Communication method and network device

Abstract

A communication method according to one aspect is performed in a mobile communication system. The communication method comprises a step in which a network device transmits, to a user device, monitoring data transmission setting information indicating that monitoring data representing a monitoring result of an artificial intelligence (AI) / machine learning (ML) model is transmitted at different periods according to an inference accuracy of the AI / ML model.

Description

Communication Method and Network Device

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

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

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

[0004] The communication method according to the first aspect is a communication method in a mobile communication system. The communication method includes a step in which a network device transmits monitoring data transmission setting information indicating that the network device transmits monitoring data representing the monitoring result of an AI / ML model at different periods according to the inference accuracy of the AI / ML model to a user device.

[0005] The network device according to the second aspect is a network device in a mobile communication system. The network device includes a transmission unit that transmits monitoring data transmission setting information indicating that the network device transmits monitoring data representing the monitoring result of an AI / ML model at different periods according to the inference accuracy of the AI / ML model to a user 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) is a diagram showing an example configuration of a functional block of a mobile communication system according to the first embodiment, and Figure 7(B) is a diagram showing an example configuration of a functional block of a UE according to the first embodiment. Figure 8 is a diagram showing an example configuration of a functional block of a mobile communication system according to the first embodiment. Figures 9(A) and 9(B) are diagrams showing examples configuration of functional blocks of a mobile communication system according to the first embodiment. Figures 10(A) and 10(B) are diagrams showing examples configuration of functional blocks of a mobile communication system according to the first embodiment. Figure 11 is a diagram showing a first operation example according to the first embodiment. Figure 12 is a diagram showing a second operation example according to the first embodiment. Figure 13 is a diagram showing a second example of operation according to the first embodiment.

[0007] This disclosure aims to enable user devices to transmit monitoring data at appropriate times.

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

[0009] [First Embodiment] The configuration of the mobile communication system according to the first embodiment will now be described. Figure 1 is a diagram showing an example of the configuration of the mobile communication system 1 according to the first embodiment. The mobile communication system 1 conforms to the 5th generation system (5GS) of the 3GPP standard. In the following description, 5GS will be used as an example, but the mobile communication system may also have an LTE (Long Term Evolution) system applied to it at least partially. The mobile communication system may also have a 6th generation (6G) system or later system applied to it at least partially.

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

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

[0012] RAN20 includes 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 (Distributed 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 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 on the physical downlink control channel (PDCCH). Specifically, UE100 performs blind decoding of the PDCCH using a Radio Network Temporary Identifier (RNTI) and acquires the successfully decoded DCI as the DCI addressed to its own UE. The DCI transmitted from network node 200 has a CRC (Cyclic Redundancy Code) parity bit, which is scrambled by the RNTI, added to it.

[0029] In NR, UE100 can use a bandwidth narrower than the system bandwidth (i.e., the cell bandwidth). Network node 200 configures UE100 with a bandwidth portion (BWP) consisting of consecutive PRBs (Physical Resource Blocks). UE100 sends and receives data and control signals in the active BWP. For example, up to four BWPs may be configured for UE100. Each BWP may have a different subcarrier spacing. The frequencies of these BWPs may overlap. If multiple BWPs are configured for UE100, network node 200 can specify which BWP to apply by controlling the downlink. This allows network node 200 to dynamically adjust the UE bandwidth according to the amount of data traffic on UE100, thereby reducing UE power consumption.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0044] Note that data collection (Data collection) may be, for example, the process of collecting data in a network node, a management entity, or the UE 100 for performing learning of an AI / ML model, management of an AI / ML model, and inference of an AI / ML model.

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

[0046] AI / ML model training is a process of training an AI / ML model from the relationship between inputs and outputs to obtain a trained AI / ML model for inference. For example, considering y = ax + b, the process of optimizing a (slope) and b (intercept) by giving an input (x) and an output (y) (that is, by providing 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 that uses correct data for training data. Unsupervised learning is a method that does not use correct data for training data. For example, in unsupervised learning, feature points are memorized from a large amount of training data, and the correct judgment (estimation of the range) is made. Reinforcement learning is a method of learning a method of attaching a score to an output result and maximizing the score. Hereinafter, supervised learning will be described, but as machine learning, unsupervised learning or reinforcement learning may be applied.

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

[0049] Note that hereinafter, AI / ML model training may be referred to as "model training" or "training".

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

[0051] The model inference unit A3 outputs inference output data to the management unit A5. The model inference unit A3 also receives management instructions from the management unit A5. For example, management instructions include selecting an AI / ML model, activating (deactivating) an AI / ML model, switching between AI / ML models, and fallback (performing inference without using an AI / ML model). The model inference unit A3 performs model inference according to the management instructions.

