Communication control method and network device

Abstract

A communication control method according to one aspect of the present invention is for a mobile communication system. The communication control method includes a step in which a user device monitors the surrounding environment. Furthermore, the communication control method includes a step in which the user device inputs the monitoring result into an Artificial Intelligence (AI) / Machine Learning (ML) model to predicting an event indicating a condition for transmitting a measurement report. In addition, the communication control method includes a step in which the user device transmits the event to a network node.

Description

Communication control method and network device

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

[0002] 3GPP (Third Generation Partnership Project) (registered trademark; hereinafter the same) is a standardization project for mobile communication systems. It is investigating the application of artificial intelligence (AI) technology, particularly machine learning (ML) technology, to wireless communication (air interface) of mobile communication systems. 3GPP has also begun discussions on applying AI technology to mobility (specifically, handover).

[0003] 3GPP TR 38.843 V18.0.0 (2023-12) RP-234055

[0004] A communication control method according to a first aspect is a communication control method in a mobile communication system. The communication control method includes a step of a user equipment monitoring a surrounding environment. The communication control method also includes a step of the user equipment inputting the monitoring results into an AI / ML model to predict an event indicating a condition for transmitting a measurement report. The communication control method also includes a step of the user equipment transmitting the event to a network node.

[0005] A user equipment according to a second aspect is a user equipment in a mobile communication system, the user equipment including a control unit that monitors a surrounding environment, inputs the monitoring results into an AI / ML model, and predicts an event indicating a condition for transmitting a measurement report, and a transmission unit that transmits the event to a network node.

[0006] FIG. 1 is a diagram illustrating an example of the configuration of a mobile communication system according to the first embodiment. FIG. 2 is a diagram illustrating an example of the configuration of a UE (user equipment) according to the first embodiment. FIG. 3 is a diagram illustrating an example of the configuration of a network node (base station) according to the first embodiment. FIG. 4 is a diagram illustrating an example of the configuration of a protocol stack according to the first embodiment. FIG. 5 is a diagram illustrating an example of the configuration of a protocol stack according to the first embodiment. FIG. 6 is a diagram illustrating an example of the configuration of functional blocks of AI / ML technology according to the first embodiment. FIGS. 7(A) and 7(B) are diagrams illustrating an example of RRM measurement prediction in the time domain according to the first embodiment. FIGS. 8(A) and 8(B) are diagrams illustrating an example of RRM measurement prediction in the space domain according to the first embodiment. FIGS. 9(A) and 9(B) are diagrams illustrating an example of RRM measurement prediction in the frequency domain according to the first embodiment. FIGS. 10(A) and 10(B) are diagrams illustrating an example of RLF prediction according to the first embodiment. FIGS. 11(A) and 11(B) are diagrams illustrating an example of measurement event prediction according to the first embodiment. FIG. 12 is a diagram illustrating an example of operation according to the first embodiment. FIG. 13 is a diagram illustrating an example of operation according to the second embodiment.

[0007] An object of the present disclosure is to provide a communication control method and a user device that can improve the transmission performance of measurement reports.

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

[0009] (Configuration of mobile communication system) The configuration of a mobile communication system according to the first embodiment will be described. FIG. 1 is a diagram showing an example of the configuration of a 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. Although 5GS will be described below as an example, the mobile communication system may also be at least partially applied with an LTE (Long Term Evolution) system. The mobile communication system may also be at least partially applied with a sixth generation (6G) system or later system.

[0010] The mobile communication system 1 includes a network (NW) 10 and a user equipment (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, and may be, for example, a mobile phone terminal (including a smartphone), a tablet terminal, a notebook PC (Personal Computer), a communication module (including a communication card or chipset), a sensor or a device provided in a sensor, a vehicle or a device provided in a vehicle (Vehicle UE), or an aircraft or a device provided in an aircraft (Aerial UE).

[0011] The NW 10 includes a radio access network (RAN) 20 and a core network (CN) 30. When the mobile communication system is a 5th generation system (5GS), the RAN 20 is referred to as a Next Generation Radio Access Network (NG-RAN), and the CN 30 is referred to as a 5G Core Network (5GC).

[0012] The RAN 20 includes a plurality of network nodes 200 (network nodes 200a to 200c in the example of FIG. 1). The network nodes 200 are connected to each other via inter-network node interfaces. The network nodes 200 may be referred to as base stations in the RAN 20. When the network node 200 is a base station, the network node 200 may be configured (i.e., functionally divided) with a CU (Central Unit) and a DU (Distribution Unit), and the two units may be connected by a fronthaul interface. When the mobile communication system 1 is 5GS, the network node 200 is referred to as a gNB, the inter-network node interface is referred to as an Xn interface, and the fronthaul interface is referred to as an F1 interface.

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

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

[0015] The CN 30 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 UE 100. The C-plane device communicates with the UE 100 using NAS (Non-Access Stratum) signaling. The U-plane device controls data forwarding. When the mobile communication system is 5GS, the C-plane device is called an AMF (Access and Mobility Management Function), the U-plane device is called a UPF (User Plane Function), and the interface between the network node 200 and the CN device 300 is called an NG interface.

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

[0017] 2 is a diagram showing an example of the configuration of a UE 100 (user equipment) according to the first embodiment. The UE 100 includes a receiving unit 110, a transmitting unit 120, and a control unit 130. The receiving unit 110 and the transmitting unit 120 configure a communication unit 140 that performs wireless communication with a network node 200. The UE 100 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 a radio signal received by the antenna into a baseband signal (received signal) and outputs the baseband signal to the control unit 130.

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

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

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

[0024] The control unit 230 performs various controls and processes in the network node 200. Such processes include processes of each layer described below. 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 in the processes by the processor. The processor may include a baseband processor and a CPU. The baseband processor performs modulation / demodulation, encoding / decoding, etc. of baseband signals. The CPU executes programs stored in the memory to perform various processes. Note that the processes or operations performed in the network node 200 may be performed by the control unit 230.

[0025] The NW communication unit 240 is connected to adjacent network nodes via an Xn interface, which is an interface between network nodes. The NW communication unit 240 is connected to the CN device 300 via an NG interface, which is an interface between a network node and a core network. The network node 200 may be configured (i.e., functionally divided) with a central unit (CU) and a distributed unit (DU), and both units may be connected via an F1 interface, which is a fronthaul interface.

