Communication method and user device

Abstract

A communication method according to one embodiment of the present invention is used in a mobile communication system. The communication method comprises a step in which a user device transmits, to a network device, a first message that includes request information for requesting either activation of a first function in an AI / ML model or switching to the first function in the AI / ML model. The communication method also comprises a step in which the user device receives, from the network device, a second message that includes result information indicating a result for the request information.

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 3GPP (Third Generation Partnership Project) (registered trademark, the same hereinafter), which is a standardization project for mobile communication systems, there has been a study 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.

[0003] 3GPP TR 38.843 V18.0.0 (2023 - 12) 3GPP TS 38.000 V18.4.0 (2024 - 12) 3GPP TS 28.105 V19.1.0 (2024 - 12)

[0004] The communication method according to the first aspect is a communication method used in a mobile communication system. The communication method includes a step in which a user equipment transmits a first message including request information for requesting either activation of a first function in an AI / ML model or switching to the first function in the AI / ML model to a network device. Further, the communication method includes a step in which the user equipment receives a second message including result information indicating a result for the request information from the network device.

[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 a first message including request information for requesting either activation of a first function of an AI / ML model or switching to the first function of the AI / ML model to a network device. Further, the user equipment includes a reception unit that receives a second message including result information indicating a result for the request information from the network device.

[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) shows an example configuration of a functional block of a mobile communication system according to the first embodiment, and Figure 7(B) shows 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 an example configuration of a functional block of a mobile communication system according to the first embodiment. Figures 10(A) and 10(B) are diagrams showing an example configuration of a functional block of a mobile communication system according to the first embodiment. Figure 11 is a diagram showing an example of operation according to the first embodiment.

[0007] This disclosure aims to provide a communication method and user device that enable the user device to appropriately execute the functions of the AI / ML model desired by the user device.

[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 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), 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 multiple network nodes 200 (network nodes 200a to 200c in the example in 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 also be the network node 200. The said network device may also be 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 over the physical downlink control channel (PDCCH). Specifically, UE100 performs blind decoding of the PDCCH using the 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 has been scrambled by 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), 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 a 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 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 (Evolved Packet Core), the SDAP layer 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 example of the functional block configuration shown in Figure 6 includes a Data Collection unit A1, a Model Training unit A2, a Model Inference unit A3, a Management unit A5, and a Model Storage unit A6.

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

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

[0043] 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.

[0044] Note that data collection, for example, may be a process of collecting data in a network node, a management entity, or UE100 for performing learning of an AI / ML model, management of the AI / ML model, and inference of the 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 from the relationship between inputs and outputs to obtain a trained AI / ML model for use in inference. For example, considering y = ax + b, the process of optimizing a (slope) and b (intercept) by giving an input (x) and an output (y) (that is, by giving training data) may be AI / ML model training.

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

[0048] The model learning unit A2 outputs the trained AI / ML model obtained by AI / ML model learning to the model recording unit A6. Also, the model learning unit A2 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 learning may sometimes be referred to as "model learning" or "learning".

[0050] The model inference unit A3 performs AI / ML model inference. Specifically, the model inference unit A3 applies the inference data provided by the data collection unit A1 to the trained AI / ML model (or updated AI / ML model) to obtain inference output data. For example, considering y = ax + b, x is the inference data and y corresponds to the inference output data. Here, "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. Here, the approaches of the model are various, including linear regression analysis, neural networks, decision tree analysis, etc. The above "y = ax + b" can also be considered as a type of linear regression analysis.

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

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

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

[0059] (X1.1) "Improved CSI Feedback" "Improved CSI Feedback" 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.

[0060] 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.

[0061] (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.

[0062] 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.

[0063] The CSI generation unit 101 includes a CSI generation inference unit 1010 and a quantization unit 1011. The CSI generation inference unit 1010 infers CSI (inference output data) 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: Channel State Information-Reference Signal) (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. The output of the CSI generation inference unit 1010 is a CSI with a larger number of CSIs than the partial CSI input, if a partial CSI is input. Also, the output of the CSI generation inference unit 1010 is a CSI if 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.

[0064] 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.

[0065] 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 preprocessing unit may be provided before the CSI generation inference unit 1010. A postprocessing unit may be provided after the CSI reconstruction inference unit 2011.

[0066] 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.

[0067] (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.

[0068] 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.

[0069] 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.

[0070] Furthermore, the CSI prediction model 102 may have a pre-processing unit before it. The CSI prediction model 102 may also have a post-processing unit after it.