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

[0053] In the following text, an AI / ML model that is being trained (or updated) may be referred to as a "Training AI / ML model" (or "Updating AI / ML Model"). Also, in the following text, if there is no distinction between an AI / ML model being trained (or updated) and an AI / ML model that has been trained (or updated), it may simply be referred to as an "AI / ML model."

[0054] The management unit A5 supervises operations on the AI / ML model (selection, activation, deactivation, switching, fallback, etc.). The management unit A5 also supervises monitoring of the AI / ML model. Based on monitoring data and inference output data, the management unit A5 can also perform actions to ensure appropriate inference operation. Therefore, the management unit A5 outputs a Model Transfer / Delivery Request to the Model Recording Unit A6, and causes the trained (or updated) AI / ML model recorded in the Model Recording Unit A6 to output to the Model Inference Unit A3. Furthermore, the management unit A5 outputs management instructions to the Model Inference Unit A3, supervising operations on the AI / ML model. Furthermore, the management unit A5 can output performance feedback and retraining requests to the model learning unit A2, which can then retrain the AI / ML model (i.e., update the trained AI / ML model).

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

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

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

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

[0059] (X1.1) CSI Feedback Improvement "CSI feedback improvement" describes a use case where AI / ML technology is applied to the CSI that is fed back from UE100 to network node 200. CSI is information about the channel status in the downlink between UE100 and network node 200. CSI includes at least one of the following: Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), and Rank Indicator (RI). Network node 200 performs, for example, downlink scheduling based on the CSI feedback from UE100.

[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 pre-processing unit may be provided before the CSI generation inference unit 1010, and a post-processing unit may be provided after the CSI reconstruction inference unit 2011.

[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] (X1.1.2) 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, or 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 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).

[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 values ​​of beamset B, which are the input to the AI / ML model 103, may be represented by 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, either the UE side model or the NW side model may be applied. In the case of BM case 2 as well, the AI / ML model 103 is located in either UE 100 or NW 10.

[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, and 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 or in the NW 10.

[0079] In the case of direct AI / ML positioning, the input to the AI / ML model 104 is the measured value at each measurement point (TRP: Transmission and / or Reception Point). The measured value can be, for example, a Channel Impulse Response (CIR), a Power Delay Profile (PDP), or a fingerprint. For example, both CIR and PDP represent the delay time for a signal at a specific frequency, but CIR represents the instantaneous delay time, while PDP represents the statistical delay time. On the other hand, the output from the AI / ML model 104 (inference output data) is the position information of the UE 100. The position information may also be represented by a fingerprint. The fingerprint represents, for example, the measurement information for the cell of the UE 100.

[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 or in the NW 10. In the case of AI / ML assisted positioning, there are cases in which one AI / ML model 105 is used for multiple inputs (Figure 9(B)), cases in which the same AI / ML model is used for each of the multiple inputs (Figure 10(A)), and cases in which different AI / ML models are used for each of the multiple inputs (Figure 10(B)).

[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 AI / ML models.

[0083] The LCM of an AI / ML model may specifically 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

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

[0094] 3GPP specifies two types of LCM: function-based LCM and model ID-based LCM.

[0095] 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.

[0096] 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.

[0097] (Communication method according to the first embodiment) One of the LCM operations is monitoring. By monitoring the inference results of the AI / ML model, for example, the network 10 can grasp the inference accuracy of the AI / ML model and, if necessary, perform appropriate LCM operations such as retraining the AI / ML model, switching to another AI / ML model, or performing fallback of the AI / ML model. Through such control, appropriate management of the AI / ML model is performed, improving the inference accuracy of the AI / ML model and enabling the accurate provision of various services using the AI / ML model to the user.

[0098] For example, consider the following scenario: AI / ML model inference is performed on UE100 (i.e., UE side model). UE100 also monitors the AI / ML model and provides monitoring data representing the monitoring results to the network device. The monitoring data is transmitted at predetermined intervals (e.g., 10 seconds). The network device checks the monitoring data and performs LCM operation.

[0099] One of the evaluation metrics used to assess the inference accuracy of an AI / ML model is the KPI (Key Performance Indicator). In cases like the one described above, if the KPI of the AI / ML model falls below the KPI threshold (inference accuracy threshold), that is, if the inference accuracy of the AI / ML model deteriorates, the network 10 may want to take control of the AI / ML model as quickly as possible (LCM operations such as retraining, switching, and fallback).