[0026] FIG. 4 is a diagram showing an example of the configuration of a protocol stack of a radio interface of a user plane that handles data.

[0027] The user plane air interface protocol includes 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 the UE 100 and the PHY layer of the network node 200 via a physical channel. The PHY layer of the UE 100 receives downlink control information (DCI) transmitted on a physical downlink control channel (PDCCH) from the network node 200. Specifically, the UE 100 performs blind decoding of the PDCCH using a radio network temporary identifier (RNTI), and acquires successfully decoded DCI as DCI addressed to the UE. The DCI transmitted from the network node 200 has a CRC (Cyclic Redundancy Code) parity bit scrambled by the RNTI added thereto.

[0029] In NR, the UE 100 can use a bandwidth narrower than the system bandwidth (i.e., the cell bandwidth). The network node 200 configures the UE 100 with a bandwidth portion (BWP) consisting of consecutive PRBs. The UE 100 transmits and receives data and control signals in the active BWP. For example, up to four BWPs may be configurable for the UE 100. Each BWP may have a different subcarrier spacing. The BWPs may overlap in frequency. When multiple BWPs are configured for the UE 100, the network node 200 can specify which BWP to apply by controlling the downlink. This allows the network node 200 to dynamically adjust the UE bandwidth according to the amount of data traffic of the UE 100, etc., 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 the serving cell. A CORESET is a radio resource for control information to be received by the UE 100. Up to 12 or more CORESETs may be configured for the UE 100 on the serving cell. Each CORESET may have an index of 0 to 11 or more. A CORESET may consist of six resource blocks (PRBs) and one, two, or three consecutive OFDM symbols in the time domain.

[0031] The MAC layer performs data priority control, retransmission processing using Hybrid Automatic Repeat reQuest (HARQ), random access procedures, etc. Data and control information are transmitted between the MAC layer of UE 100 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 uplink and downlink transport format (transport block size, modulation and coding scheme (MCS)) and resource blocks to be allocated to UE 100.

[0032] The RLC layer transmits data to the RLC layer on the receiving side using the functions of the MAC layer and the PHY layer. Data and control information are transmitted between the RLC layer of the UE 100 and the RLC layer of the network node 200 via logical channels.

[0033] The PDCP layer performs header compression / decompression, encryption / decryption, and the like.

[0034] The SDAP layer maps IP flows, which are units for Quality of Service (QoS) control by the core network, to radio bearers, which are units for QoS control by the access stratum (AS). Note that if the RAN is connected to the EPC, SDAP may not be required.

[0035] FIG. 5 is a diagram showing the configuration of a protocol stack of a radio interface of a control plane that handles signaling (control signals).

[0036] The protocol stack of the radio interface of the control plane includes a radio resource control (RRC) layer and a non-access stratum (NAS) instead of the SDAP layer shown in FIG.

[0037] RRC signaling for various settings is transmitted between the RRC layer of the UE 100 and the RRC layer of the network node 200. The RRC layer controls logical channels, transport channels, and physical channels in accordance with the establishment, re-establishment, and release of radio bearers. When there is a connection (RRC connection) between the RRC of the UE 100 and the RRC of the network node 200, the UE 100 is in an RRC connected state. When there is no connection (RRC connection) between the RRC of the UE 100 and the RRC of the network node 200, the UE 100 is in an RRC idle state. When the connection between the RRC of the UE 100 and the RRC of the network node 200 is suspended, the UE 100 is in an RRC inactive state.

[0038] The NAS, which is located above the RRC layer, performs session management, mobility management, etc. NAS signaling is transmitted between the NAS of the UE 100 and the NAS of the AMF. Note that the UE 100 has an application layer in addition to the radio interface protocol. Also, the layer below the NAS is called an Access Stratum (AS).

[0039] (AI / ML Technology) Next, AI / ML (Artificial Intelligence / Machine Learning) technology according to an embodiment will be described. First, three points (functional blocks, use cases, and LCM) discussed in 3GPP as the AI / ML air interface (AI / ML for NR air interface) will be described below.

[0040] (Functional Blocks) FIG. 6 is a diagram showing an example of the configuration of functional blocks of the AI / ML technology in the mobile communication system 1 according to the first embodiment.

[0041] As shown in FIG. 6, the functional blocks include 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 storage unit (Model Storage) A6.

[0042] The functional block configuration example shown in FIG. 6 represents a functional framework of a general AI / ML technology. Therefore, depending on the hypothetical use case, some of the functional block configuration example (e.g., model recording unit A6, etc.) may not be included in the functional block configuration example. The functional block configuration example shown in FIG. 6 may also be distributed between the UE 100 and the network device. Alternatively, some functions of the functional block configuration example (e.g., model learning unit A2 or model inference unit A3, etc.) may be located in both the UE 100 and the network device. Note that the network device refers to a node or entity included in the NW 10. The network device may be the CN device 300. The network device may also be the network node 200.

[0043] The data collection unit A1 provides input data to the model learning unit A2, the model inference unit A3, and the management unit A5. The input data includes training data for the model learning unit A2, inference data for the model inference unit A3, and monitoring data for the management unit A5.

[0044] The training data is data required as input when the AI / ML model is learning. The inference data is data required as input when the AI / ML model is inferring. The monitoring data is data required as input when the AI / ML model is managing.

[0045] In addition, data collection may refer to the process of collecting data at a network node, a management entity, or a UE 100, for example, to train an AI / ML model, manage an AI / ML model, and perform inference on an AI / ML model.

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

[0047] AI / ML model learning is the process of learning an AI / ML model from input / output relationships to obtain a trained AI / ML model to be used for inference. For example, considering y = ax + b, AI / ML model learning may be the process of optimizing a (slope) and b (intercept) by providing input (x) and output (y) (i.e., providing learning data).

[0048] Generally, machine learning includes supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a method that uses correct answer data as training data. Unsupervised learning is a method that does not use correct answer data as training data. For example, unsupervised learning memorizes feature points from a large amount of training data and determines the correct answer (estimates the range). Reinforcement learning is a method that assigns a score to the output result and learns how to maximize the score. Although supervised learning will be described below, unsupervised learning may also be applied as machine learning. Reinforcement learning may also be applied as machine learning.