[0071] (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).

[0072] Figure 8 is a diagram showing 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, a UE side model in which inference is performed at UE 100 may be applied. A NW side model in which inference is performed on the network side may also be applied. Therefore, as shown in Figure 8, the AI / ML model 103 that performs inference may reside at UE 100. The AI / ML model 103 that performs inference may reside at NW 10 (a network node 200 or CN device 300 included in NW 10).

[0073] 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 value of beamset B, which is the input to the AI / ML model 103, may be expressed as RSRP (Reference Signal Received Power).

[0074] 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, the UE side model may also be applied. In the case of BM case 2, the NW side model may also be applied. In the case of BM case 2, the AI / ML model 103 is located in either UE 100 or NW 10.

[0075] 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. Beamset B may be a subset of beamset A. Also, in BM Case 2, beamset A and beamset B may be the same.

[0076] 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.

[0077] (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.

[0078] (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. The AI / ML model 104 used for inference may reside in the NW 10.

[0079] In the case of 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 UE 100. This position information may also be represented by a fingerprint. The fingerprint, for example, represents the measurement information for the cells of the UE 100.

[0080] (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. The AI / ML model 105 used for inference may reside in the NW 10. In the case of AI / ML assisted positioning, there are cases where one AI / ML model 105 is used for multiple inputs (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)).

[0081] 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.

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

[0083] Specifically, the LCM of an AI / ML model may include at least one of the following actions:

[0084] (L1) Data collection;

[0085] (L2) Model training;

[0086] (L3) Identification;

[0087] (L4) Model delivery or transfer;

[0088] (L5) Model inference operation;

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

[0090] (L7) Monitoring;

[0091] (L8) Model update;

[0092] (L9) UE capability: 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 the deletion (or disposal) of AI / ML models. 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".

[0093] 3GPP specifies two types of LCM: function-based LCM and model ID-based LCM.

[0094] 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.

[0095] 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.

[0096] (Communication method according to the first embodiment) Next, the communication method according to the first embodiment will be described.

[0097] Let's assume that the UE100 maintains an AI / ML model. In such a case, if the UE100 moves to a certain location (or environment), it may be possible to improve the accuracy of the AI / ML model by using a different function of the AI / ML model than the one previously used. For example, if the UE100 moves from a suburban area to an urban area, considering the radio propagation environment, especially the effects of multipath, it may be possible to improve the accuracy of the AI / ML model by using a different function of the AI / ML model than the one previously used.

[0098] When UE100 moves and the environment or surrounding conditions change, UE100 is the one that best knows those conditions. Therefore, UE100 may, on its own judgment, want to train (learn) or perform model inference (inference) using one function of an AI / ML model and another.

[0099] However, in the current 3GPP, there is no mechanism for UE100 to request the AI / ML model functions it desires from the network 10. Therefore, UE100 may not be able to properly activate the AI / ML model functions it desires or to properly switch to the AI / ML model functions it desires.

[0100] Therefore, the objective of the first embodiment is to enable the UE100 to appropriately execute the functions of the AI / ML model desired by the UE100.

[0101] Therefore, in the first embodiment, firstly, the user device (e.g., UE100) sends a first message to the network device containing request information requesting either the activation of a first function accessible to the AI / ML model, or switching to the first function in the AI / ML model. Secondly, the user device receives a second message from the network device containing result information indicating the result of the request information.

[0102] This allows the UE100, for example, to activate or switch to the desired AI / ML model functions based on the results from the network 10 in response to requests to the network 10. Thus, the UE100 can appropriately execute the AI / ML model functions it desires.

[0103] (Example of operation according to the first embodiment) Next, an example of operation according to the first embodiment will be described. In the example of operation according to the first embodiment, a network node 200 will be used as an example of a network device. Furthermore, in the example of operation according to the first embodiment, a UE side model in which inference is performed in UE 100 will be used as an example. In addition, in the example of operation according to the first embodiment, the switching operation of functions in the AI / ML model will be described.

[0104] (1) Common Operation Examples First, we will describe some common operation examples from the operation examples according to the first embodiment.

[0105] Figure 11 is a diagram illustrating an example of operation according to the first embodiment. Steps S10 to S18 in Figure 11 represent a common operation example. The common operation example will be described below.

