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 receives, from a network device, a first message including a report transmission condition that indicates a condition for transmitting a report regarding low power to the network device. The communication method also comprises a step in which the user device transmits the report regarding low power to the network device when the report transmission condition is satisfied.

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; hereinafter the same), 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 receives a first message including a report transmission condition indicating a condition for transmitting a report related to low power to a network device from the network device. Further, the communication method includes a step in which the user equipment transmits a report related to low power to the network device when the report transmission condition is met.

[0005] The user equipment according to the second aspect is a user equipment in a mobile communication system. The user equipment includes a receiving unit that receives a first message including a report transmission condition indicating a condition for transmitting a report related to low power to a network device from the network device. Further, the user equipment includes a transmitting unit that transmits a report related to low power to the network device when the report transmission condition is met.

[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 illustrating another example of operation according to the first embodiment.

[0007] This disclosure aims to provide a communication method and user device that can automatically stop data collection when the user device enters a low-power state.

[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 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 added, which has been scrambled by RNTI.

[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 each BWP 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 (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 (Evolved Packet Core), an 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 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 may be, for example, a process of collecting data in a network node, a management entity, or UE 100 to perform 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 data for training data. Unsupervised learning is a method of not using correct data for training data. For example, in unsupervised learning, feature points are memorized from a large amount of training data and a correct judgment (estimation of a 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 unsupervised learning may be applied as the machine learning. Reinforcement learning may be applied as the 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] Note that hereinafter, AI / ML model learning may be referred to as "model learning" or "learning".

[0050] The model inference unit A3 performs AI / ML model inference. Specifically, the model inference unit A3 applies the inference data provided by the data collection unit A1 to the trained AI / ML model (or updated AI / ML model) to obtain inference output data. For example, considering y = ax + b, x is 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, 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, management instructions include selection of the AI / ML model, activation (deactivation) of the AI / ML model, 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] In the mobile communication system 1 using an AI / ML model, data collection may be performed, for example, as shown in Figure 6. Data collection may be performed for various purposes, such as model inference (i.e., inference), model monitoring, model selection, and / or model updating. For example, in inference, the data collected during data collection can be input into the AI / ML model as inference data, and inference can be performed in the AI / ML model.

[0098] Regarding data collection, 3GPP has agreed that data collection will be controlled by network 10, and that UE100 will not automatically stop even if it detects a low power state. It has also been agreed that UE100 can report to the network if it is in a low power state.

[0099] From the perspective of network 10, by not automatically stopping data collection when it is being performed on UE 100, control of network 10 can be maintained while data collection continues, and as a result, control of network 10 can be thoroughly enforced. In addition, in the case of a network-side model, the data collected by UE 100 can be used for inference in the AI / ML model of the network device, and continuous inference can be used to thoroughly enforce control of the network.

[0100] On the other hand, from the UE100's perspective, if data collection cannot be automatically stopped even when the UE100's battery level falls below a certain point, the power may turn off at unintended times. It is not necessarily desirable for the UE100 to turn off even when it is not using an application.

[0101] Therefore, the objective of the first embodiment is to enable the UE100 to automatically stop data acquisition when it enters a low-power state.

[0102] Therefore, in the first embodiment, if the UE100 meets certain conditions, it sends a report regarding low power consumption to the network device.

[0103] Specifically, firstly, the user device (e.g., UE100) receives a first message from the network device that includes report transmission conditions indicating the conditions for sending a low power report to the network device. Secondly, if the report transmission conditions are met, the user device sends a low power report to the network device.

[0104] Thus, since the UE100 can transmit reports regarding low power consumption, it is expected that network devices will respond to these reports by instructing it to stop data collection. The UE100 can then automatically stop data collection in accordance with these instructions. Therefore, the UE100 can automatically stop data collection when it enters a low power state.

[0105] (Examples of operation according to the first embodiment) Next, examples of operation according to the first embodiment will be described. The examples of operation according to the first embodiment include a first example of operation and a second example of operation. In addition, the examples of operation according to the first embodiment include common examples that are common to both the first example of operation and the second example of operation. First, the common examples of operation will be described, and then the first example of operation and the second example of operation will be described.

[0106] (1) Common Operation Example Figure 11 is a diagram showing an example of operation according to the first embodiment. Of Figure 11, steps S10 to S18 represent the common operation example. The common operation example will be described below.

