Communication method and user device

Abstract

The present invention provides a communication method and a reader device capable of appropriately processing the function ID of an AI / ML model in a user device. [Solution] One aspect of the communication method is a communication method in a mobile communication system. The communication method includes a step in which a user device transmits to a network node, along with the function identification information, designation information indicating whether to use function identification information that directly represents the functions of an AI / ML model that are not applicable, or to use function identification information that indirectly represents the functions of an AI / ML model that are not applicable by using function identification information of an applicable AI / ML model.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Non-Patent Documents

[0003]

Non-Patent Document 1

Non-Patent Document 2

Non-Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present disclosure is to provide a communication method and a user device capable of appropriately transmitting a function ID of a function that is not applicable in a user device.

Means for Solving the Problems

[0005] The communication method according to the first embodiment is a communication method in a mobile communication system. The communication method includes a step in which a user device transmits to a network node, along with the function identification information, designation information indicating whether to use function identification information that directly represents the functions of an AI / ML model that are not applicable, or to use function identification information that indirectly represents the functions of an AI / ML model that are not applicable by using function identification information of an applicable AI / ML model.

[0006] The user device according to the second embodiment is a user device in a mobile communication system. The user device has a transmitting unit that transmits to a network node, along with the function identification information, designation information indicating whether the function identification information representing the functions of an AI / ML model is function identification information that directly represents the functions of an AI / ML model that are not applicable, or function identification information that indirectly represents the functions of an AI / ML model that are not applicable by representing the functions of an applicable AI / ML model. [Effects of the Invention]

[0007] According to this disclosure, a communication method and user device can be provided that enable the user device to appropriately transmit the function ID of a function that is not applicable. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a diagram showing an example configuration of a mobile communication system according to the first embodiment. [Figure 2] Figure 2 is a diagram showing an example configuration of a UE (User Equipment) according to the first embodiment. [Figure 3] Figure 3 is a diagram showing an example of the configuration of a network node (base station) according to the first embodiment. [Figure 4] Figure 4 is a diagram showing an example of the configuration of a protocol stack according to the first embodiment. [Figure 5]Figure 5 is a diagram showing an example of the configuration of a protocol stack according to the first embodiment. [Figure 6] Figure 6 is a diagram showing an example of the configuration of a functional block of the AI / ML technology according to the first embodiment. [Figure 7] Figure 7(A) shows an example of the configuration of a functional block in a mobile communication system according to the first embodiment, and Figure 7(B) shows an example of the configuration of a functional block in a UE according to the first embodiment. [Figure 8] Figure 8 is a diagram showing an example of the configuration of a functional block in a mobile communication system according to the first embodiment. [Figure 9] Figures 9(A) and 9(B) are diagrams showing examples of the configuration of a functional block in a mobile communication system according to the first embodiment. [Figure 10] Figures 10(A) and 10(B) are diagrams showing examples of the configuration of a functional block of a mobile communication system according to the first embodiment. [Figure 11] Figure 11 is a diagram showing a reported example according to the first embodiment. [Figure 12] Figure 12 is a diagram illustrating an example of operation according to the first embodiment. [Figure 13] Figure 13 is a diagram illustrating an example of operation according to the first embodiment. [Figure 14] Figure 14 is a diagram illustrating an example of operation according to the first embodiment. [Modes for carrying out the invention]

[0009] 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.

[0010] [First Embodiment] The configuration of the mobile communication system according to the first embodiment will be described. FIG. 1 is a diagram showing a configuration example of a mobile communication system 1 according to the first embodiment. The mobile communication system 1 complies with the 5th generation system (5GS: 5th Generation System) of the 3GPP standard. In the following, 5GS will be taken as an example for description. However, in the mobile communication system, the LTE (Long Term Evolution) system may be at least partially applied, or a system after the 6th generation (6G) system may be at least partially applied.

[0011] The mobile communication system 1 includes a network (NW) 10 and a user equipment (UE) 100. The UE 100 is a movable communication device that performs wireless communication with the NW 10. The UE 100 may be any device used by a user, for example, a mobile phone terminal (including a smartphone), a tablet terminal, a notebook PC (Personal Computer), a communication module (including a communication card or a chipset), a sensor or a device provided on the sensor, a vehicle or a device provided on the vehicle (Vehicle UE), an aircraft or a device provided on the aircraft (Aerial UE).

[0012] The NW 10 includes a radio access network (RAN) 20 and a core network (CN) 30. When the mobile communication system is the 5th generation system (5GS: 5th Generation System), the RAN 20 is called NG-RAN (Next Generation Radio Access Network), and the CN 30 is called 5GC (5G Core Network).