[0106] As shown in Figure 11, in step S10, the receiving unit 110 of the UE 100 receives an AI / ML model and model information from the OTT (Over The Top) server. The AI / ML model may be a trained AI / ML model. The AI / ML model may be a trained AI / ML model. On the other hand, the model information indicates model information relating to the AI / ML model. The model information may include information indicating what kind of information is input and output to the AI / ML model. The model information may include identification information of the AI / ML model (e.g., model ID).

[0107] In step S11, the NW communication unit 240 of the network node 200 receives model information transmitted from the OTT server. The model information may be the same as the model information transmitted to the UE 100 (step S10).

[0108] In step S12, the transmitting unit 210 of the network node 200 sends a UE capability inquiry message to the UE 100. The UE capability inquiry message is used to request wireless access capability from the UE 100. The UE capability inquiry message may also be used to request capability for the AI / ML model (function) from the UE 100. The receiving unit 110 of the UE 100 receives the UE capability inquiry message.

[0109] In step S13, 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 is used to transmit the wireless access capability requested by the network 10. The UE capability information message may also be used to transmit the capability for the AI / ML model (function) requested by the network. The receiving unit 220 of the network node 200 receives the UE capability information message.

[0110] In step S14, the transmitting unit 210 of the network node 200 sends an RRC reconfiguration message to the UE 100 in response to receiving the UE capability information message. The RRC reconfiguration message may include query information asking whether the functions of the AI / ML model held by the UE 100 are applicable. The receiving unit 110 of the UE 100 receives the RRC reconfiguration message.

[0111] In step S15, the transmitting unit 120 of UE 100, upon receiving the RRC reset message, transmits Applicable Function Reporting information to the network node 200. The Applicable Function Reporting information includes information about applicable functions in UE 100. The Applicable Function Reporting information may also include information about non-applicable functions in UE 100. The Applicable Function Reporting information may also be an RRC message. The Applicable Function Reporting information may be transmitted as part of an RRC message. Alternatively, the Applicable Function Reporting information may be transmitted as part of MAC CE or UCI (Uplink Control Information). The receiving unit 220 of the network node 200 receives the Applicable Function Reporting information.

[0112] In step S16, the transmitting unit 210 of the network node 200 sends an RRC reset message to the UE 100 in response to receiving the applicable function report information. The RRC reset message may include instruction information that instructs activation of the AI / ML model (or its functions), taking into account the functions of the applicable AI / ML model reported in the applicable function report information. Alternatively, the RRC reset message may include instruction information that instructs operation related to the LCM. The receiving unit 110 of the UE 100 receives the RRC reset message.

[0113] In step S17, the control unit 130 of UE100 activates an AI / ML model having the function indicated in the instruction information, in response to receiving the RRC reset message (step S16). For example, UE100 activates function #1 of the AI / ML model according to the instruction information.

[0114] In the following example, the activation of the AI / ML model initiates inference, but the activation of the AI / ML model may also initiate training of the AI / ML model. The activation of the AI / ML model may also initiate monitoring of the AI / ML model. The instruction information may specify inference, training, or monitoring, and the control unit 130 may perform one of these actions according to the instruction information.

[0115] In step S18, the transmitter 120 of UE 100 sends an RRC Reconfiguration Complete message to the network node 200 in response to the activation of function #1 of the AI / ML model. The RRC Reconfiguration Complete message may include information indicating that the activation of the AI / ML model instructed by the instruction information (step S16) has been started. The receiver 220 of the network node 200 receives the RRC Reconfiguration Complete message.

[0116] (2) Example of operation The steps from step S20 onwards shown in Figure 11 represent a specific example of operation according to the first embodiment.

[0117] In step S20, the UE100 begins to move. In the following description, the UE100 will be described as moving, for example, from a LOS (Line-of-Sight) environment to an NLOS (Non-Line-of-Sight) environment.

[0118] In step S21, the control unit 130 of UE100 detects that function #2 of the AI / ML model is better than function #1 of the AI / ML model. For example, UE100 detects that function #2 is a better function in the following way.