[0049] The model learning unit A2 outputs a trained AI / ML model (Trained Model) obtained by AI / ML model learning to the model recording unit A6, and also outputs an updated AI / ML model (Updated Model) obtained by relearning the trained AI / ML model to the model recording unit A6.

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

[0051] 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, in the equation 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. There are various model 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.

[0052] The model inference unit A3 outputs inference output data to the management unit A5. The model inference unit A3 also receives management instructions from the management unit A5. For example, the management instructions include selection of an AI / ML model, activation (deactivation) of an AI / ML model, switching of an AI / ML model, and fallback (performing inference without using an AI / ML model). The model inference unit A3 performs model inference in accordance with the management instructions.

[0053] Note that AI / ML model inference is, for example, a process of obtaining a set of outputs from a set of inputs using a trained AI / ML model (or an updated AI / ML model). Alternatively, model inference may be a process of obtaining inference output data from inference data using a trained AI / ML model (or an updated AI / ML model). Hereinafter, AI / ML model inference may be referred to as "model inference" or "inference."

[0054] In the following, an AI / ML model that is currently being trained (or updated) may be referred to as a training AI / ML model (or an updating AI / ML model). In the following, when there is no need to distinguish between a training (or updating) AI / ML model and a trained (or updated) AI / ML model, they may be simply referred to as an "AI / ML model."

[0055] 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. The management unit A5 can also perform operations to ensure appropriate inference operations based on monitoring data and inference output data. To this end, the management unit A5 outputs a model transfer and / or model delivery request (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 be output to the model inference unit A3. The management unit A5 also outputs management instructions to the model inference unit A3 and supervises operations on the AI / ML model. Furthermore, the management unit A5 can output performance feedback and a re-learning request to the model learning unit A2, causing the model learning unit A2 to re-learn the AI / ML model (i.e., update the learned AI / ML model).

[0056] (Use Cases) Next, three use cases discussed in the AI / ML air interface will be described. There are three use cases as follows.

[0057] (A1) "CSI (Channel State Information) Feedback Enhancement"

[0058] (A2) "Beam management"

[0059] (A3) “Positioning accuracy enhancement”

[0060] (A1) CSI Feedback Enhancement CSI feedback enhancement represents a use case in which, for example, AI / ML techniques are applied to CSI fed back from the UE 100 to the network node 200. The CSI is information about a channel state in a downlink between the UE 100 and the network node 200. The CSI includes at least one of a Channel Quality Indicator (CQI), a Precoding Matrix Indicator (PMI), and a Rank Indicator (RI). The network node 200 performs, for example, downlink scheduling based on the CSI feedback from the UE 100.

[0061] The use case of improved CSI feedback has two sub-use cases: CSI compression in the frequency domain and CSI prediction in the time domain.

[0062] In the CSI compression, the CSI inferred in the UE 100 using the trained AI / ML model is compressed in the UE 100. Specifically, the compression is performed by quantizing the inferred CSI. The compressed (or quantized) CSI is transmitted from the UE 100 to the network node 200.

[0063] In CSI prediction, a trained AI / ML model is used to infer (predict) future CSI from the history of past CSI. The UE 100 has the trained AI / ML model, and uses the past history of CSI as input to infer future CSI (predicted CSI). The future CSI is transmitted to the network node 200 as CSI feedback.

[0064] (A2) Beam Management The beam management use case also has 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. The spatial-domain downlink beam prediction is called "BM Case 1" (BM-Case 1), and the temporal downlink beam prediction is called "BM Case 2" (BM-Case 2).

[0065] In BM case 1, the input to the AI / ML model 103 is a measurement value for each beam included in beam set B. On the other hand, the output from the AI / ML model 103 is the probability that each (downlink) beam included in (predicted) beam set A will be the top beam. Beam set A and beam set B may be different. Alternatively, beam set B may be a subset of beam set A. In BM case 1, the inference may be performed on the UE 100 side. The inference may also be performed on the network device side.

[0066] In BM case 2, the input to AI / ML model 103 is the history of measurement values ​​for each beam included in beam set B. Measurement values ​​measured in the past for each beam are input to AI / ML model 103. On the other hand, the output (inference output data) from AI / ML model 103 is the probability that each (downlink) beam included in (predicted) beam set A will be the top beam. Beam set A and beam set B may be different, or beam set B may be a subset of beam set A. Also, in BM case 2, beam set A and beam set B may be the same. In BM case 2, inference may also be performed on the UE 100 side. The inference may also be performed on the network device side.

[0067] (A3) Location Accuracy Improvement The location accuracy improvement use case also has two sub-use cases: Direct AI / ML positioning, which uses a trained AI / ML model to directly infer the location of UE 100, and AI / ML assisted positioning, which infers intermediate position measurements.

[0068] In direct AI / ML positioning, a trained AI / ML model is used to directly infer the location of UE 100 from measurements at each measurement point (TRP: Transmission and / or Reception Point).

[0069] On the other hand, in AI / ML-assisted positioning, an intermediate location measurement value is inferred from measurements at each measurement point (TRP: Transmission and / or Reception Point) using a trained AI / ML model. The intermediate location measurement value is transmitted to a Location Management Function (LMF) and used by the LMF to measure (or infer) the location of the UE 100. The LMF may have a trained AI / ML model, and the location of the UE 100 is inferred using the trained AI / ML model.

[0070] (LCM) In the AI / ML wireless interface, LCM (Life Cycle Management) of the AI / ML model is being discussed.

[0071] Specifically, the LCM of the AI / ML model may include at least one of the following operations.

[0072] (L1) Data collection

[0073] (L2) Model training

[0074] (L3) Identification

[0075] (L4) Model delivery / transfer

[0076] (L5) Model inference operation

[0077] (L6) Selection, Activation, Deactivation, Switching, and Fallback Operations

[0078] (L7) Monitoring

[0079] (L8) Model updating

[0080] (L9) UE capability

[0081] On the network side, for example, by controlling each operation of the LCM, it becomes possible to appropriately manage the process from the generation of an AI / ML model to the deletion (or disposal) of the AI / ML model. Note that fallback means 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."

[0082] Regarding LCM, 3GPP defines functionality-based LCM and model ID-based LCM.