[0100] However, if the transmission of monitoring data is fixed at a predetermined interval, monitoring data when inference accuracy decreases will also be transmitted at the same interval as when inference accuracy is good. Therefore, even though the network device wants to quickly acquire monitoring data when inference accuracy decreases, it will acquire the monitoring data at the same interval as when inference accuracy is good, and may not be able to acquire monitoring data when inference accuracy decreases at the appropriate time. In this case, the network 10 may not be able to perform LCM operation on the AI / ML model at the appropriate time, and may not be able to properly manage the AI / ML model.

[0101] Therefore, the objective of the first embodiment is to enable the UE100 to appropriately transmit monitoring data even when the inference accuracy of the AI / ML model decreases.

[0102] Therefore, in the first embodiment, the network device sends monitoring data transmission setting information to the user device (e.g., UE100) indicating that it will transmit monitoring data representing the monitoring results of the AI / ML model at different intervals depending on the inference accuracy of the AI / ML model.

[0103] This allows the UE100 to transmit monitoring data at any interval, so for example, if the inference accuracy of the AI / ML model decreases, it becomes possible to transmit monitoring data at a shorter interval than the predetermined interval. Therefore, even if the inference accuracy of the AI / ML model decreases, the UE100 can transmit monitoring data appropriately.

[0104] The following describes an example of operation according to the first embodiment. The example of operation according to the first embodiment will be described in two parts: a first example of operation and a second example of operation.

[0105] (First Operation Example According to the First Embodiment) First, a first operation example according to the first embodiment will be described. In the first operation example, an example will be described in which the network node 200 specifies to the UE 100 each time, using monitoring data transmission setting information, how often to transmit monitoring data.

[0106] Figure 11 is a diagram showing a first operation example according to the first embodiment. In the example shown in Figure 11, a network node 200 is used as the network device. Furthermore, the example shown in Figure 11 represents an example in which inference is performed in UE 100 (UE side model). In addition, the example shown in Figure 11 represents an example in which monitoring is performed in UE 100 (UE side monitoring).

[0107] As shown in Figure 11, in step S10, the NW communication unit 240 of the network node 200 receives an AI / ML model from the OTT server. This AI / ML model is a trained AI / ML model created by training on the OTT server.

[0108] In step S11, the NW communication unit 240 of the network node 200 receives information about the AI / ML model from the OTT server. This information may include, for example, the creation date and time of the AI / ML model, the type of input data (inference data) used during inference of the AI / ML model, the type of inference output data representing the inference result, and the KPI threshold (inference accuracy threshold). Steps S10 and S11 may be transmitted at the same time with the same message.

[0109] In step S13, the transmitting unit 210 of the network node 200 forwards the AI / ML model received in step S10 to the UE 100. The AI / ML model may be forwarded by an RRC message, a message of a newly defined layer for AI / ML (e.g., an AI / ML message), or a user plane message. The receiving unit 110 of the UE 100 receives the AI / ML model.

[0110] In step S14, the transmitting unit 210 of the network node 200 transmits an activation instruction for the AI / ML model transferred in step S13 to the UE 100. The activation instruction may include the model ID of the AI / ML model to be activated (step S13). The activation instruction may be given using an RRC message, MAC CE, a Downlink Control Information (DCI), or an AI / ML message. The activation instruction may be given simultaneously with the transfer of the AI / ML model (step S13). The transfer of the AI / ML model (step S13) may imply the activation instruction. The receiving unit 110 of the UE 100 receives the activation instruction.

[0111] In step S15, the transmitting unit 210 of the network node 200 sends a message to the UE 100 containing monitoring data transmission setting information (e.g., first monitoring data transmission setting information). The monitoring data transmission setting information in this step includes information indicating that monitoring data (e.g., first monitoring data) will be transmitted in the first cycle (e.g., 10 seconds). This message may be an RRC message or an AI / ML message. Alternatively, the monitoring data transmission setting information may be transmitted using downlink control information (DCI), MAC CE, or a data bearer (DRB). The same applies to subsequent transmissions of monitoring data transmission setting information. The receiving unit 110 of the UE 100 receives the monitoring data transmission setting information.

[0112] In step S16, the control unit 130 of the UE 100 starts inference of the AI / ML model received in step S13, in accordance with the startup instruction (step S14). The control unit 130 starts monitoring the inference accuracy of the AI / ML model, triggered by the start of AI / ML model inference. The monitoring results may be stored in the memory of the UE 100 as monitoring data.

[0113] In step S17, the transmitting unit 120 of UE 100 transmits inference output data representing the inference result of the AI / ML model (step S16) in which inference was initiated to the network node 200. The inference output data may be transmitted using RRC messages, AI / ML messages, or user plane messages. The transmission of inference output data thereafter is also done similarly. The receiving unit 220 of the network node 200 receives the inference output data.