[0013] RAN 20 includes a plurality of network nodes 200 (in the example of FIG. 1, network nodes 200a to 200c). The network nodes 200 are interconnected via an interface between network nodes. 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 is composed of a CU (Central Unit) and a DU (Distribution Unit) (i.e., functionally split), and the two units may be connected by a fronthaul interface. When the mobile communication system 1 is a 5G system, the network node 200 is a gNB, the interface between network nodes is an Xn interface, and the fronthaul interface is an F1 interface, respectively.

[0014] In addition, when 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, when the mobile communication system 1 is a system after the 6th generation system, the network node 200 has the function of a base station and may be a device corresponding to a gNB or an eNB.

[0015] Each network node 200 manages one or more cells. The network node 200 performs wireless communication with UE 100s that have established a connection to its own cell. Each network node 200 has functions such as Radio 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). In the following explanation, the gNB may be used as an example of a network node 200.

[0016] 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. If 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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 for 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.

[0022] Figure 3 shows an example configuration of a network node 200 according to the first embodiment. The network node 200 comprises a transmitter 210, a receiver 220, a control unit 230, and an NW communication unit 240. The transmitter 210 and receiver 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.

[0023] 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 radio signal and transmits it from the antenna.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 4 shows an example of the protocol stack configuration for a user-plane wireless interface that handles data.

[0028] The user plane radio interface protocol comprises a physical (PHY) layer, a media 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.

[0029] 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 over 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 parity bit added, which is scrambled by the RNTI.

[0030] 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. UE100 sends and receives data and control signals in the active BWP. UE100 may have up to four BWPs configured, for example. Each BWP may have a different subcarrier spacing, and their frequencies 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.

[0031] The network node 200 can, for example, configure 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 12 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 symbols in the time domain.

[0032] The MAC layer performs data priority control, retransmission processing using Hybrid ARQ (HARQ), 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.

[0033] The RLC layer transmits data to the receiving RLC layer by utilizing 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.

[0034] The PDCP layer performs header compression / decompression, encryption / decryption, etc.

[0035] The SDAP layer maps IP flows, which are the units under which the core network performs QoS 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 layer may not be necessary.

[0036] Figure 5 shows the configuration of the protocol stack of the wireless interface of the control plane that handles signaling (control signals).

[0037] The protocol stack of the control plane's wireless interface includes a Radio Resource Control (RRC) layer and a Non-Access Stratum (NAS) layer, instead of the SDAP layer shown in Figure 4.

[0038] 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.

[0039] 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. The UE100 also has application layers in addition to its wireless interface protocol. Furthermore, layers below the NAS are called AS (Access Stratum).

[0040] (AI / ML technology) Next, we will describe the AI / ML (Artificial Intelligence / Machine Learning) technology according to the embodiment. 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.

[0041] The example of the functional block configuration shown in Figure 6 includes a Data Collection unit A1, a Model Training unit A2, an Inference unit A3, a Management unit A5, and a Model Storage unit A6.

[0042] The example functional block configuration shown in Figure 6 represents a typical functional framework for AI / ML technology. Therefore, depending on the hypothetical use case, some parts of the example functional block configuration (e.g., model recording unit A6) may not be included. Furthermore, the example functional block configuration shown in Figure 6 may be distributed between the UE100 and the network device. Alternatively, some functions (e.g., model learning unit A2 or model inference unit A3) may be located in both the UE100 and the network device.

[0043] 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.

[0044] Training data is the data required as input when an AI / ML model is learning. Similarly, inference data is the data required as input when an AI / ML model is performing inference. Furthermore, monitoring data is the data required as input when managing the AI / ML model.

[0045] Data collection may also refer to the process of collecting data at network nodes, management entities, or UE100 for purposes such as training AI / ML models, managing AI / ML models, and performing inference on AI / ML models.

[0046] Model learning unit A2 performs AI / ML model training, AI / ML model validation, and AI / ML model testing.

[0047] AI / ML model training is the process of training an AI / ML model based on input-output relationships to obtain a trained AI / ML model that can be used for inference. For example, y = ax + b In this context, the process of optimizing a (slope) and b (intercept) by providing input (x) and output (y) (i.e., providing training data) can also be considered AI / ML model learning.

[0048] Generally, machine learning includes supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses correct answer data as training data. Unsupervised learning does not use correct answer data as training data. For example, in unsupervised learning, feature points are memorized from a large amount of training data, and correct answers (range estimation) are determined. Reinforcement learning is a method in which output results are scored, and the system learns how to maximize the score. Supervised learning will be explained below, but unsupervised learning or reinforcement learning may also be applied to machine learning.

[0049] The model learning unit A2 outputs the trained AI / ML model obtained through AI / ML model learning to the model recording unit A6. The model learning unit A2 also outputs the updated AI / ML model obtained through retraining of the trained AI / ML model to the model recording unit A6.

[0050] In the following, AI / ML model learning may be referred to as "model learning" or "learning."