[0083] The function-based LCM may be an operation performed on a function of the AI / ML model. Specifically, the function-based LCM may be an operation of activation, deactivation, switching, or fallback performed on a function of the AI / ML model. The network side can instruct the LCM operation on the function of the AI / ML model, for example, by using 3GPP signaling (RRC message, MAC CE, or DCI). In the function-based LCM, the UE 100 may support one AI / ML model for one function. The UE 100 may support multiple AI / ML models for one function.

[0084] On the other hand, model ID-based LCM may be an operation performed on an individual AI / ML model using a model ID. The model ID is identification information for distinguishing an AI / ML model from other AI / ML models. Specifically, model ID-based LCM may be an operation of activating, deactivating, switching, or selecting an AI / ML model using the model ID.

[0085] (AI / ML Mobility) Next, AI / ML mobility (AI / ML for mobility) will be described.

[0086] Here, the existing Layer 3 (L3) handover will be considered as follows. That is, in the existing Layer 3 (L3) handover, it is triggered and executed based on past measurement results and event conditions reported from the UE 100. In that handover is executed using past results, the existing L3 handover can be said to be a kind of reactive scheme. When the mobility of the UE 100 is below a certain level, such an L3 handover may function well. However, when the mobility of the UE 100 is higher than a certain level or when cells are deployed more densely than a certain level, various problems may occur. Specifically, unintended problems such as radio link failure (RLF), premature handover, premature handover, handover failure (HOF), ping-pong handover, etc. may occur.

[0087] To address such problems, it is possible to avoid the above-mentioned problems by predicting future beam measurement results or future cell measurement results using, for example, an AI / ML model. That is, by establishing a proactive scheme for handover using the AI / ML model, it is possible to predict the occurrence of unintended problems in advance and take preventative measures, thereby improving handover performance. The shift from a reactive scheme to a proactive scheme is one of the motivations for research on AI / ML mobility in 3GPP.

[0088] Next, three prediction models for AI / ML mobility that are being discussed in 3GPP will be described: RRM (Radio Resource Management) measurement prediction, measurement event prediction, and RLF / HOF prediction.

[0089] (B1) RRM Measurement Prediction First, RRM measurement prediction will be described. In RRM measurement prediction, an AI / ML model is used to finally output L3 cell-level measurements. Specifically, the L3 cell-level measurements correspond to the output after L1 filtering is performed on layer 1 (L1) beam-level measurements, cell quality is derived by solidation, and L3 filtering is performed on the cell quality. The L3 cell-level measurements are included in a measurement report and transmitted from the UE 100 to the network node 200.

[0090] RRM measurement prediction has three subcases: measurement prediction in the time domain (intra-cell temporal domain prediction), measurement prediction in the spatial domain (intra-cell spatial domain prediction), and measurement prediction in the frequency domain (inter-frequency inter-cell prediction).

[0091] (B1-1) RRM Measurement Prediction in the Time Domain In the RRM measurement prediction in the time domain, basically, an AI / ML model is used to predict unmeasured measurement results from measured measurement results in the time direction.

[0092] 7(A) and 7(B) are diagrams illustrating examples of RRM measurement prediction in the time domain according to the first embodiment. In the RRM measurement prediction in the time domain, as shown in FIG. 7(A), there is Case A in which future measurement results in a prediction window are predicted based on measurement results actually measured by the UE 100 in an observation window. The observation window represents a period during which the UE 100 actually performs measurements. For example, the UE 100 reports future measurement results to the network node 200, which enables the network node 200 to trigger handover at an appropriate timing. Furthermore, in the RRM measurement prediction in the time domain, there is Case B in which a subset of measurement results is predicted based on actually measured measurement results, as shown in FIG. 7(B).

[0093] (B1-2) RRM Measurement Prediction in the Spatial Domain In the RRM measurement prediction in the spatial domain, basically, an AI / ML model is used to output cell level measurement results for L3 from partial beam level measurement results for L1.

[0094] 8A and 8B are diagrams illustrating an example of RRM measurement prediction in the spatial domain according to the first embodiment. In the spatial domain RRM measurement prediction, as shown in FIG. 8A, an AI / ML model is used to predict overall L1 beam level measurement results from partial L1 beam level measurement results, and legacy consolidation and L3 filtering are applied to the predicted results to output L3 cell level measurement results. In the case shown in FIG. 8A, the AI / ML model is used to predict L1 level beam level measurement results. Also, as shown in FIG. 8B, an AI / ML model is used to directly infer L3 cell level measurement results from partial L1 beam level measurement results.

[0095] (B1-3) RRM Measurement Prediction in the Frequency Domain Figures 9(A) and 9(B) are diagrams showing examples of RRM measurement prediction in the frequency domain according to the first embodiment. As shown in Figure 9(A), in the RRM measurement prediction in the frequency domain, an AI / ML model is used to predict the measurement results in a cell B, which is different from cell A, from the measurement results in cell A. However, as shown in Figure 9(B), it is assumed that cell A and cell B exist in the same sector.

[0096] An example of RRM measurement prediction has been described above, and in 3GPP, RRM measurement prediction is currently given the highest priority compared to other prediction models.

[0097] (B2) Measurement Event Prediction In 3GPP, it has been agreed that there is at least a method using the output of the RRM measurement prediction described above for measurement event prediction. However, it has also been agreed in 3GPP that discussion on measurement event prediction will be suspended until progress in the discussion on RRM measurement prediction is made.

[0098] (B3) RLF / HOF Prediction FIGS. 10(A) and 10(B) are diagrams showing examples of RLF prediction according to the first embodiment.

[0099] In 3GPP, it has been agreed that there are two methods for predicting RLF / HOF: an indirect method (FIG. 10(A)) that uses the output of RRM measurement prediction, and a direct method (FIG. 10(B)) that does not use RRM measurement prediction. It has also been agreed that in RLF / HOF prediction, RLF prediction takes priority over HOF prediction.

[0100] (Communication Control Method According to First Embodiment) In the first embodiment, of the three prediction models of AI mobility being discussed in 3GPP, the following description focuses on measurement event prediction.

[0101] Regarding measurement event prediction, a method using the output of RRM measurement prediction has been agreed upon in 3GPP. Fig. 11(A) is a diagram showing an example of measurement event prediction according to the first embodiment. For example, the configuration shown in Fig. 11(A) may be realized by cooperative operation of a processor and a memory included in the control unit 130 of the UE 100.