[0119] In other words, UE100 has a GNSS (Global Navigation Satellite System) receiver, and the control unit 130 of UE100 confirms the location of UE100 based on the GNSS signal received by the GNSS receiver and detects that the location is an NLOS environment. The control unit 130 of UE100 may then detect that in an NLOS environment, function #2 is a better function than function #1. UE100 maintains table information that includes correspondence information between location and NLOS environment, and information indicating which function is better for each environment. Based on this table information, the control unit 130 of UE100 confirms that the confirmed location is an NLOS environment and may detect that function #2 is a better function than function #1 in that NLOS environment. Alternatively, the control unit 130 of UE 100 may detect that UE 100 has moved to an NLOS environment when the quality of the wireless signal received from the network node 200 is below a certain level, and may further detect that function #2 is better than function #1 in the NLOS environment. The NLOS environment may be estimated by a method of estimating the NLOS environment by comparing the arrival time of the LOS with the current arrival time and determining the arrival time delay. The NLOS environment may also be estimated by estimating the angle of arrival (AoA) of the radio waves to determine whether the propagation is direct or reflected. Other known methods may also be used to estimate the NLOS environment. UE 100 maintains table information in memory regarding which function is better for each environment, and may detect from this table information that function #2 is better than function #1 in the NLOS environment.

[0120] In step S22, the transmitting unit 210 of the network node 200 may transmit transmit permission information to the UE 100 indicating that the UE 100 permits the transmission of the request information. The transmit permission information may be transmitted using an RRC reset message (an example of a third message). The transmit permission information may be transmitted using another RRC message (an example of a third message). The transmit permission information may be transmitted in step S16. Alternatively, the transmit permission information may be transmitted using MAC CE or DCI (Downlink Control Information). The transmit permission information may include identification information about the permitted function (e.g., function ID). The transmit permission information may include identification information about the permitted AI / ML model (e.g., model ID). The receiving unit 110 of the UE 100 receives a message (an example of a third message) containing the transmit permission information.

[0121] In step S23, the transmission unit 120 of UE 100 sends a message containing request information (an example of a first message) to the network node 200.

[0122] Firstly, the request information may be information requesting a switch to a function in the AI / ML model (an example of a first function) (e.g., function #2). The request information may include the function ID of the function to be switched to. Alternatively, the request information may include the function ID of the function to be switched to (e.g., function #1).

[0123] Secondly, the request information may be transmitted in a UE Assistance Information message. Alternatively, the request information may be transmitted in another RRC message (such as an RRC Setup Request message or a Measurement Report message). Alternatively, it may be transmitted in Applicable Function Reporting information. Applicable Function Reporting information may also be an RRC message as described above. The Applicable Function Reporting information may be transmitted in a new message for a newly defined AI / ML layer for AI / ML (e.g., an AI / ML layer message). When Applicable Function Reporting information is used, the request information may be indicated by marking the function to which the request information pertains (e.g., function #2) is located among the applicable functions.

[0124] Furthermore, if UE100 is permitted to send the request information by the transmission permission information (step S22), it may send a message containing the request information to the network node 200 (step S23).

[0125] In step S24, the control unit 230 of the network node 200 determines whether to permit the request contained in the request information (for example, switching to function #2) in response to receiving the request information. The control unit 230 may also determine whether to permit the request by considering the resource status of the UE 100. For example, the control unit 230 can obtain the CPU processing speed of the UE 100 or the memory capacity of the UE 100 in the UE capability information message (step S13), and may determine the resource status of the UE 100 based on this information. Alternatively, the control unit 230 may determine the resource status based on the amount (or number) of reference signals transmitted.

[0126] In step S25, the transmission unit 210 of the network node 200 transmits result information indicating the determination result to the UE 100.

[0127] Firstly, the result information may include information indicating whether or not to permit the request indicated by the request information. This information may, for example, indicate whether or not to permit switching to function #2. The result information may also include information indicating that the startup of the original function (e.g., function #1) should be stopped (deactivated). Alternatively, the result information may include information indicating that the original function may remain running. The result information may include the function ID of the function to which the judgment result applies. The result information may also include the model ID of the AI / ML model to which the judgment result applies. If the result information includes information indicating that the request is not permitted, it may also include reason information indicating the reason for not permitting the request.

[0128] Secondly, the result information may be transmitted in an RRC reset information message. The result information may also be transmitted in another RRC message. Alternatively, the result information may be transmitted using MAC CE or DCI. Alternatively, the result information may be transmitted in an AI / ML layer message. The receiving unit 110 of UE 100 receives a message containing the result information (an example of a second message).

[0129] In step S26, the control unit 130 of the UE 100 activates function #2 of the AI / ML model in response to receiving result information. Function #1 (step S17) and function #2 may be the same AI / ML model. Function #1 (step S17) and function #2 may be different AI / ML models. In the case of different AI / ML models, the model ID of the AI / ML model is included in the result information, so the control unit 130 may activate function #2 of the different AI / ML model according to the result information. However, if the result information includes information indicating that the request is not permitted, the control unit 130 will not perform the switching operation to function #2 according to the result information.