[0114] In step S18, the transmitter 120 of UE 100 transmits monitoring data representing the monitoring results of the AI / ML model to the network node 200. The transmission of this monitoring data may be performed at the time the monitoring data is acquired. From then on, unless UE 100 receives monitoring data transmission setting information for a different cycle than the first cycle, the transmitter 120 of UE 100 will transmit monitoring data in the first cycle according to the monitoring data transmission setting information (step S15).

[0115] Firstly, the monitoring data may represent the inference accuracy of the AI / ML model. The monitoring data may also represent the evaluation results of the assessment of the inference accuracy. The inference accuracy or evaluation results may be expressed using the KPIs described above. The inference accuracy or evaluation results may be expressed using the Mean Absolute Error (MAE) or the Mean Squared Error (MSE). Alternatively, the monitoring data may represent the results of a comparison between the inference output data, which is the inference result of the AI / ML model, and actual values ​​obtained without using the AI / ML model (i.e., legacy data or ground truth data). Alternatively, the monitoring data may be expressed using evaluation metrics that represent the performance of the AI / ML model.

[0116] Secondly, monitoring data may be transmitted using RRC messages, AI / ML messages, or user plane messages. Alternatively, monitoring data may be transmitted using uplink control information (UCI), MAC CE, or data bearer (DRB). The same applies to subsequent transmissions of monitoring data.

[0117] The receiving unit 220 of the network node 200 receives monitoring data.

[0118] In step S19, the control unit 230 of the network node 200 compares the monitoring data (step S18) with the inference accuracy threshold to detect whether the monitoring data is less than the inference accuracy threshold, that is, whether the inference accuracy of the AI / ML model has decreased. In the following description, it is assumed that the monitoring data includes KPI as a monitoring result of inference accuracy. Furthermore, in the following description, it is assumed that the KPI threshold is used as the inference accuracy threshold. That is, the control unit 230 detects whether the KPI, which is the monitoring result, is less than the KPI threshold, and whether the KPI of the AI / ML model has decreased. In this step, it is assumed that the control unit 230 has detected that the KPI included in the monitoring data is greater than or equal to the KPI threshold, that is, that the KPI has not decreased. By detecting that the KPI has not decreased, the control unit 230 can confirm that the inference accuracy of the AI / ML model has not decreased. The control unit 230 continues to transmit the monitoring data for the first cycle without changing the cycle. It is assumed that the KPI threshold is stored in the memory of the network node 200.

[0119] In step S20, the transmitter 120 of UE 100 transmits the inference output data of the AI / ML model to the network node 200. Also, in step S21, the transmitter 120 of UE 100 transmits monitoring data (e.g., first monitoring data) representing the results of monitoring the inference results of the AI / ML model (step S20) in the first cycle, according to the monitoring data transmission setting information (step S15). The receiver 220 of network node 200 receives the inference output data and the monitoring data.

[0120] In step S22, the control unit 230 of the network node 200 compares the monitoring data (KPI) received in step S21 with the KPI threshold. Here, the control unit 230 detects that the KPI is less than the KPI threshold, that is, that the KPI of the AI / ML model has decreased.

[0121] In step S23, the transmitting unit 210 of the network node 200 detects that the inference accuracy for the AI / ML model inference is below the KPI threshold and transmits monitoring data transmission setting information (e.g., second monitoring data transmission setting information) to the UE 100. The monitoring data transmission setting information in this step includes information indicating that monitoring data (e.g., second monitoring data) will be transmitted in a second cycle (e.g., 1 second) that is shorter than the first cycle. The receiving unit 110 of the UE 100 receives the monitoring data transmission setting information.

[0122] In step S24, the transmitting unit 120 of UE 100 transmits the inference output data to the network node 200. The receiving unit 220 of the network node 200 receives the inference output data.

[0123] In step S25, the transmitting unit 120 of UE 100 transmits monitoring data in the second cycle according to the monitoring data transmission setting information (step S23). From then on, the transmitting unit 120 of UE 100 will transmit monitoring data in the second cycle unless UE 100 receives monitoring data transmission setting information for a different cycle than the second cycle. The receiving unit 220 of network node 200 receives the monitoring data.

[0124] In step S26, the control unit 230 of the network node 200 compares the monitoring data (KPI) received in step S25 with the KPI threshold to confirm that the KPI has decreased. It is expected that once a decrease in the KPI of the AI / ML model is detected (step S22), the decrease will continue. Therefore, it is expected that a decrease in the KPI will be detected again in step S26.