[0102] As shown in FIG. 11A , multiple AI / ML models exist for each cell's measurement results. Each AI / ML model inputs the measurement results (e.g., L1 measurement results) for each cell and predicts the cell-level measurement results for L3. The event evaluation unit evaluates the cell-level measurement results for each cell using legacy criteria rather than the AI / ML model. Specifically, the evaluation unit evaluates whether the entering condition for each event is met (i.e., whether the event is triggered (entered)) based on the cell-level measurement results for each cell. The evaluation unit also evaluates whether the leaving condition for each event is met based on the cell-level measurement results for each cell. To this end, the evaluation unit receives legacy parameters (e.g., thresholds, offset values, time-to-trigger (TTT) values, and hysteresis values ​​corresponding to each event, such as event A1). The evaluation unit outputs, as an evaluation result, information indicating that the entering condition is satisfied (entering condition) or information indicating that the leaving condition is satisfied (leaving condition).

[0103] In FIG. 11A, the cell-level measurement values ​​of L3 are predicted by the AI / ML model, and the evaluation results are output by the event evaluation unit based on the legacy standard, representing a framework based on the indirect method.

[0104] 11A, since the event evaluation itself uses a conventional method, the characteristics of the measurement event prediction are determined by the performance of the RRM measurement prediction (AI / ML model). That is, from the perspective of the accuracy of the measurement event prediction, the accuracy of the inference result of the RRM measurement prediction is the accuracy of the measurement event prediction. Also, from the perspective of the target time of the measurement event prediction, if the inference result of the RRM measurement prediction is a current measurement value, a current event is evaluated, and if the inference result of the RRM measurement prediction represents a future measurement value, a future event is evaluated.

[0105] In general, measurement event prediction using an AI / ML model can be expected to use the results of event evaluation by the AI / ML model to trigger measurement reports. In particular, when RRM measurement prediction infers current measurements, it is possible to use the same measurement reporting framework as the current one, as shown in Figure 11(A). On the other hand, when measurement event prediction infers future events, it is also possible to use it in a different way from the current measurement reporting framework.

[0106] In the current measurement reporting framework, the UE 100 evaluates cell-level measurement results using event conditions (or events, hereinafter sometimes referred to as "events") specified by the network node 200. Therefore, even if the measurement results satisfy an event not specified by the network node 200, the UE 100 is unable to detect this state. The network node 200 is also unable to detect this state, and is unable to optimize measurement reporting performance until it receives a notification of a Radio Link Failure (RLF) from the UE 100.

[0107] Therefore, the first embodiment aims to improve the performance of measurement reports.

[0108] Therefore, in the first embodiment, the UE 100 uses the AI / ML model to infer an appropriate event (or measurement event, hereinafter sometimes referred to as an "event") and reports the inferred event to the network node 200. Specifically, first, the user equipment (e.g., the UE 100) monitors the surrounding environment. Second, the user equipment inputs the monitoring result into the AI / ML model to predict an event indicating a condition for sending a measurement report. Third, the user equipment transmits the event to the network node (e.g., the network node 200).

[0109] As described above, in the first embodiment, since the UE 100 transmits the inferred event to the network node 200, the network node 200 can handle the event as an optimal event for the UE 100. Therefore, the network node 200 can include the event in measurement report configuration information (Measurement Configuration) and transmit it to the UE 100. Furthermore, the network node 200 can transmit a handover request (HANDOVER REQUEST) to a target candidate cell in advance before receiving the measurement report, assuming that the measurement report will be transmitted from the UE 100 based on the event. That is, it is possible to optimize the performance of the measurement report (or handover).

[0110] As described above, in the first embodiment, an AI / ML model is used to directly infer events to be used in measurement reports. That is, the first embodiment represents an example in which measurement event prediction is performed using the direct method.

[0111] Fig. 11(B) is a diagram illustrating a configuration example of measurement event prediction according to the first embodiment. The configuration example illustrated in Fig. 11(B) may also be executed, for example, by cooperative operation of a processor and a memory included in the control unit 130 of the UE 100. As illustrated in Fig. 11(B), the event prediction model, which is an AI / ML model, uses, as input, monitoring results of the surrounding environment monitored by the UE 100.

[0112] The monitoring results may include radio measurement values ​​for the serving cell and radio measurement values ​​for neighboring cells adjacent to the serving cell. The radio measurement values ​​may be L1 beam level measurement values. Specifically, the radio measurement values ​​may be reception quality values ​​(e.g., RSRQ, RSRP, or SINR) for reference signals (e.g., CSI reference signals (CSI-RS: Channel State Information-Reference Signal) or demodulation reference signals (DMRS: Demodulation Reference Signal) included in SSBs) received by the UE 100 from the serving cell (or neighboring cell). The radio measurement values ​​may be L3 cell level measurement values. As described above, the L3 level cell level measurement values ​​represent the results of applying L1 filtering, integration, and L3 filtering to the L1 beam level measurement values.

[0113] The monitoring result may also include location information of the UE 100. The location information may be represented by latitude, longitude, and / or altitude.

[0114] Alternatively, the monitoring result may include movement information of the UE 100. The movement information may include the speed of the UE 100 and / or the movement direction of the UE 100.

[0115] Alternatively, the monitoring result may include information about an application executed by the UE 100. The application information may include a user quality of experience (QoE) measured by the execution of the application.

[0116] Alternatively, the monitoring result may include information about the neighboring cell. For example, the monitoring result may include information about the neighboring cell that the UE 100 acquires from the broadcast information (MIB or SIB).

[0117] The AI / ML model according to the first embodiment (FIG. 11B) is a model that receives monitoring results as input and predicts events to be used in measurement reports.

[0118] (Example of Operation According to First Embodiment) Next, an example of operation according to the first embodiment will be described.

[0119] 12 is a diagram illustrating an example of operation according to the first embodiment. In FIG. 12, it is assumed that the UE 100 is in an RRC connected state with the network node 200.

[0120] In step S10, the transmission unit 210 of the network node 200 transmits measurement event prediction configuration information to the UE 100. The measurement event prediction configuration information indicates information for setting an AI / ML model used for measurement event prediction.