[0051] Model inference unit A3 performs AI / ML model inference. Specifically, model inference unit A3 applies the inference data provided by data collection unit A1 to a trained AI / ML model (or updated AI / ML model) to obtain inference output data. For example, y = ax + b In this context, x represents the inference data, and y represents the inference output data. Note that "y=ax+b" is an AI / ML model. A model with optimized slope and intercept, such as "y=5x+3", is a trained AI / ML model. Various modeling methods (approaches) exist, 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.

[0052] 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.

[0053] AI / ML model inference is, for example, the 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 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."

[0054] In the following, 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, if there is no distinction between a training (or updating) AI / ML model and a trained (or updated) AI / ML model, it may simply be referred to as an "AI / ML model."

[0055] The management unit A5 oversees operations on the AI / ML model (selection, activation, deactivation, switching, fallback, etc.). It also oversees 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 / Deliverly Request to the Model Recording Unit A6, causing the Model Recording Unit A6 to output the trained (or updated) AI / ML model recorded thereto to the Model Inference Unit A3. Furthermore, the management unit A5 outputs management instructions to the Model Inference Unit A3, overseeing operations on the AI / ML model. In addition, the management unit A5 can output performance feedback and retraining requests to the Model Learning Unit A2, causing the Model Learning Unit A2 to retrain the AI / ML model (i.e., update the trained AI / ML model).

[0056] (Use case) Next, we will discuss use cases where AI / ML technology is applied. For example, the following three use cases are examples where AI / ML technology is applied:

[0057] • (X1.1) "CSI (Channel State Information) feedback enhancement"; (X1.2) "Beam management"; ·(X1.3) "Positioning accuracy enhancement".

[0058] (X1.1) Improved CSI feedback "CSI Feedback Improvement" describes a use case where AI / ML technology is applied to the CSI 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 uses the CSI feedback from UE100 to schedule downlinks, for example.

[0059] 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.

[0060] (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 then sent from UE100 to network node 200.

[0061] 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.

[0062] 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. If a partial CSI is input, the output of the CSI generation inference unit 1010 will be a CSI with more CSI values ​​than the partial CSI. If a CSI reference signal is input, the output of the CSI generation inference unit 1010 will be a CSI. Hereinafter, the output of the CSI generation inference unit 1010 will be referred to as a 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.

[0063] 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.

[0064] 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, the CSI generation inference unit 1010 may have a preprocessing unit, and the CSI reconstruction inference unit 2011 may have a postprocessing unit.

[0065] 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 trained AI / ML models on both the UE100 side and the network node 200 side.

[0066] (X.1.1.2) Sub-use case: CSI prediction CSI prediction uses a pre-trained AI / ML model to infer (predict) future CSIs based on past CSI history.

[0067] 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 a UE-sided model in UE100 where inference is performed using a pre-trained AI / ML model.

[0068] 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) sends the predicted CSI as CSI feedback to the network node 200.

[0069] Furthermore, the CSI prediction model 102 may have a pre-processing unit before it, or a post-processing unit after it.

[0070] (X1.2) Beam management In beam management use cases, there are two sub-use cases: spatial-domain downlink beam prediction, which predicts the beam in the spatial direction, and temporal downlink beam prediction, which predicts the beam in the temporal direction. Spatial-domain downlink beam prediction is called "BM-Case 1," and temporal downlink beam prediction is called "BM-Case 2."

[0071] 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).

[0072] In BM Case 1, the input to AI / ML Model 103 is the measured value for each beam in beamset B. On the other hand, the output from AI / ML Model 103 (inference output data) is the probability that each (downstream) beam 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 value of beamset B, which is the input to AI / ML Model 103, may be expressed as RSRP (Reference Signal Received Power).

[0073] Figure 8 also shows an example configuration of the mobile communication system 1 in BM case 2. In BM case 2, either the UE side model or the NW side model may be applied. In BM case 2, the AI / ML model 103 is located in either UE100 or NW10.

[0074] 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 measurements for each beam taken in the past 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.

[0075] In both BM Case 1 and BM Case 2, the UE side model allows UE100 to report prediction results to NW10. Furthermore, in both BM Case 1 and BM Case 2, the NW side model can predict the top beam based on the measurements for each beam included in beamset B reported by UE100.

[0076] (X1.3) "Positioning accuracy enhancement" In use cases for improving positional 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 positional measurements. In the latter, AI / ML assisted positioning, the position of UE100 is measured or inferred in the LMF (Location Management Function) using intermediate positional measurements. Intermediate positional measurements can serve as assist information for measuring or inferring the position of UE100 in the LMF. The LMF may have a trained AI / ML model, and uses this trained AI / ML model to infer the position of UE100.

[0077] (X1.3.1) Sub-use case: Direct AI / ML positioning Figure 9(A) shows an example of the configuration of a functional block in 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 UE 100 or in NW 10.