[0125] In step S27, the transmitting unit 210 of the network node 200 receives the monitoring data transmitted in the first cycle (step S25) and confirms that the KPI is less than the KPI threshold (step S26), and in response, instructs the UE 100 to fall back to the AI / ML model. The transmitting unit 210 sends the fallback instruction to the UE 100. A deactivation instruction may be used instead of a fallback instruction. The fallback instruction may be sent using an RRC message, an AI / ML message, MAC CE, or DCI. The receiving unit 110 of the UE 100 receives the fallback instruction.

[0126] Alternatively, instead of sending a fallback instruction, the network node 200 may send a switching instruction to the UE 100, assuming that the UE 100 has another AI / ML model.

[0127] In step S28, UE100 stops the AI / ML model inference in accordance with the fallback instruction.

[0128] (Another example of the first operation example) In step S26, if the control unit 230 of the network node 200 does not detect a decrease in the KPI of the AI / ML model, the transmission unit 210 of the network node 200 may send monitoring data transmission setting information to the UE 100 instructing it to transmit monitoring data in the first cycle. This is because it is possible that although the inference accuracy of the AI / ML model was below the inference accuracy threshold in step S22, the inference accuracy immediately thereafter became above the inference accuracy threshold, and a decrease in inference accuracy was not detected in step S26.

[0129] (Another example of the first operation example 2) In the example shown in Figure 11, the network node 200 is shown to issue a fallback instruction when it detects a decrease in inference accuracy twice (steps S22 and S26). However, for example, the fallback instruction may be issued when a decrease in inference accuracy is detected three or more times. Alternatively, the network node 200 may send a fallback instruction when it detects a decrease in inference accuracy for a certain period of time (continuously).

[0130] (Second Operation Example According to the First Embodiment) Next, a second operation example according to the first embodiment will be described. In the second operation example, the network node 200 sends monitoring data transmission setting information to the UE 100 in advance, and the UE 100 determines the monitoring result and transmits monitoring data at one of the intervals based on its own judgment.

[0131] Specifically, the monitoring data transmission setting information includes information indicating that monitoring data will be transmitted in the first cycle (e.g., 10 seconds) when the inference accuracy for the AI / ML model's inference is equal to or greater than the inference accuracy threshold, and in the second cycle (e.g., 1 second) with a shorter interval than the first cycle when the inference accuracy is less than the inference accuracy threshold. Alternatively, the monitoring data transmission setting information may include information indicating that data will be transmitted in the first cycle when the inference accuracy is detected to be equal to or greater than the inference accuracy threshold a certain number of times (consecutively), and when the inference accuracy is detected to be equal to or greater than the inference accuracy threshold for a certain period of time (continuously). Alternatively, the monitoring data transmission setting information may include information indicating that data will be transmitted in the second cycle when the inference accuracy is detected to be less than the inference accuracy threshold a certain number of times (consecutively), and when the inference accuracy is detected to be less than the inference accuracy threshold for a certain period of time (continuously).

[0132] For example, the UE100 can monitor the inference accuracy of an AI / ML model and detect a decrease in inference accuracy by comparing the inference accuracy with an inference accuracy threshold. Upon detecting this decrease, the UE100 can then transmit monitoring data in a second cycle, which has a shorter interval than the first cycle, according to the monitoring data transmission settings. Therefore, similar to the first example, if the inference accuracy of the AI / ML model decreases, monitoring data can be transmitted in the second cycle, allowing for transmission at a more appropriate timing compared to when the first cycle is fixed.

[0133] Figures 12 and 13 are diagrams illustrating a second operation example according to the first embodiment. Similar to the first operation example, Figures 12 and 13 use a network node 200 as an example of a network device and represent an example where inference is performed in UE 100 (UE side model). Also, Figures 12 and 13 represent an example where monitoring (UE side monitoring) is performed in UE 100. Note that in Figures 12 and 13, explanations may be omitted for parts that are the same as in the first operation example (Figure 11).

[0134] As shown in Figure 12, in step S30, the NW communication unit 240 of the network node 200 receives AI / ML model #A from the OTT server.

[0135] In step S31, the NW communication unit 240 of the network node 200 receives information about AI / ML model #A from the OTT server.

[0136] In step S32, the transmission unit 210 of the network node 200 forwards the AI / ML model #A received in step S30 to the UE 100.

[0137] In step S33, the transmission unit 210 of the network node 200 sends a startup instruction for AI / ML model #A to the UE 100.

