includes the user device transmitting the local value as the function identification information to the first network device based on the mapping table.

8. User device in a mobile communication system, comprising a transmitting unit that transmits function identification information identifying the functions of an AI / ML (Artificial Intelligence / Machine Learning) model to a first network device, wherein the function identification information is represented by at least one of the following: sub-use case information indicating a sub-use case of the AI / ML model; area information indicating an area in which the AI / ML model can be used; and additional condition information indicating additional conditions that the network side adds to the usage conditions of the AI / ML model.

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