[0121] First, the measurement event prediction configuration information may include a range of event types to be inferred (or predicted). The range of event types is expressed, for example, as Event A1 to Event A5. The event is used, for example, when determining a condition (entering condition) for transmitting a measurement report. The event may be any of Event A1 to Event A6 related to the quality of a neighboring cell. The event may be an event other than Event A1 to Event A6. For example, the event may be any of Event B1 and Event B2 related to the quality between different radio access technologies (RATs). Alternatively, the event may be any of Event C1 and Event C2 used in a sidelink. Alternatively, the event may be an event related to the distance from the UE 100 (such as Event D1). The event may be a time-related event (such as Event T1). The event may be an event used in a U2N relay (such as Event X1). The event may be a height-related event (such as Event H1). Alternatively, the event may be an event used in a conditional handover (such as a conditional event A3 (condEventA3)). Alternatively, the event may be an event used to trigger an L1 measurement report in LTM (L1 / L2 Triggered Mobility).

[0122] Second, the measurement event prediction configuration information may include a range of measurement configuration identification information (MeasID) of the measurement configuration (Measurement Configuration) as an inference target. For example, when the measurement event prediction configuration information includes a range of measurement configuration identification information from MeasID=1 corresponding to event A1 to MeasID=5 corresponding to event A5, the inference target is event A1 to event A5. The range of measurement configuration identification information may also be indicated by identification information (LTM Configuration ID) of the LTM configuration (LTM Configuration). The range of measurement configuration identification information may also be indicated by identification information (Conditional Reconfiguration ID) of the conditional configuration (Conditional Reconfiguration) of conditional handover (CHO).

[0123] Third, the measurement event prediction setting information includes setting information and / or parameter information of an AI / ML model used for measurement event prediction. The setting information may include information on what information is used as input to the AI / ML model (specifically, what information is used as a monitoring result).

[0124] The measurement event prediction configuration information may be included in an RRC message (e.g., an RRCReconfiguration message or an RRCResume message) and transmitted from the network node 200 to the UE 100. Alternatively, the measurement event prediction configuration information may be included in a message of a layer newly defined for AI / ML (AI / ML layer message) and transmitted. The receiving unit 110 of the UE 100 receives the measurement event prediction configuration information.

[0125] In step S11, the control unit 130 of the UE 100 generates an AI / ML model to be used for measurement event prediction. For example, the control unit 130 of the UE 100 generates the AI / ML model by using configuration information of the AI / ML model included in the measurement event prediction configuration information. Then, the control unit 130 of the UE 100 may generate a trained AI / ML model by performing learning on the generated AI / ML model.

[0126] In step S12, the control unit 130 of the UE 100 monitors the surrounding environment. The control unit 130 of the UE 100 may monitor the surrounding environment in accordance with the configuration information of the AI / ML model included in the measurement event prediction configuration information.

[0127] In step S13, the control unit 130 of the UE 100 inputs the monitoring result acquired in step S12 into the trained AI / ML model generated in step S11 to start inference. The inference result is information about the optimal event. The optimal event is an event that is not set by the measurement configuration (or an event that is set but not triggered), but may represent an event that should be set currently (or an event that should be triggered). Alternatively, the optimal event may be the optimal event among the events set by the measurement configuration. Alternatively, the optimal event may be an event that is predicted to occur in the future in the UE 100. The event may be an event set by the measurement configuration. The event may be an event that is not set by the measurement configuration. The AI / ML model may infer the occurrence probability of an event that is predicted to occur in the future. In this way, the inference result may represent the most appropriate event under a certain situation. In step S14 , the transmitter 120 of the UE 100 transmits the inference result to the network node 200 .

[0128] First, the inference result may be transmitted in a measurement report, or may be transmitted in another RRC message such as UE Assistance Information, or may be transmitted in a message of a layer newly defined for AI / ML (AI / ML Assistance Information message).

[0129] Second, the inference result may include information about the optimal event inferred by UE 100. A plurality of optimal events may be included. The event recommended by UE 100 may be the optimal event. Alternatively, the inference result may include parameters used in the optimal event. The parameters may be a threshold, an offset value, a TTT value, and / or a hysteresis value. Alternatively, the inference result may include a measurement result. The measurement result may be a beam level measurement value of L1. The measurement result may be a cell level measurement value of L3. Alternatively, the inference result may include a monitoring result.

[0130] Third, the transmitter 120 of the UE 100 may transmit a measurement report including an inference result only when an entering condition for the optimal event is satisfied (i.e., when the optimal event is triggered). That is, even if an event set in the measurement configuration is triggered, if it is a non-optimal event, the transmitter 120 of the UE 100 may not transmit a measurement report.

[0131] Fourth, the inference result may include information indicating that the AI / ML model has predicted that an event set in the measurement setting will occur in the future. The information may include an occurrence probability of the event. For example, the information may include an occurrence probability of the event occurring within a prediction time window (e.g., 500 ms from the present). Alternatively, the information may include information regarding an expected time at which the event will occur. For example, the information may include an expected time at which the event will occur with a predetermined occurrence probability (e.g., 80%).

[0132] The receiver 220 of the network node 200 receives the inference result.

[0133] In step S15, the control unit 230 of the network node 200 performs a predetermined operation in response to receiving the measurement result. The predetermined operation may be optimization of the measurement configuration. For example, the control unit 230 of the network node 200 may perform the optimization by including an optimal event included in the measurement result in the measurement configuration. Alternatively, the predetermined operation may be an early handover request. For example, the NW communication unit 240 of the network node 200 may transmit a handover request to the target cell before receiving a measurement report based on the optimal event from the UE 100.

[0134] (Another Operation Example According to the First Embodiment) In the first embodiment, an example has been described in which the UE 100 generates an AI / ML model based on measurement event prediction configuration information and derives a trained AI / ML model. For example, the measurement event prediction configuration information (step S10) may include a trained AI / ML model. That is, the trained AI / ML model may be generated in the network node 200 and transmitted to the UE 100. The UE 100 may perform inference using the trained AI / ML model received from the network node 200 (step S13).

[0135] Second Embodiment Next, a second embodiment will be described, focusing on the differences from the first embodiment.

[0136] In the second embodiment, as in the first embodiment, of the three prediction models of AI mobility being discussed in 3GPP, attention is focused on measurement event prediction. Also in the second embodiment, the direct method shown in Fig. 11 (B) is used. However, in the second embodiment, an example of predicting a candidate cell that is a target for transmitting a measurement report in the AI / ML model will be described.