[0138] In step S34, the transmission unit 210 of the network node 200 transmits monitoring data transmission setting information to the UE 100. The monitoring data transmission setting information includes information that instructs the transmission of monitoring data for AI / ML model #A at different intervals depending on the inference accuracy of AI / ML model #A. Specifically, the monitoring data transmission setting information includes information that instructs the transmission of monitoring data in the first period when the inference accuracy is equal to or greater than the inference accuracy threshold, and in the second period, which is shorter than the first period, when the inference accuracy is less than the inference accuracy threshold. Alternatively, the monitoring data transmission setting information may include information indicating that data should be transmitted in the first period if the inference accuracy is detected to be equal to or greater than the inference accuracy threshold a certain number of times (consecutively), and if the inference accuracy is detected to be equal to or greater than the inference accuracy threshold for a certain period of time (continuously). Alternatively, the monitoring data transmission setting information may include information indicating that data should be transmitted in the second period if the inference accuracy is detected to be less than the inference accuracy threshold a certain number of times (consecutively), and if the inference accuracy is detected to be less than the inference accuracy threshold for a certain period of time (continuously). Furthermore, the monitoring data transmission settings include an inference accuracy threshold. In the second example, as in the first example, KPI will be used as an example of inference accuracy, and the KPI threshold as an example of the inference accuracy threshold. Also, in the second example, as in the first example, the monitoring data will be explained assuming that KPI is included as a result of the inference accuracy. That is, the monitoring data transmission settings include information instructing the system to transmit monitoring data in the second cycle if the KPI of the AI / ML model has decreased, and in the first cycle if the KPI of the AI / ML model is good, and further includes a KPI threshold for detecting a decrease in KPI.

[0139] In step S35, the control unit 130 of the UE 100 starts inference for AI / ML model #A in accordance with the instruction to start AI / ML model #A (step S33). The control unit 130 starts monitoring the inference accuracy for AI / ML model #A, triggered by the start of inference. The monitoring results may be stored in the memory of the UE 100 as monitoring data.

[0140] In step S36, the transmission unit 120 of UE100 transmits the inference output data of AI / ML model #A to the network node 200.

[0141] In step S37, the control unit 130 of UE100 detects whether the monitoring data (KPI) is below the KPI threshold, that is, whether the KPI of AI / ML model #A has decreased. In this step, the control unit 130 will be described below assuming that the KPI has not decreased and is in good condition.

[0142] In step S38, the control unit 130 of the UE 100 detects that the KPI is good and sets the monitoring data transmission cycle to the first cycle (for example, 10 seconds) according to the monitoring data transmission setting information (step S34).

[0143] In step S39, the transmitting unit 120 of UE100 transmits monitoring data to the network node 200. Thereafter, the transmitting unit 120 of UE100 continues to transmit monitoring data in the first cycle according to the monitoring data transmission setting information (step S34), unless the control unit 130 detects that the KPI is below the KPI threshold.

[0144] In step S40, the transmission unit 120 of UE100 transmits the inference output data of AI / ML model #A to the network node 200.

[0145] In step S41, the control unit 130 of UE100 detects that the monitoring data (KPI) has fallen below the KPI threshold and that the KPI of AI / ML model #A has decreased.

[0146] In step S42, the control unit 130 of UE100 detects a decrease in the KPI of AI / ML model #A and sets the monitoring data transmission period to the second period (for example, 1 second) according to the monitoring data transmission setting information (step S34).

[0147] In step S43, the transmitting unit 120 of UE 100 transmits monitoring data to the network node 200 in the second cycle. The receiving unit 220 of the network node 200 receives the monitoring data.

[0148] In step S44, the control unit 230 of the network node 200 confirms that the KPIs included in the monitoring data are less than the KPI threshold.

[0149] In step S45 (Figure 13), the NW communication unit 240 of the network node 200 detects a decrease in the KPI of AI / ML model #A and sends a transmission request for AI / ML model #B, which is different from AI / ML model #A, to the OTT server.

[0150] The NW communication unit 240 of the network node 200 receives AI / ML model #B from the OTT server (step S46), and also receives information about AI / ML model #B from the OTT server (step S47).

[0151] Network node 200 transfers AI / ML model #B to UE 100 (step S48), and UE 100 sends the inference output data of AI / ML model #A to network node 200 (step S49). UE 100 again detects that the monitoring data (KPI) of AI / ML model #A is below the KPI threshold (step S50), and sends the monitoring data of AI / ML model #A to network node 200 in the first cycle (step S51).

[0152] Then, in step S52, the network node 200 confirms that the KPI is less than the KPI threshold based on the monitoring data (KPI) received in step S51, and confirms that AI / ML model #A is NG.

[0153] In step S53, the transmitting unit 210 of the network node 200 receives the monitoring data transmitted in the first cycle (step S51) and confirms that the KPI of AI / ML model #A is NG (step S52). In response, it sends a switching instruction to the UE 100 instructing it to switch the AI / ML model from AI / ML model #A to AI / ML model #B. The switching instruction may also be sent using an RRC message, an AI / ML message, MAC CE, or DCI, similar to the fallback instruction in the first operation example (step S27 in Figure 11). The receiving unit 110 of the UE 100 receives the switching instruction. The network node 200 may, instead of the switching instruction, instruct a fallback of AI / ML model #A, similar to the first operation example. A deactivation instruction may also be given instead of a fallback instruction.