[0130] In step S27, the transmission unit 120 of UE 100 transmits completion information to the network node 200 indicating that function #2 has been activated, in response to the activation of function #2. The completion information may also be transmitted as an RRC reset completion message. The completion information may also be transmitted as another message (including MAC CE, UCI, or new AI / ML layer messages).

[0131] (3) Other Operation Examples Next, other operation examples according to the first embodiment will be described. The other operation examples will be described mainly in terms of the differences from the first embodiment.

[0132] (3.1) Other Operation Example 1 In the first embodiment, a switching operation for switching functions of the AI / ML model was described, but the first embodiment can also be applied to the activation operation of functions in the AI / ML model. In this case, in Figure 11, when UE 100 detects function #2 which is better than function #1 (step S21), it sends request information to the network node 200 requesting the activation of a function in the AI / ML model (an example of a first function) (for example, function #2) (step S23). Then, when UE 100 receives result information indicating that it is OK to activate the requested function (step S25), it activates the requested function (step S26). If UE 100 receives result information indicating that it is OK to stop the activation of function #1 which is already activated and to activate the requested function #2, it will stop function #1 and activate function #2 according to the result information. If the result information does not include instructions for function #1, UE 100 may continue the activation of function #1. UE100 may stop the activation of function #1.

[0133] In addition to the startup operation, one of the following operations may also be applied: selection operation, deactivation operation, or fallback operation.

[0134] In the case of a selection operation, the request information may include the functions of the AI / ML model to be selected (step S23). The UE100 will then start the AI / ML model having the selected functions according to the result information (step S26).

[0135] In the case of non-activation operation, the request information may include the functions of the AI / ML model to be deactivated (step S23). The UE100 will stop the startup of the AI / ML model having the functions to be deactivated according to the result information.

[0136] In the case of fallback operation, the request information may include the functions of the AI / ML model to be fallbacked (step S23). UE100 executes the functions of the AI / ML model to be fallbacked using the legacy model, according to the result information.

[0137] (3.2) Other Operation Examples 2 In the first embodiment, an example in which UE 100 transmits request information was described (step S23). For example, UE 100 may transmit request information to the network node 200 along with inference result information indicating the inference result. Specifically, the transmission unit 120 of UE 100 activates function #1 of the AI / ML model (an example of a second function) (step S17), and transmits request information to the network node 200 along with the inference result for function #1 of the AI / ML model (step S23). The inference result and request information may be transmitted in an RRC message (an example of a first message). The inference result and request information may be transmitted in an AI / ML layer message (an example of a first message). The model ID (or function ID) of the inference result target may be included in the RRC message or the AI / ML layer message. The model ID (or function ID) of the inference result target may be transmitted in MAC CE or UCI.

[0138] (3.3) Other Operation Examples 3 In the first embodiment, an example of the UE side model was described, but the first embodiment can also be implemented with the NW side model.

[0139] For example, if the network device is a network node 200, the operation example shown in Figure 11 can be applied. That is, UE 100 does not receive the AI / ML model, but receives model information (step S10), while network node 200 receives the AI / ML model along with the model information (step S11). Also, UE 100 does not activate function #1 of the AI / ML model (step S17), but this is done at network node 200.

[0140] In this case, when UE 100 starts moving (step S20), it detects that function #2 is a better function than function #1, similar to the first embodiment (step S21). For example, UE 100 may detect that function #2 is a better function in the following way. That is, UE 100 can understand the functions of the AI / ML model that can be executed at the network node 200 based on model information (step S10). Similar to the first embodiment, UE 100 understands the surrounding environment based on GNSS received signals or wireless signal quality, etc. Then, similar to the first embodiment, UE 100 may obtain which function is better for each environment from table information and detect that the obtained function is a better function. In this case, since UE 100 may not know what functions are currently being executed at the network node 200, even if it cannot detect function #1, it is possible to detect that function #2 is a better function depending on the environment based on table information. Then, by collecting data associated with the function, UE 100 can request the function even in NW-side models that do not perform inference. From this point onward, the procedure can be carried out in the same manner as the first embodiment.

[0141] (3.4) Other Operation Examples 4 The first embodiment is also applicable to a two-side model in which inference is performed on both the UE 100 and the network device. When the network device is the network node 200, the operation example shown in Figure 11 is applicable. In this case, the network node 200 receives not only model information but also the AI / ML model from the OTT server (step S11). Furthermore, the AI / ML model is started not only on the UE 100 (step S17) but also on the network node 200. The rest can be carried out in the same manner as the first embodiment.