[0078] In direct AI / ML positioning, the input to the AI / ML model 104 is measured values ​​at each measurement point (TRP: Transmission and / or Reception Point). These measured values ​​may include, for example, channel impulse response (CIR), power delay profile (PDP), or fingerprint. For instance, 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 UE100. This position information may also be represented by a fingerprint. The fingerprint, for example, represents the measurement information for the UE100 cell.

[0079] (X1.3.2) Sub-use case: AI / ML assisted positioning Figures 9(B) to 10(B) show examples of the configuration of functional blocks in 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 UE 100 or in NW 10. In the case of AI / ML assisted positioning, there are cases where one AI / ML model 105 is used for multiple inputs (Figure 9(B)), where the same AI / ML model is used for each of the multiple inputs (Figure 10(A)), and where different AI / ML models are used for each of the multiple inputs (Figure 10(B)).

[0080] 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 positional 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.

[0081] (LCM) Currently, 3GPP is discussing Life Cycle Management (LCM) for AI / ML models.

[0082] Specifically, the Lifecycle Management (LCM) of an AI / ML model may include at least one of the following actions:

[0083] (L1) Data collection; (L2) Model training; ·(L3)Identification; (L4) Model delivery or transfer; (L5) Model inference operation; • (L6) Selection, Activation, Deactivation, Switching, and Fallback operations; (L7) Monitoring; (L8) Model updating; ·(L9)UE capability.

[0084] On the network side, for example, by controlling each operation of the LCM, it becomes possible to properly manage everything from the creation 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."

[0085] 3GPP specifies two types of Lifecycle Management (LCM): function-based LCM and model ID-based LCM.

[0086] Function-based LCM may be an operation performed on the functions of an AI / ML model. Specifically, function-based LCM may perform one of the following operations on the functions of an AI / ML model: activation, deactivation, switching, or fallback. The network side can instruct LCM operations on the functions of an AI / ML model using, for example, 3GPP signaling (RRC messages, MAC CE, or DCI). In function-based LCM, UE100 may have one AI / ML model for one function, or it may have multiple AI / ML models for one function.

[0087] 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 others. 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.

[0088] (Communication method according to the first embodiment) Next, the communication method according to the first embodiment will be described.

[0089] For example, in a UE-side model where model inference (hereinafter sometimes referred to as "inference") is performed on the UE100 side, the AI / ML model's functionality can be classified as follows:

[0090] Supported functionalities: Supported functions are those that the UE100 can indicate using UE Capability Information messages. Functions of AI / ML models that the UE100 can report to network devices using UE Capability Information messages can be considered supported functions.

[0091] Applicable functionalities: Applicable functions are those in the UE100 that are ready for inference using AI / ML models. In this context, any function that enables the UE100 to perform AI / ML model inference upon receiving instructions from a network device can be considered an applicable function.

[0092] • Activated functionalities: An activated function is a function in UE100 that performs inference using an AI / ML model. Therefore, any function in UE100 that performs inference using an AI / ML model can be considered an activated function.

[0093] In the first embodiment, the applicable functionalities will be described.

[0094] For example, consider the following scenario: In UE100, a certain AI / ML model's functionality was initially applicable, but due to insufficient CPU resources, it became non-applicable. Therefore, UE100 transmits (or reports) information about the AI / ML model's functionality that can no longer be applied to the network device.

[0095] Figures 11(A) and 11(B) illustrate examples of reporting of an unapplicable function. As shown in Figure 11(A), five functions were initially applicable to UE100, but function ID = "0005" became unapplicable. In this case, UE100 can report the function ID (= "0005") of the unapplicable function.

[0096] On the other hand, as shown in Figure 11(B), assuming that applicable functions have function IDs from "0001" to "0005", UE100 can indirectly report an inapplicable function (function ID = "0005") by reporting the function IDs of the applicable functions (= "0001" to "0004").

[0097] In other words, when reporting the function IDs of functions that UE100 cannot apply, it is possible to report them either by using a function ID that explicitly represents the function ID of the non-applicable AI / ML model, or by using a function ID that implicitly represents the function ID of the non-applicable AI / ML model by using the function ID of an applicable AI / ML model.

[0098] However, for example, in Figure 11(A), if four functions (for example, function IDs "0001" through "0004") become unapplicable, reporting indirectly (reporting the function ID of an applicable function = "0005") requires only one function ID to be reported, rather than directly specifying the function IDs of the unapplicable functions. This reduces the number of function IDs to be reported. This, for example, can improve the efficiency of wireless resources used for reporting.

[0099] In other words, when reporting non-applicable feature IDs, if the number of applicable feature IDs is greater than the number of non-applicable feature IDs, it is better to specify them directly (Figure 11(A)). Conversely, if the number of non-applicable feature IDs is greater than the number of applicable feature IDs, it is better to specify them indirectly. The reporting method for non-applicable feature IDs should be determined based on the number of applicable feature IDs and the number of non-applicable feature IDs.

[0100] Therefore, in the first embodiment, the objective is to enable UE100 to appropriately transmit the function ID of an inapplicable function.