[0154] In step S54, the control unit 130 of UE100 switches the AI / ML model from AI / ML model #A to AI / ML model #B in response to receiving an AI / ML model switching instruction.

[0155] (Other examples of the second operation example) In the examples shown in Figures 12 and 13, the network node 200 sends a switching instruction when it detects a decrease in inference accuracy twice (steps S44 and S52). However, for example, it may send a switching instruction when it detects a decrease in inference accuracy three or more times. Alternatively, the network node 200 may send a switching instruction when it detects a decrease in inference accuracy for a certain period of time (continuously).

[0156] (Another Operation Example 1 of the First Embodiment) In the first embodiment (first and second operation examples), examples with two types of periods (10 seconds and 1 second) were described, but there may be three or more types of periods. In this case, there will be multiple inference accuracy thresholds (KPI thresholds) depending on the type of period. The monitoring data transmission setting information related to the second operation example will include information indicating that monitoring data will be transmitted at different periods depending on each inference accuracy threshold. The monitoring data transmission setting information related to the first operation example will initially include information indicating that training data will be transmitted at the first period (step S15). Then, the network node 200 may transmit monitoring data transmission setting information indicating that monitoring data will be transmitted at a second period shorter than the first period, or monitoring data transmission setting information indicating that monitoring data will be transmitted at a third period shorter than the second period, depending on the comparison result between the monitoring data (KPI) and each inference accuracy threshold (each KPI threshold).

[0157] (Another Operation Example 2 According to the First Embodiment) In the first embodiment (first and second operation examples), 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, in Figures 11 to 13, the network node 200 may be replaced with a CN device. If the CN device is an AMF, NAS messages may be used for messages between the UE 100 and the AMF.

[0158] Alternatively, an LMF may be used as an example of a network device. In this case, the LMF may be used instead of the network node 200 in Figures 11 to 13. LPP messages may be used for communication between the UE100 and the LMF.

[0159] Alternatively, in the examples shown in Figures 11 to 13, an OTT server may be used instead of the network node 200. In this case, IP messages using the IP (Internet Protocol) protocol may be used between the UE 100 and the OTT server.

[0160] Alternatively, UE100 may be connected via DC (Dual Connectivity) to two cells: a master cell belonging to MCG (Master Cell Group) and a secondary cell belonging to SCG (Secondary Cell Group). In this case, the network node 200 on the master cell side performs the same processing as in the first embodiment (first and second operation examples). On the secondary cell side, the same processing as in the first embodiment is performed between UE100 and the network node 200 on the secondary cell side, except that the transfer of the AI / ML model (steps S13 and S32) and the startup instruction for the AI / ML model (steps S14 and S33) are performed on the master cell side. UE100 can transmit monitoring data to the network node 200 on the secondary cell side according to the monitoring data transmission setting information transmitted from the network node 200 on the secondary cell side.

[0161] [Other Embodiments] The first embodiment described above mainly describes supervised learning, but is not limited thereto. For example, the first embodiment may be applied to unsupervised learning or reinforcement learning.

[0162] 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.

[0163] In the above embodiment, an example was described in which 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. Additionally, 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.

[0164] 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.

[0165] A program is provided that causes a computer to execute each of the processes according to the above embodiment. 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 storage medium. The non-transient storage medium is not particularly limited, but for example, it may be a storage medium such as a CD-ROM and / or DVD-ROM. Furthermore, the circuits that execute each of the processes performed by the apparatus according to the above embodiment may be integrated, and at least a part of the apparatus may be configured as a semiconductor integrated circuit (chipset, SoC: System on a chip).

[0166] The functions realized by the apparatus according to the above-described embodiment 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 circuits, and / or combinations thereof, which are programmed to realize the described functions. The processor includes transistors and / or other circuits and is considered to be a circuit or processing circuit. The processor may be a programmed processor that executes a program stored in memory. In this disclosure, circuit, unit, and means are hardware programmed to realize or execute the described functions. The hardware may be any hardware disclosed herein, or any hardware known to be programmed to perform or execute the functions described herein. If the hardware is a processor that is considered to be of the type of circuit, the circuit, means, or unit is a combination of hardware and software used to constitute the hardware and / or processor.

[0167] 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.

[0168] 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 work. Furthermore, each embodiment, each operation example, or each process can be combined as appropriate, as long as they do not contradict each other.

[0169] This application claims priority to Japanese Patent Application No. 2024-171562 (filed September 30, 2024), the entirety of which is incorporated into the specification of this application.