[0142] (3.5) Other Operation Examples 5 In the first embodiment, an example was described in which request information is sent targeting the functions of an AI / ML model, but request information may also be sent targeting a use case or a sub-use case. In the first embodiment, this can also be implemented in the case of a use case or a sub-use case by replacing "function" with "use case" (or "sub-use case"), "function #1" with "use case #1" (or "sub-use case #1"), and "function #2" with "use case #2" (or "sub-use case #2").

[0143] (3.6) Other Operation Examples 6 In the first embodiment, a network node 200 was used as an example of a network device, but the network device may be a CN device 300. In this case, the first embodiment can be implemented by replacing "network node 200" with "CN device". If the CN device is an AMF, NAS messages may be used between the AMF and the UE 100.

[0144] Alternatively, the network device may be an LMF. In this case, the first embodiment can be implemented by replacing "network node 200" with "LMF". LPP messages using LPP (LTE Positioning Protocol) may be used between the LMF and the UE 100.

[0145] Alternatively, the network device may be an OTT server. In this case, the first embodiment can be implemented by replacing "network node 200" with "OTT server". IP messages using the IP (Internet Protocol) protocol may be used between the OTT server and the UE 100.

[0146] (3.7) Other Operational Examples 7 In the first embodiment, an example was described in which messages between the UE 100 and the network node 200 are transmitted using RRC messages, MAC CE, or UCI (or DCI). However, at least a portion of such messages may be new messages (e.g., AI / ML layer messages) for a newly defined layer (e.g., AI / ML layer) for the AI / ML model.

[0147] [Other Embodiments] The above-described operation flows are not limited to being performed separately and independently; two or more operation flows can be combined and performed. 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. Also, the order of steps in each flow may be changed as appropriate.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] This application claims priority to Japanese Patent Application No. 2025-018692 (filed on February 6, 2025), the entirety of which is incorporated into the specification of this application.

[0155] (Note) The above embodiments can be summarized as shown in the note, but the note does not limit the embodiments.

[0156] (Note 1) A communication method in a mobile communication system, comprising the steps of: a user device transmitting a first message to a network device, which includes request information requesting either the activation of a first function in an AI / ML model or switching to the first function in the AI / ML model; and the user device receiving a second message from the network device, which includes result information indicating the result of the request information. (Note 2) The communication method according to Note 1, further comprising the step of the user device activating a second function different from the first function in the AI / ML model, wherein the transmitting step includes transmitting the first message, which includes the request information along with the inference result for the second function of the AI / ML model, to the network device. (Note 3) The communication method according to Note 1 or Note 2, wherein the request information includes identification information of the first function. (Note 4) The communication method according to any one of Notes 1 to 3, further comprising the step of the user device receiving a third message from the network device, which includes transmission permission information permitting the transmission of the request information. (Note 5) A user device in a mobile communication system, comprising: a transmitting unit that transmits a first message to a network device that includes request information requesting either the activation of a first function of an AI / ML model or switching to the first function of the AI / ML model; and a receiving unit that receives a second message from the network device that includes result information indicating the result of the request information.

[0157] 1: Mobile communication system 20: RAN 30: CN 100: UE 110: Receiving unit 120: Transmitting unit 130: Control unit 200: Network node 210: Transmitting unit 220: Receiving unit 230: Control unit 240: NW communication unit 300: CN device

Claims

1. A communication method in a mobile communication system, comprising: a user device transmitting a first message to a network device containing request information requesting either the activation of a first function in an AI (Artificial Intelligence) / ML (Machine Learning) model or switching to the first function in the AI / ML model; and the user device receiving a second message from the network device containing result information indicating the result of the request information.

2. The communication method according to claim 1, further comprising the user device activating a second function different from the first function in the AI / ML model, wherein the transmission includes transmitting the first message, which includes the request information along with the inference result for the second function of the AI / ML model, to the network device.

3. The communication method according to claim 1, wherein the request information includes identification information of the first function.

4. The communication method according to claim 1, further comprising the user device receiving a third message from the network device, which includes transmission permission information that permits the transmission of the request information.

5. User device in a mobile communication system, comprising: a transmitting unit that transmits a first message to a network device containing request information requesting either the activation of a first function of an AI / ML model or switching to the first function of the AI / ML model; and a receiving unit that receives a second message from the network device containing result information indicating the result of the request information.

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