[0101] Therefore, in the first embodiment, when reporting a function ID for a function that is not applicable, information indicating whether the function ID is used directly (or explicitly) or indirectly by using the function ID of an applicable function is reported along with the function ID.

[0102] Specifically, the user device (e.g., UE100) sends designation information to the network node (e.g., network node 200) along with the function identification information (e.g., function ID) indicating whether to use function identification information that directly represents the functions of non-applicable AI / ML models, or to use function identification information that indirectly represents the functions of non-applicable AI / ML models by using function identification information of applicable AI / ML models.

[0103] This allows UE100 to directly use non-applicable function IDs or indirectly use applicable function IDs, depending on the number of applicable function IDs and the number of non-applicable function IDs, enabling it to report using fewer function IDs. Therefore, UE100 can appropriately transmit function IDs of non-applicable functions. Furthermore, UE100 can report to network node 200 which function IDs were used.

[0104] In the following, we may use "Explicitly" to directly represent the functions of an AI / ML model that are not applicable, and "Implicitly" to indirectly represent the function IDs of non-applicable functions by using the function IDs of applicable AI / ML models.

[0105] (Example of operation according to the first embodiment) Next, an example of operation according to the first embodiment will be described.

[0106] Figures 12 to 14 are diagrams illustrating an example of operation according to the first embodiment. In Figures 12 to 14, an example of a network node 200 is shown as a network device.

[0107] In the following explanation, messages or information are sent from network node 200 to UE100, and these may be sent using RRC messages, MAC CE, or DCI (Downlink Control Information). Alternatively, they may be sent using messages from the AI / ML layer newly introduced for AI / ML models. Similarly, messages or information are sent from UE100 to network node 200, and these may also be sent using RRC messages, MAC CE, UCI (Uplink Control Information), or the new AI / ML layer messages. In all cases, the explanation will be omitted below.

[0108] As shown in Figure 12, in step S10, the receiving unit 110 of the UE 100 receives a trained AI / ML model (hereinafter sometimes referred to as "AI / ML model") from the OTT (Over The Top) server. The receiving unit 110 may receive multiple AI / ML models. The receiving unit 110 may also receive the function ID of the AI / ML model. The receiving unit 110 may also receive usage conditions indicating the conditions for using the AI / ML model. The usage conditions may be represented by identification information that identifies the usage conditions. The receiving unit 110 may also receive additional conditions for the AI / ML model. The additional conditions indicate conditions that the network side adds to the usage conditions of the AI / ML model. The additional conditions may be represented by an Associated ID.

[0109] In step S11, the NW communication unit 240 of the network node 200 (for example, the first network node) receives model information from the OTT server. The model information represents information about the AI / ML model transmitted by the OTT server in step S10. 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 the UE 100 together with the AI / ML model in step S10.

[0110] In step S12, the transmitting unit 210 of the network node 200 sends a UE Capability Enquiry message. The UE Capability Enquiry message may be a message that queries the capabilities of the AI / ML model held by the UE. The UE Capability Enquiry message may include information to query whether the functions of the AI / ML model held by the UE 100 are applicable. Alternatively, instead of information to query whether they are applicable, the UE Capability Enquiry message may include information to query whether they are supported functionalities. The receiving unit 110 of the UE 100 receives the UE Capability Enquiry message.

[0111] In step S13, the transmitter 120 of UE100 sends a UE Capability Information message to the network node 200. The UE Capability Information message is a response message to the UE Capability Inquiry message. The UE Capability Information message includes the function IDs of the applicable functionalities that can be used with the AI / ML model it holds. These function IDs may include the function IDs of all functions applicable to UE100. In the example in Figure 12, the function IDs are "0001" to "0005" (Figure 11(A)). As mentioned above, when UE100 reports the function IDs to the network node 200, the transmission message may include a serial number related to the function IDs. The serial number may be a number that is incremented each time a function ID is transmitted. In the example in Figure 12, the UE Capability Information message includes the serial number "#1". Note that the UE Capability Information message may include the function IDs of supported functions instead of the function IDs of applicable functions. The receiving unit 220 of the network node 200 receives the UE capability information message.

[0112] In step S14, the transmitter 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 transmitter 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 by instructing activation. The AI / ML Configuration message may also be an RRC Reconfiguration message. The receiver 110 of the UE 100 receives the AI / ML Configuration message.

[0113] In step S15, the control unit 130 of the UE100, upon receiving the AI / ML configuration message, starts inference of an AI / ML model having the function specified by the function ID.

[0114] In step S16, the transmitter 120 of the UE 100 sends an AI / ML Configuration Complete message. The AI / ML Configuration Complete message is a response message to the AI / ML Configuration message (step S14). The AI / ML Configuration Complete message includes the response result to the activation instructed in step S14. In the example in Figure 12, the response result may include an indication that inference is being performed on the AI / ML model for the function ID.