[0107] As shown in Figure 11, in step S10, the receiving unit 110 of the UE 100 receives model information of the AI / ML model from the OTT (Over The Top) server. The model information indicates model information relating to the AI / ML model (step S11) transmitted from the OTT server to the network node 200. The model information may also include information indicating what kind of information is used for input and output to the AI / ML model. The model information may also include identification information of the AI / ML model (e.g., model ID).

[0108] In step S11, the NW communication unit 240 of the network node 200 receives the AI / ML model and model information transmitted from the OTT server. The AI / ML model may be a trained AI / ML model. The AI / ML model may also be a training AI / ML model. The model information may be the same as the model information transmitted to the UE 100 (step S10).

[0109] 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.

[0110] 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.

[0111] 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 inquiring whether the functions of the AI / ML model held by the UE 100 are applicable. Alternatively, the RRC reconfiguration message may include query information specifying the functions of the AI / ML model and inquiring whether those functions are applicable. In the network 10, even if the UE 100 does not hold an AI / ML model, it is possible to ascertain the applicability of the functions by sending this query information. The receiving unit 110 of the UE 100 receives the RRC reconfiguration message.

[0112] In step S15, the transmission unit 120 of UE 100 transmits Applicable Function Reporting information to the network node 200 in response to receiving the RRC reset message. 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. Applicable or non-applicable functions do not necessarily have to be AI / ML models held by UE100. UE100 may determine which functions are applicable or non-applicable based on its processing capabilities and transmit applicable function report information. The applicable function report information may also be an RRC message. This applicable function report information may be included in the RRC message and transmitted. Alternatively, the applicable function report information may be included in MAC CE or UCI (Uplink Control Information) and transmitted. The receiving unit 220 of the network node 200 receives the applicable function report information.

[0113] 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 an operation related to the LCM. In the first example of operation, it is explained that an instruction for data collection is included as one of the instructions for operation related to the LCM. In this case, the instruction information may include information about the type of data to be collected. Alternatively, the instruction information may include information about the data collection period. The receiving unit 110 of the UE 100 receives the RRC reset message.

[0114] In step S17, the control unit 130 of UE100 starts collecting data as instructed by the instruction information in response to receiving the RRC reset message (step S16).

[0115] On the other hand, in step S18, the control unit 230 of the network node 200 starts the AI / ML model received in step S11 and begins inference of the AI / ML model. Alternatively, the control unit 230 may start (re)training of the AI / ML model. The control unit 230 may also start monitoring the AI / ML model. In the following description, it will be assumed that inference of the AI / ML model has started.

[0116] In step S19, the transmitting unit 120 of UE 100 sends an RRC Reconfiguration Complete message to the network node 200 in response to the start of data collection. The RRC Reconfiguration Complete message may include information indicating that data collection has started as instructed by the instruction information (step S16). The receiving unit 220 of the network node 200 receives the RRC Reconfiguration Complete message.

[0117] (1.1) First Operation Example The first operation example is an example in which data collection automatic stop permission information, indicating that it is acceptable to automatically stop data collection of data used in the AI / ML model, is sent from the network node 200 to the UE 100.

[0118] UE100 can automatically stop data collection by receiving permission information to automatically stop data collection. This also aligns with the agreement that data collection is controlled by network 10, as the permission information is transmitted from network 10. Furthermore, since UE100 does not automatically stop data collection before receiving permission information to automatically stop data collection, it also aligns with the agreement that UE100 will not automatically stop even if it detects a low power state. In this way, UE100 can automatically stop data collection in a manner that conforms to the agreed-upon conditions.

[0119] The data collection automatic stop permission information may be included in the RRC reset message in step S16 (an example of the first message). Alternatively, the data collection automatic stop permission information may be included in a message of a newly defined layer as an AI / ML layer (for example, an AI / ML layer message) (an example of the first message).

[0120] Firstly, the data collection automatic stop permission information may include a time interval during which the UE100 checks the power. The UE100 may check the power at this time interval and confirm a low power state. The time interval may include not just one but multiple time intervals. The multiple time intervals may gradually shorten, for example, 10 minutes, 5 minutes, 3 minutes, etc. For example, the UE100 can check the power at time intervals that gradually shorten as the remaining battery capacity decreases.

[0121] Secondly, the data collection automatic stop permission information may also be information indicating that UE 100 may automatically stop data collection at any time. In this case, UE 100 can automatically stop data collection at any time in response to receiving the data collection automatic stop permission information. Alternatively, the data collection automatic stop permission information may include a restart instruction indicating that data collection may be resumed after it has been automatically stopped.