[0137] Specifically, first, a user equipment (e.g., UE 100) monitors the surrounding environment. Second, the user equipment inputs the monitoring results into an AI / ML model to predict candidate cells indicating targets for sending measurement reports. Third, the user equipment transmits the candidate cells to a network node (e.g., network node 200).

[0138] As described above, in the second embodiment, since the candidate cell is transmitted from the UE 100 to the network node 200, for example, the network node 200 can optimize the measurement configuration by changing the measurement configuration to add the candidate cell to a "white cell" that allows event evaluation. Also, for example, the network node 200 can transmit a handover request message to the candidate cell before receiving a measurement report from the UE 100, thereby making it possible to perform a handover request earlier than in the past and optimizing the handover request. That is, the network node 200 can improve the performance of the measurement report based on the candidate cell, as in the first embodiment.

[0139] (Example of operation according to the second embodiment) Fig. 13 is a diagram illustrating an example of operation according to the second embodiment. In Fig. 13, it is assumed that the UE 100 is in an RRC connected state with the network node 200.

[0140] In step S20, the transmission unit 210 of the network node 200 transmits candidate cell prediction configuration information to the UE 100. The candidate cell prediction configuration information indicates information for setting an AI / ML model used for predicting a candidate cell.

[0141] First, the candidate cell prediction setting information may include information indicating whether or not to perform candidate cell prediction for each measurement configuration (Measurement Configuration). Specifically, the candidate cell prediction setting information may include bit information indicating whether or not to perform candidate cell prediction for each measurement configuration identification information (MeasID). Alternatively, the candidate cell prediction setting information may include information indicating whether or not to perform prediction for each conditional configuration (Conditional Configuration) used for conditional handover. Specifically, bit information indicating whether or not to perform prediction for each conditional configuration identification information (Conditional Configuration ID) may be included. Alternatively, the candidate cell prediction setting information may include information indicating whether or not to perform prediction for each LTM configuration (LTM-Config) used in LTM. Specifically, bit information indicating whether or not a prediction is performed may be included for each identification information (Conditional Configuration ID) of the LTM configuration. Note that it is assumed that the UE 100 has received the measurement configuration, the conditional configuration, and / or the LTM configuration from the network node 200 in advance.

[0142] Second, the candidate cell prediction configuration information includes configuration information and / or parameter information of an AI / ML model used for candidate cell prediction. The configuration information may include information on what information is used as an input to the AI / ML model (specifically, what information is used as a monitoring result).

[0143] The candidate cell prediction configuration information may be included in an RRC message (for example, an RRC reconfiguration message or an RRC restart message) and transmitted from the network node 200 to the UE 100. Alternatively, the candidate cell prediction configuration information may be included in an AI / ML layer message newly defined for AI / ML and transmitted. The receiving unit 110 of the UE 100 receives the candidate cell prediction configuration information.

[0144] In step S21, the control unit 130 of the UE 100 generates an AI / ML model to be used for candidate cell prediction. For example, the control unit 130 of the UE 100 generates the AI / ML model by using configuration information of the AI / ML model included in the candidate cell prediction configuration information. Then, the control unit 130 of the UE 100 may generate a trained AI / ML model by performing learning on the generated AI / ML model.

[0145] In step S22, the control unit 130 of the UE 100 monitors the surrounding environment. The control unit 130 of the UE 100 may monitor the surrounding environment according to the setting information of the AI ​​ / ML model included in the candidate cell prediction setting information. The monitoring result may be the same as the monitoring result described in the first embodiment (step S12 in FIG. 12).

[0146] In step S23, the control unit 130 of the UE 100 inputs the monitoring result acquired in step S22 into the learned AI / ML model generated in step S21, and starts inference. Here, the control unit 130 of the UE 100 may start inference in response to an entering condition being satisfied in an event set by a measurement setting having certain identification information (i.e., the event being triggered (entered)). The inference result is a candidate cell indicating a target to which the UE 100 transmits a measurement report. Specifically, for example, it is as follows.

[0147] First, the candidate cell may be an optimal target cell among multiple target cells that satisfy the starting condition. For example, in the AI / ML model, the probability of occurrence of ROF and / or HOF may be inferred for each of the multiple target cells, and the target cell with the lowest probability of occurrence may be determined as the optimal target cell. In this case, the inference result may include the probability of occurrence for each target cell.

[0148] Second, the candidate cell may be a cell other than the target cells that satisfy the initiation condition, i.e., a cell other than the target cell that satisfies the event triggered based on the measurement configuration, or a cell that is not related to the measurement configuration.

[0149] In addition, the control unit 130 of UE 100 may perform inference using an AI / ML model (step S23), and may also evaluate an event based on the measurement setting (or conditional setting, or LTM setting) to determine whether the event will trigger.

[0150] In step S24, the transmitter 120 of the UE 100 transmits the inference result to the network node 200. The inference result may be transmitted by being included in a measurement report, as in the first embodiment. The inference result may be transmitted by being included in another RRC message such as UE assist information. The inference result may be transmitted by being included in a message newly defined for AI / ML (AI / ML Assistance Information message). The inference result includes information indicating the candidate cell (e.g., a cell ID). The inference result may include the probability of occurrence of RLF and / or HOF for each candidate cell inferred by the AI / ML model. Alternatively, the inference result may include measurement results (such as beam level measurements of L1 and / or cell level measurements of L3) as in the first embodiment. The inference result may include the monitoring result of step S22.

[0151] The receiver 220 of the network node 200 receives the inference result.

[0152] In step S25, the control unit 230 of the network node 200 performs a predetermined operation in response to receiving the measurement result. As described above, the predetermined operation may be an operation of changing the measurement configuration by adding the candidate cell to the "white cells" included in the measurement configuration. Alternatively, the predetermined operation may be an operation of the transmission unit 210 of the network node 200 transmitting a handover request message to the candidate cell before receiving the measurement report from the UE 100, thereby performing a handover request earlier than conventionally. Alternatively, when the transmission unit 210 of the network node 200 transmits the context of the UE 100 to the candidate cell in the handover request and the UE 100 enters HOF, the control unit 230 of the network node 200 performs an RRC reestablishment procedure, but may complete the procedure earlier because the context of the UE 100 has been transmitted to the candidate cell.