[0115] In step S20 (Figure 13), the control unit 130 of the UE100 detects that the battery is low. The control unit 130 may also detect low battery capacity by detecting the battery's capacity and determining that the capacity is below a threshold.

[0116] In step S21, the control unit 130 of the UE100 stops all AI / ML models in response to detecting insufficient battery power (step S20). In the example described above, the control unit 130 stops all AI / ML models having function IDs from "0001" to "0005".

[0117] In step S22, the transmitter 120 of UE100 sends a UE Assistance Information message to the network node 200. In the example shown in Figure 13, the UE Assistance Information message is used to report to the network node 200 that UE100 has stopped all AI / ML models.

[0118] Firstly, UE Assist information includes functional information (non-applicable functionality identifiers). Functional information represents information about non-applicable functions.

[0119] The function information may include designation information. The designation information may indicate whether to use an explicit function ID (a function ID used when directly using a function ID that cannot be applied) or an implicit function ID (a function ID used when indirectly using a function that can be applied). The function information may include the target function ID along with the designation information. For example, if the designation information in the function information is "explicitly designated function ID" and the function ID is "0001", then the function information indicates that the function ID of the function that cannot be applied is "0001". Also, for example, if the designation information in the function information is "implicitly designated function ID" and the function ID is "0001", then the function information indicates that the function ID of the function that cannot be applied is a function ID other than "0001" (in other words, the function ID of the function that can be applied is "0001").

[0120] The function information may include information indicating "All NG" or "All OK". "All NG" indicates that all of the target functions are not applicable (non-applicable). "All OK" indicates that all of the target functions are applicable (applicable).

[0121] Secondly, UE assist information may include differential information. Differential information is indicated by a serial number. The function ID sent in the message containing the serial number may become the function ID subject to the function information. The differential information represents the serial number included in the message that sent the target function ID. For example, in step S22 of Figure 12, if the differential target = "serial number #1", then the function IDs (functions from "0001" to "0005") sent in the UE capability information message (step S13) containing serial number #1 become the target of the function information. And since the function information is "all NG", it indicates that not all of the target "0001" to "0005" are applicable.

[0122] Thirdly, the UE assist information may include a serial number. In the example in Figure 12, the serial number is serial number #2.

[0123] The UE assist information in step S22 shows an example that includes "All NG" as functional information, "Serial number #1" as differential information, and "Serial number #2". The receiving unit 220 of the network node 200 receives this UE assist information.

[0124] In step S23, the control unit 230 of the network node 200 detects, based on the function information and differential information, that all of the functions reported in the UE capability information message (functions "0001" to "0005") are not applicable to the UE 100.

[0125] The following describes other operational examples different from steps S20 to S23. All of these examples represent the results after the processing shown in Figure 12, from steps S10 to S16.

[0126] (Another example of operation according to the first embodiment 1) Steps S30 to S33 illustrate an example of what happens when the operation of an AI / ML model with a certain function ID is stopped in UE100.

[0127] In other words, in step S30, the control unit 130 of the UE100 detects a shortage of CPU resources. The control unit 130 may also check the CPU usage rate and detect a shortage of CPU resources if the usage rate falls below a certain level.

[0128] In step S31, the control unit 130 of the UE100 detects a shortage of CPU resources and stops the AI / ML model having a function indicated by a certain function ID (here, function ID = "0005").

[0129] In step S32, the transmitter 120 of UE100 transmits UE assist information to the network node 200 in response to the AI / ML model being stopped (step S31). In the example shown in Figure 13, the UE assist information includes function information, difference information (=sequence number #1), and a sequence number (=sequence number #3). The function information includes "Explicitly Function ID" as designation information and "0005" as the function ID. That is, the function ID that is not applicable is "0005", and the applicable function IDs are from "0001" to "0005", or in other words, the function IDs that are applicable to UE100 are from "0001" to "0004". The difference information may be used to identify the applicable function IDs. The receiver 220 of the network node 200 receives the UE assist information.

[0130] In step S33, the control unit 230 of the network node 200 detects the function ID of a function that is not applicable to UE100 based on the function information and differential information. In the example shown in Figure 13, the control unit 230 detects that the AI / ML model with function ID = "0005" is not applicable to UE100.

[0131] (Another example of operation according to the first embodiment 2) Steps S40 to S43 (Figure 14) illustrate an example of operation when an AI / ML model with a function other than a certain function ID is stopped in UE100.

[0132] In other words, in step S40, the control unit 130 of the UE100 detects a memory shortage. The control unit 130 may also check the remaining memory capacity and detect a memory shortage if the remaining capacity falls below a certain value.

[0133] In step S41, the control unit 130 of UE100 stops the AI / ML model having a function other than function ID "0001" in response to detecting insufficient memory (step S40).