[0122] Thirdly, the data collection automatic stop permission information may include stop conditions that indicate the conditions under which data collection will be automatically stopped. The stop conditions may include the remaining battery time of the UE100. For example, if the stop condition is "10 minutes," the UE100 will stop data collection when the remaining battery time reaches "10 minutes." Alternatively, the stop conditions may include the remaining battery capacity of the UE100. The remaining capacity may be expressed as a percentage of the battery's remaining capacity. For example, if the stop condition is "10% battery capacity," the UE100 will stop data collection when the remaining battery capacity reaches "10%." Furthermore, the data collection automatic stop permission information may include restart conditions that will resume data collection after it has been stopped. For example, if the restart condition is "battery capacity exceeds 10%," the UE100 will resume data collection when the remaining battery capacity exceeds "10%."

[0123] Fourth, the data collection automatic stop permission information may include information indicating the data to be stopped. This information may include, for example, information indicating the type of data to be stopped. There may be just one data item to be stopped, or there may be multiple data items to be stopped.

[0124] Fifth, the data collection automatic stop permission information may include configuration information indicating whether or not UE 100 sends a report (or automatic stop notification) indicating that data collection has been automatically stopped after it has been automatically stopped. If a report is sent, the configuration information may include report transmission timing information indicating whether the report is sent immediately after data collection is automatically stopped, or whether the report is sent after a predetermined period has elapsed. The predetermined period may be included in the configuration information. UE 100 can send a report to the network node 200 indicating that data collection has been automatically stopped, or it can choose not to send such a report, according to the configuration information. Alternatively, the data collection automatic stop permission information may include configuration information indicating whether or not to send a report indicating that data collection has been resumed after it has been automatically stopped.

[0125] Sixth, the data collection automatic stop permission information may include information indicating the period during which automatic stoppage will occur. In the UE100, data collection will resume after the specified period has elapsed following the automatic stoppage. Since data collection is stopped for a certain period in the UE100, it is possible to notify the user during that time to avoid situations where the power is turned off at an unexpected time for the user.

[0126] The receiving unit 110 of UE100 receives data collection automatic stop permission information transmitted from the network node 200 (for example, step S16).

[0127] In step S20, the control unit 130 of UE100 checks its own power.

[0128] In step S21, the control unit 130 of the UE100 detects a low power state. For example, the control unit 130 may detect a low power state when the power output from the battery built into the UE100 falls below a power threshold. Alternatively, the control unit 130 may detect a low power state when the remaining battery time falls below a battery time threshold. Alternatively, the control unit 130 may detect a low power state when the remaining battery capacity falls below a battery capacity threshold.

[0129] In step S22, the control unit 130 of the UE 100 checks whether the conditions for automatically stopping data acquisition have been met, based on the detection of a low-power state. If the conditions for stopping are met (YES in step S22), the process proceeds to step S23. On the other hand, if the conditions for stopping are not met (NO in step S22), the process proceeds to step S25.

[0130] In step S23, the transmission unit 120 of UE100 automatically stops data collection when the stop condition is met.

[0131] In step S24, the transmission unit 120 of UE 100 sends a report to the network node 200 containing information indicating that data collection has been automatically stopped. This report may be an automatic stop notification regarding data collection. Alternatively, this report may be a report regarding low power. This report may include information indicating that UE 100 is in a low power state. This report may also include information indicating the reason why UE 100 entered a low power state. Furthermore, this report may include information regarding the data for which data collection was stopped. This information may include the type of data for which data collection was automatically stopped, or the duration of the stoppage. This report may be transmitted using an RRC message, MAC CE, or UCI.

[0132] In step S25, the control unit 130 of UE100 does not automatically stop data acquisition if it detects low power (step S21) but does not meet the stop conditions (No in step S22).

[0133] (1.2) Second Operation Example Next, a second operation example according to the first embodiment will be described. The second operation example is an example of operation in which, when data collection is being performed in UE100, a report on low power is sent when the report transmission conditions are met.

[0134] As described above, when UE100 is collecting data, by sending a report regarding low power consumption, UE100 can expect to receive a data collection stop command from the network 10. Upon receiving the stop command, UE100 can automatically stop data collection. It is also possible to consider the report transmission as the trigger for the automatic stop.

[0135] The report transmission conditions may be included in the RRC reset message in step S16 (an example of the first message). Alternatively, the report transmission conditions may be included in the message of a newly defined layer as an AI / ML layer (for example, an AI / ML layer message) (an example of the first message).