[0170] (Note) The above can be summarized as stated in the note, but the note does not limit the embodiments.

[0171] (Note 1) A communication method in a mobile communication system, comprising the step of transmitting monitoring data transmission setting information to a user device, indicating that a network device transmits monitoring data representing the monitoring results of the AI / ML model at different intervals according to the inference accuracy of the AI / ML model.

[0172] (Note 2) The communication method according to Note 1, wherein the transmission step includes: transmitting first monitoring data transmission setting information to the user device indicating that the network device transmits first monitoring data in a first cycle; and transmitting second monitoring data transmission setting information to the user device indicating that the network device transmits second monitoring data in a second cycle with a shorter interval than the first cycle, in response to detecting that the inference accuracy for the inference of the AI / ML model is less than an inference accuracy threshold.

[0173] (Note 3) The inference accuracy is the communication method described in Note 1 or Note 2 included in the first monitoring data.

[0174] (Note 4) The communication method according to any one of Notes 1 to 3, wherein the monitoring data transmission setting information includes information indicating that the monitoring data is transmitted in the first cycle when the inference accuracy is equal to or greater than the inference accuracy threshold, and in the second cycle with a shorter interval than the first cycle when the inference accuracy is less than the inference accuracy threshold.

[0175] (Note 5) The communication method described in any of Notes 1 to 4, which includes the inference accuracy threshold, for the monitoring data transmission setting information.

[0176] (Note 6) The detection of the inference accuracy of the AI / ML model is performed in the user device using the communication method described in any one of Notes 1 to 5.

[0177] (Note 7) The communication method according to any one of Notes 1 to 6, further comprising the step of instructing the user device to either fall back the AI / ML model or switch the AI / ML model in response to the network device receiving the monitoring data transmitted in the second cycle.

[0178] (Note 8) A network device in a mobile communication system, comprising a transmitting unit that transmits monitoring data transmission setting information to a user device, indicating that it transmits monitoring data representing the monitoring results of the AI / ML model at different intervals depending on the inference accuracy of the AI / ML model.

[0179] 1: Mobile communication system 10: NW 20: RAN 30: CN 100: UE 101: CSI generation unit 102: CSI prediction model 103: AI / ML model (inference unit) 104: AI / ML model (inference unit) 105: AI / ML model (inference unit) 110: Receiving unit 120: Transmitting unit 130: Control unit 140: Communication unit 200: Network node 210: Transmitting unit 220: Receiving unit 230: Control unit 240: NW communication unit 250: Communication unit 300: CN device A1: Data acquisition unit A2: Model learning unit A3: Model inference unit A5: Management unit A6: Model recording unit 1010: Inference unit for CSI generation 1011: Quantization unit 2010: Inverse quantization unit 2011: Reasoning unit for CSI reconstruction

Claims

1. A communication method in a mobile communication system, comprising: a network device transmitting monitoring data transmission setting information to a user device indicating that the network device transmits monitoring data representing the monitoring results of the AI ​​(Artificial Intelligence) / ML (Machine Learning) model at different intervals depending on the inference accuracy of the AI / ML model.

2. The communication method according to claim 1, wherein the transmission includes: the network device transmitting first monitoring data transmission setting information to the user device indicating that it transmits first monitoring data in a first cycle; and the network device transmitting second monitoring data transmission setting information to the user device indicating that it transmits second monitoring data in a second cycle with a shorter interval than the first cycle, in response to detecting that the inference accuracy for the inference of the AI / ML model is less than an inference accuracy threshold.

3. The communication method according to claim 2, wherein the inference accuracy is included in the first monitoring data.

4. The communication method according to claim 1, wherein the monitoring data transmission setting information includes information indicating that the monitoring data is transmitted in a first cycle when the inference accuracy is equal to or greater than the inference accuracy threshold, and in a second cycle with a shorter interval than the first cycle when the inference accuracy is less than the inference accuracy threshold.

5. The communication method according to claim 4, wherein the monitoring data transmission setting information includes the inference accuracy threshold.

6. The communication method according to claim 4, wherein the detection of the inference accuracy of the AI / ML model is performed in the user device.

7. The communication method according to claim 2 or 4, further comprising the network device instructing the user device to either fall back the AI / ML model or switch the AI / ML model in response to receiving the monitoring data transmitted in the second cycle.

8. A network device in a mobile communication system, comprising a transmitting unit that transmits monitoring data transmission setting information to a user device, indicating that it transmits monitoring data representing the monitoring results of the AI ​​(Artificial Intelligence) / ML (Machine Learning) model at different intervals depending on the inference accuracy of the AI / ML model.

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