[0134] In step S42, the transmitter 120 of UE 100 transmits UE assist information to the network node 200 in response to the AI / ML model being stopped (step S41). This UE assist information includes function information and a serial number (=serial number #4). The function information includes "Implicitly applicable function ID" as designation information and "0001" as the function ID. In other words, it indicates that AI / ML models with functions other than function ID "0001" are not applicable; to put it another way, function ID "0001" is applicable, and other function IDs are not. In this case, the UE assist information does not need to include differential information. If differential information is considered to be information used to identify applicable function IDs, then the above function information can be used to identify applicable function IDs, so differential information does not need to be included. Alternatively, differential information may be included in the UE assist information to identify inapplicable function IDs. If the difference information is "serial number #1", the target function IDs are "0001" to "0005", so it can be identified that function IDs other than "0001" ("0002" to "0005") are not applicable function IDs. The receiving unit 220 of the network node 200 receives the UE assist information.

[0135] In step S43, the control unit 230 of the network node 200 detects, based on the function information, that the AI / ML model with function ID = "0001" is applicable to the UE 100. The control unit 230 may also detect function IDs that are not applicable (in this case, "0002" to "0005") based on the function information and the difference information.

[0136] (Another example of operation according to the first embodiment 3) Steps S50 to S53 illustrate an example of operation in UE100 when all AI / ML models are stopped due to insufficient battery power (steps S20 to S32 in Figure 13), and then all AI / ML models are started after the battery power is restored.

[0137] In other words, in step S50, the control unit 130 of the UE100 detects that the battery charge has been restored. The control unit 130 may also detect that the battery charge has been restored if it detects that the battery capacity has fallen below a threshold and then risen above the threshold.

[0138] In step S51, the control unit 130 of the UE100 activates all AI / ML models in response to detecting that the battery level has been restored (step S50).

[0139] In step S52, the transmitter 120 of UE 100 transmits UE assist information to the network node 200 in accordance with the fact that all AI / ML models have been activated (step S51). This UE assist information includes function information, difference information (=sequence number #1), and a sequence number (=sequence number #5). The function information includes "All OK". That is, the function information and difference information indicate that all of the function IDs = "0001" to "0005" included in the UE capability information message (step S13 in Figure 12) that includes sequence number #1 are applicable functions. When "All OK" is included as function information, the function IDs included in the UE assist information may represent applicable functions. The receiver 220 of the network node 200 receives this UE assist information.

[0140] In step S53, the control unit 230 of the network node 200 detects, based on the function information, that the AI / ML for all functions with function IDs "0001" to "0005" are applicable to the UE100.

[0141] (Another example of operation according to the first embodiment 4) In the first embodiment, an example was described in which UE100 determines the specification information that specifies either an "explicitly specified function ID" or an "implicitly specified function ID". For example, the specification information may be set by the network node 200. Specifically, firstly, UE100 receives configuration information from the network node 200 indicating whether to use a function ID that directly represents a function of an inapplicable AI / ML model, or to use a function ID that indirectly represents a function of an inapplicable AI / ML model by using a function of an applicable AI / ML model. Secondly, UE100 sets the specification information according to the configuration information.

[0142] This allows, for example, UE100 to specify information according to the settings from network node 200 and transmit UE assist information including said information (steps S22, S32, S42, and S52).

[0143] The configuration information may be included in the UE capability inquiry message (step S12 in Figure 12) or in the AI / ML configuration message (step S14 in Figure 12).

[0144] [Other embodiments] In the first embodiment, an example of stopping individual AI / ML models (steps S31 and S41) was described. For example, the first embodiment can also be implemented when individual AI / ML models change from a non-applicable state to an applicable state. In this case, the function ID of the function of the AI / ML model that has become applicable can be expressed as an "explicitly defined function ID" (step S32) or an "implicitly defined function ID" (step S42), and the embodiment can be implemented in the same way as the first embodiment.

[0145] Furthermore, although the first embodiment described an example in which a sequential number is used when the UE100 reports a function ID, a sequential number does not have to be used. In this case, the control unit 230 of the network node 200 can store in memory the function IDs of applicable functions and / or inapplicable functions that have been reported by the UE100 in the past, thereby making them applicable function IDs that are subject to "explicitly specified function IDs" or "implicitly specified function IDs".

[0146] Furthermore, in the first embodiment, a network node 200 was used 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 to 14. In this case, NAS messages may be used for messages between the AMF and the UE 100.

[0147] Alternatively, the network device may be an LMF. In this case, the network node 200 in Figures 12 to 14 may be replaced with an LMF. LPP messages using LPP (LTE Positioning Protocol) may be used between the LMF and the UE100.

[0148] Alternatively, the network device may be an OTT server. In this case, the network node 200 in Figures 12 to 14 may be replaced with an OTT server. Messages between the OTT server and UE100 may use IP messages via the IP (Internet Protocol) protocol.