[0136] Firstly, the report transmission conditions may include a time interval for UE100 to check the power, similar to the data collection automatic stop permission information. The method of using this time interval in UE100 may be the same as in the first example of operation.

[0137] Secondly, the report transmission conditions may include information indicating that UE 100 may transmit a report at any time. In this case, UE 100 can transmit a low-power report at any time upon receiving the report transmission conditions.

[0138] Thirdly, the report transmission conditions may include conditions for sending a report. In this case, the report transmission conditions may include the remaining battery time of the UE100. For example, if the report transmission condition is "10 minutes," the UE100 will send a low power report when the remaining battery time reaches "10 minutes." Alternatively, the report transmission conditions may include the remaining battery capacity of the UE100. The remaining capacity may be expressed as a percentage of the battery's remaining capacity. For example, if the report transmission condition is "10%," the UE100 will send a low power report when the remaining battery capacity reaches "10%."

[0139] The receiving unit 110 of UE100 receives the report transmission conditions sent from the network node 200 (for example, in step S16).

[0140] Figure 12 is a diagram showing a second operation example according to the first embodiment. Steps S20 and S21 are the same processes as in the first operation example and are therefore omitted from the explanation.

[0141] In step S30, the control unit 130 of the UE 100 checks whether the report transmission conditions are met, based on the detection of a low-power state. If the report transmission conditions are met (YES in step S30), the process proceeds to step S31. On the other hand, if the report transmission conditions are not met (NO in step S30), the process proceeds to step S34.

[0142] In step S31, the transmitting unit 120 of UE 100 transmits a report regarding low power consumption if the report transmission conditions are met. This report may be transmitted using an RRC message, MAC CE, or UCI. The receiving unit 220 of the network node 200 receives this report.

[0143] In step S32, the transmitting unit 210 of the network node 200 sends a data collection stop instruction to the UE 100 in response to receiving a low power report. The data collection stop instruction is instruction information that instructs the UE 100 to stop data collection. The data collection stop instruction may include information about the type of data to be stopped. Alternatively, the data collection stop instruction may include a period for which data collection will be stopped. Alternatively, the data collection stop instruction may include configuration information for sending information to the network node 200 indicating that data collection has been stopped (or restarted) (e.g., a data collection stop notification or a data collection restart notification) when data collection is stopped. Alternatively, the data collection stop instruction may include restart conditions for restarting stopped data collection. The configuration information may be the same as in the first example of operation. The data collection stop instruction may be sent using an RRC message, MAC CE, or DCI (e.g., the second message). The receiving unit 110 of the UE 100 receives the data collection instruction.

[0144] In step S33, the control unit 130 of UE100 automatically stops data collection in accordance with the data collection stop instruction. The transmission unit 120 of UE100 may send an automatic stop notification to the network node 200 indicating that data collection has been automatically stopped.

[0145] On the other hand, in step S34, if the report transmission conditions are not met, the control unit 130 of UE100 does not transmit a report even if it detects low power (step S21), and does nothing in particular.

[0146] (1.3) Other Operation Example 1 Next, another operation example 1 according to the first embodiment will be described.

[0147] In the first embodiment, a network-side model in which AI / ML model inference is performed on the network 10 side was used as an example, but this can also be applied to a UE-side model in which inference is performed on the UE 100 side. That is, the UE 100 uses the data it has collected itself to perform inference on the AI / ML model it holds.

[0148] In this case, as in the first example of operation, data collection automatic stop permission information may be used, but it is also possible to automatically stop data collection by automatically stopping the functions of the AI / ML model held by UE100. That is, instead of data collection automatic stop permission information, function stop permission information that permits the automatic stopping of the functions of the AI / ML model may be used.

[0149] Firstly, the function deactivation permission information may include a time interval during which the UE100 checks the power, similar to the data collection automatic deactivation permission information. This time interval may be the same as in the first example of operation. Alternatively, the function deactivation permission information may include a restart instruction to restart the function of the AI / ML model after it has been deactivated.

[0150] Secondly, the function deactivation permission information may also include information indicating that UE100 may deactivate the AI / ML model's functions at any time. In this case, upon receiving the function deactivation permission information, UE100 can automatically deactivate the AI / ML model's functions at any time, and this automatic deactivation of functions can also automatically stop data collection.

[0151] Thirdly, the function deactivation permission information may include deactivation conditions that indicate the conditions for deactivating the AI / ML model's functions. The deactivation conditions may include battery remaining time and / or battery remaining capacity, similar to the data collection automatic deactivation permission information. Alternatively, the function deactivation permission information may include restart conditions for restarting the AI / ML model's functions after they have been deactivated. In this case, the same can be achieved by replacing "deactivation of data collection" with "deactivation of AI / ML model functions" and "restart of data collection" with "restart of AI / ML model functions" in the first example of operation.

[0152] Fourth, the function deactivation permission information may include information indicating the targets for automatic deactivation. This information may be a function of the AI / ML model to be automatically deactivated. If the AI / ML model held by UE100 has multiple functions, at least one of the functions of the AI / ML model may be designated as this information. Alternatively, this information may be a use case or sub-use case of the AI / ML model. In this case, the targets for automatic deactivation may be all AI / ML models that have that use case (or sub-use case). Alternatively, the function deactivation permission information may include information not only on targets for automatic deactivation but also on targets that are not automatically deactivated. For example, it is possible to specify that the CSI feedback use case is a target for automatic deactivation, while the position accuracy improvement use case is not. Alternatively, the AI / ML model itself may be designated as a target for automatic deactivation. In this case, this information may be indicated by a model ID. The AI / ML model having that model ID will be a target for automatic deactivation.

[0153] Fifth, the function deactivation permission information may include setting information indicating whether or not to send a report (or automatic deactivation notification, or restart notification) after the UE100 has automatically deactivated (or restarted) the AI / ML model's functions. In this case, the first operation example can be similarly implemented by replacing "automatic deactivation of data collection" with "deactivation of AI / ML model functions" and "restart of data collection" with "restart of AI / ML model functions".

[0154] Another example of operation 1 can be explained using Figure 11.

[0155] In this case, in step S10, the AI / ML model is transmitted along with the model information, and in step S11, the transmission of the AI / ML model is not required. Furthermore, in step S17, the AI / ML model is activated in place of or along with data collection. Also, step S18 is optional, because the UE side model already has an AI / ML model in UE100.

[0156] The function deactivation permission information may be sent in step S16 using the RRC reset message.

[0157] In UE100, if the stop conditions included in the function stop permission information are met (YES in step S22), the functions of the AI / ML model will be automatically stopped instead of, or in conjunction with, the automatic stopping of data collection (step S23). On the other hand, in UE100, if the stop conditions are not met (NO in step S22), the functions of the AI / ML model will continue to be used.

[0158] (1.4) Other Operation Example 2 Regarding the deactivation of the AI / ML model described in Other Operation Example 1, the transmission of low-power reports described in the second operation example can also be applied.

[0159] In this case, UE100 can send a report on low power consumption if the report transmission conditions are met (YES in step S30 of Figure 12), similar to the second operation example. However, at this point, the AI / ML model function is not stopped, and the function is stopped in response to receiving a command to stop the AI / ML model function from the network node 200. That is, in response to receiving the report on low power consumption (step S31 of Figure 12), the network node 200 sends a command to stop the AI / ML model function instead of a command to stop data collection (step S32). The command to stop the function may be instruction information that instructs the AI / ML model function to be stopped. The command to stop the function may include identification information (function ID) of the function to be stopped, and may include information indicating the period for which the function will be stopped. Alternatively, the command to stop the function may include restart conditions for restarting the stopped function. Alternatively, the function deactivation instruction may include configuration information for sending information (e.g., a deactivation notification) to the network node 200 indicating that the AI / ML model's function has been stopped (or restarted). The configuration information itself may be the same as the configuration information in the first example of operation.

[0160] (1.5) Other Operation Examples 3 In the first and second operation examples, the remaining battery life was explained. The remaining battery life may be generated in the application layer of UE100 and output to the AS layer of UE100.

[0161] Figure 13 is a diagram showing another example of operation (operation example 3) according to the first embodiment.

[0162] As shown in Figure 13, in step S40, the application layer of UE100 measures the remaining battery life. For example, the application layer of UE100 may measure the remaining battery life based on the execution time of the application. Alternatively, the application layer of UE100 may estimate the remaining battery life using AI / ML functionality. In this case, an AI / ML model may be used that outputs the remaining battery life when the current battery level is input. The input to the AI / ML model may further include power consumption due to communication, the average power consumption for each application, and / or the degree of battery degradation (which may be expressed as a percentage of the maximum capacity).

[0163] In step S41, the application layer of UE100 outputs the measured (or estimated) remaining battery time to the AS layer of UE100. The AS layer of UE100 may use this remaining battery time to determine the conditions for stopping data collection (step S22 in Figure 11) and / or the conditions for sending a report (step S30 in Figure 12).