[0149] Furthermore, although the first embodiment described a function-based example, it can also be implemented in a model-based manner. In this case, in the first embodiment, "applicable function" is replaced with "applicable model," and "non-applicable function" is replaced with "non-applicable model." Instead of processing being performed on a function ID basis in the UE100 and network node 200, processing will be performed on a model ID basis.

[0150] 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.

[0151] In the embodiments and examples described above, an example was given where the base station is an NR base station (gNB), 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 repeater that performs signal relay. Such a terminal function unit is called an MT. Examples of MTs other than IAB-MT include, for example, NCR (Network Controlled Repeater)-MT and RIS (Reconfigurable Intelligent Surface)-MT.

[0152] 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.

[0153] A program may be provided that causes a computer to execute each process performed by the UE100 or 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 or DVD-ROM. Alternatively, the circuits that execute each process performed by the UE100 or network node 200 may be integrated, and at least a part of the UE100 or network node 200 may be configured as a semiconductor integrated circuit (chipset, SoC).

[0154] The functions realized by the above-described communication device (UE100 or network node 200, etc.) may be implemented in a circuit or processing circuitry, 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. A processor, including transistors and other circuits, is considered a circuit or processing circuitry. A processor may be a programmed processor that executes a program stored in memory. In this specification, circuitry, unit, and means are hardware programmed to realize or execute the described functions. Such hardware may be any hardware disclosed herein, or any hardware known to be programmed to realize or execute the described functions. If such hardware is a processor that is considered a type of circuitry, then such circuitry, means, or unit is a combination of hardware and software used to constitute such hardware and / or processor.

[0155] The terms "based on" and "depending on / in response to" used in this disclosure do not mean "based solely on" or "depending solely on" unless otherwise specified. "Based on" means both "based solely on" and "at least partially on." Similarly, "depending on" means both "at least partially on" and "at least partially on." The terms "include," "comprise," and their variations do not mean to 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" used in this disclosure is not intended to mean exclusive OR. Additionally, any reference to elements using designations such as "first," "second," etc., 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 by the context that they are not.

[0156] 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.

[0157] (Note) The embodiments described above can be summarized as shown in the appendix, but the appendix does not limit the embodiments.

[0158] (Note 1) A communication method in a mobile communication system, The user device transmits to a network node, along with the function identification information, designation information indicating whether it uses function identification information that directly represents the functions of an AI / ML model that are not applicable, or function identification information that indirectly represents the functions of an AI / ML model that are not applicable by using function identification information of an applicable AI / ML model. User device.

[0159] (Note 2) The user device further includes the step of receiving from the network node configuration information indicating whether to use the function identification information that directly represents the function of the inapplicable AI / ML model, or to use the function identification information that indirectly represents the function of the inapplicable AI / ML model by representing the function of the applicable AI / ML model. The transmission step includes the user device setting the function identification information and the designation information according to the setting information. User equipment as described in Appendix 1.

[0160] (Note 3) The aforementioned transmission step involves the user device transmitting to the network node, along with the function identification information and the designation information, a sequential number that is incremented each time the function identification information is transmitted. User device as described in Appendix 1 or Appendix 2.

[0161] (Note 4) A user device in a mobile communication system, The system includes a transmission unit that transmits, along with the function identification information, to a network node, designation information indicating whether the function identification information directly represents a non-applicable function of the AI / ML model, or whether the function identification information indirectly represents a non-applicable function of the AI / ML model by representing an applicable function of the AI / ML model. User device. [Explanation of symbols]

[0162] 1: Mobile communication system 20: RAN 30 :CN 100 :UE 110: Receiving unit 120: Transmitting unit 130: Control Unit 200: Network Node 210: Transmitter 220: Receiver 230: Control Unit 240: Network Communication Unit 300:CN device

Claims

1. A communication method in a mobile communication system, The user device transmits to a network node, along with the function identification information, designation information indicating whether it uses function identification information that directly represents the functions of an AI / ML model that are not applicable, or function identification information that indirectly represents the functions of an applicable AI / ML model by using function identification information of an applicable AI / ML model. User device.

2. The user device further includes the step of receiving from the network node configuration information indicating whether to use the function identification information that directly represents the function of the inapplicable AI / ML model, or to use the function identification information that indirectly represents the function of the inapplicable AI / ML model by representing the function of the applicable AI / ML model. The transmission step includes the user device setting the function identification information and the designation information according to the setting information. The user device according to claim 1.

3. The aforementioned transmission step involves the user device transmitting to the network node, along with the function identification information and the designation information, a sequential number that is incremented each time the function identification information is transmitted. The user device according to claim 1.

4. A user device in a mobile communication system, The system includes a transmission unit that transmits, along with the function identification information, to a network node, designation information indicating whether the function identification information directly represents a non-applicable function of the AI / ML model, or whether the function identification information indirectly represents a non-applicable function of the AI / ML model by representing an applicable function of the AI / ML model. User device.

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