[0164] (1.6) Other Operation Examples 4 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.

[0165] 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.

[0166] 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.

[0167] (1.7) Other Operational Examples 5 In the first embodiment, an example was described in which messages between UE100 and 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.

[0168] [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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] This application claims priority to Japanese Patent Application No. 2025-018683 (filed on February 6, 2025), the entirety of which is incorporated into the specification of this application.

[0176] (Note) The above embodiments can be summarized as shown in the note, but the note does not limit the embodiments.

[0177] (Note 1) A communication method in a mobile communication system, comprising the steps of: a user device receiving a first message from a network device that includes report transmission conditions indicating the conditions for transmitting a low power report to a network device; and the user device transmitting the low power report to the network device when the report transmission conditions are met.

[0178] (Note 2) The report transmission conditions are the communication method described in Note 1, which includes the remaining battery time and / or remaining battery capacity of the user device.

[0179] (Note 3) The report transmission conditions are the communication method described in Note 1 or Note 2, which includes a time interval during which the user device checks the power.

[0180] (Note 4) The communication method according to any one of Notes 1 to 3, further comprising: the step of the user device collecting data to be used in the AI / ML model; the step of the user device receiving a second message from the network device, including a data collection stop instruction, in response to the user device transmitting the low power report to the network device; and the step of the user device automatically stopping the data collection in accordance with the data collection stop instruction.

[0181] (Note 5) The communication method according to any one of Notes 1 to 4, further comprising the step of the user device collecting data to be used in the AI / ML model, wherein the first message includes data collection automatic stop permission information indicating that the data collection may be automatically stopped.

[0182] (Note 6) The data collection automatic stop permission information is communicated by any of the methods described in Notes 1 to 5, including a time interval during which the user device checks the power.

[0183] (Note 7) The data collection automatic stop permission information is a communication method described in any one of Notes 1 to 6, which includes a stop condition indicating the conditions for automatically stopping the data collection.

[0184] (Note 8) The stop condition is a communication method according to any one of Notes 1 to 7, including the remaining battery time and / or remaining battery capacity of the user device.

[0185] (Note 9) The first message is a communication method according to any one of Notes 1 to 8, which includes permission information to stop the function of the AI / ML model, indicating that it may be automatically stopped. (Note 10) The data collection stop permission information is a communication method according to any one of Notes 1 to 9, which includes a restart instruction indicating that it may be restarted after the automatic stop of data collection.

[0186] (Note 11) A user device in a mobile communication system, comprising: a receiving unit that receives a first message from a network device that includes report transmission conditions indicating the conditions for transmitting a report on low power to a network device; and a transmitting unit that transmits the report on low power to the network device when the report transmission conditions are met.

[0187] 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 receiving a first message from a network device that includes report transmission conditions indicating the conditions for transmitting a low power report to a network device; and the user device transmitting the low power report to the network device when the report transmission conditions are met.

2. The communication method according to claim 1, wherein the report transmission conditions include the remaining battery time of the user device and / or the remaining battery capacity of the user device.

3. The communication method according to claim 1, wherein the report transmission conditions include a time interval during which the user device checks the power.

4. The communication method according to claim 1, further comprising: the user device collecting data for use in an AI (Artificial Intelligence) / ML (Machine Learning) model; the user device receiving a second message from the network device, including a data collection stop instruction, in response to the user device transmitting a low power report to the network device; and the user device automatically stopping the data collection in accordance with the data collection stop instruction.

5. The communication method according to claim 1, further comprising the user device performing data collection for use in an AI / ML model, wherein the first message includes data collection automatic stop permission information indicating that the data collection may be automatically stopped.

6. The communication method according to claim 5, wherein the data collection automatic stop permission information includes a time interval during which the user device checks the power.

7. The communication method according to claim 5, wherein the data collection automatic stop permission information includes a stop condition indicating the conditions for automatically stopping the data collection.

8. The communication method according to claim 7, wherein the stop condition includes the remaining battery time and / or remaining battery capacity of the user device.

9. The communication method according to claim 1, wherein the first message includes permission information to automatically stop the function of the AI / ML model.

10. The communication method according to claim 5, wherein the data collection automatic stop permission information includes a restart instruction indicating that data collection may be resumed after the automatic stop of data collection.

11. A user device in a mobile communication system, comprising: a receiving unit that receives a first message from a network device including report transmission conditions indicating the conditions for transmitting a low power report to a network device; and a transmitting unit that transmits the low power report to the network device when the report transmission conditions are met.

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