[0153] (Another Operation Example According to the Second Embodiment) In the second embodiment, an example (step S21) has been described in which the UE 100 generates an AI / ML model based on candidate cell prediction configuration information and derives a trained AI / ML model. For example, the candidate cell prediction configuration information (step S20) may include a trained AI / ML model. That is, the trained AI / ML model may be generated in the network node 200 and transmitted to the UE 100. The UE 100 may perform inference using the trained AI / ML model received from the network node 200 (step S23).

[0154] [Other Embodiments] In the first embodiment described above, supervised learning has been mainly described, but the present invention is not limited to this. For example, unsupervised learning or reinforcement learning may be applied to the first embodiment.

[0155] The above-described operational flows are not limited to being implemented independently, but can be implemented by combining two or more operational flows. For example, some steps of one operational flow may be added to another operational flow, or some steps of one operational flow may be replaced with some steps of another operational flow. In each flow, it is not necessary to execute all steps, and only some steps may be executed. Furthermore, the order of steps in each flow may be changed as appropriate.

[0156] In the above embodiment, an example in which the base station is an NR base station (gNB) has been described, but the base station may be an LTE base station (eNB) or a 6G base station. 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 the IAB node. The UE 100 may also be an MT (Mobile Termination) of the IAB node. That is, the UE 100 may be a terminal function unit (a type of communication module) for the base station to control a relay that relays signals. Such a terminal function unit is referred to as an MT. Examples of MTs include, in addition to IAB-MT, NCR (Network Controlled Repeater)-MT and RIS (Reconfigurable Intelligent Surface)-MT.

[0157] 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). A network node may also be configured by a combination of at least a part of a core network device and at least a part of a base station.

[0158] A program may be provided that causes a computer to execute each process according to the above-described embodiments. The program may be recorded on a computer-readable medium. The computer-readable medium can be used to install the program on a computer. Here, the computer-readable medium on which the program is recorded may be a non-transitory storage medium. The non-transitory storage medium is not particularly limited, and may be, for example, a storage medium such as a CD-ROM or a DVD-ROM. Furthermore, circuits that execute each process performed by the device according to the above-described embodiments may be integrated, and at least a part of the device may be configured as a semiconductor integrated circuit (chipset, SoC).

[0159] The functions performed by the apparatus according to the above-described embodiments may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), a central processing unit (CPU), conventional circuits, and / or combinations thereof, programmed to perform the described functions. A processor includes transistors and other circuits and is considered to be circuitry or processing circuitry. A processor may also be a programmed processor that executes a program stored in a memory. In this disclosure, circuitry, units, and means refer to hardware that is programmed to perform or executes the described functions. The hardware may be any hardware disclosed herein or any hardware known to be programmed or capable of performing the described functions. If the hardware is a processor, the circuitry, means, or unit is a combination of the hardware and software used to configure the hardware and / or processor.

[0160] As used in this disclosure, the terms "based on" and "depending on / in response to" do not mean "based only on" or "depending only on," unless expressly stated otherwise. The term "based on" means both "based only on" and "based at least in part on." Similarly, the term "depending on" means both "depending only on" and "depending at least in part on." The terms "include," "comprise," and variations thereof do not mean including only the listed items, but may mean including only the listed items or may include additional items in addition to the listed items. Additionally, the term "or," as used in this disclosure, is not intended to mean an exclusive or. Furthermore, 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 method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed therein or that the first element must precede the second element in some way. In this disclosure, where articles are added by translation, such as a, an, and the in English, these articles shall include the plural unless the context clearly indicates otherwise.

[0161] Although the embodiments have been described in detail above with reference to the drawings, the specific configuration is not limited to the above, and various design changes can be made within the scope of the gist. Furthermore, the embodiments, operation examples, and processes can be combined as appropriate within the scope of not causing any contradiction.

[0162] This application claims priority to U.S. Provisional Application No. 63 / 679,721 (filed August 6, 2024), the entire contents of which are incorporated herein by reference.

[0163] (Additional Notes) The above can be summarized as in the additional notes, but the additional notes do not limit the embodiments.

[0164] (Supplementary Note 1) A communication control method in a mobile communication system, comprising: a step in which a user equipment monitors a surrounding environment; a step in which the user equipment inputs the monitoring results into an AI / ML model to predict an event indicating a condition for transmitting a measurement report; and a step in which the user equipment transmits the event to a network node.

[0165] (Supplementary Note 2) The communication control method according to Supplementary Note 1, wherein the event is an event that is not configured by a measurement configuration in the user equipment.

[0166] (Supplementary Note 3) The communication control method according to Supplementary Note 1 or Supplementary Note 2, wherein the event is an optimal event among events configured by measurement configuration in the user device.

[0167] (Supplementary Note 4) The communication control method according to any one of Supplementary Note 1 to Supplementary Note 3, wherein the event is an event that is predicted to occur in the user device in the future.

[0168] (Supplementary Note 5) The communication control method according to any one of Supplementary Note 1 to Supplementary Note 4, wherein the transmitting step includes a step in which the user equipment transmits, together with the event, an expected time at which the event will occur and an occurrence probability of the event to the network node.

[0169] (Supplementary Note 6) The communication control method according to any one of Supplementary Note 1 to Supplementary Note 5, wherein the monitoring result includes at least one of radio measurement values ​​for a serving cell and radio measurement values ​​for neighboring cells adjacent to the serving cell, location information of the user equipment, movement information of the user equipment, information measured by an application executed in the user equipment, and information about the neighboring cells.

[0170] (Supplementary Note 7) A user equipment in a mobile communication system, comprising: a control unit that monitors a surrounding environment, inputs the monitoring results into an AI / ML model, and predicts an event indicating a condition for transmitting a measurement report; and a transmission unit that transmits the event to a network node.

[0171] 1: Mobile communication system 10: NW 20: RAN 30: CN 100: UE 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

Claims

1. A communication control method in a mobile communication system, comprising: a user equipment monitoring a surrounding environment; the user equipment inputting the monitoring results into an AI (Artificial Intelligence) / ML (Machine Learning) model to predict an event indicating a condition for transmitting a measurement report; and the user equipment transmitting the event to a network node.

2. A communication control method according to claim 1, wherein the event is an event that is not set by measurement configuration in the user device.

3. A communication control method according to claim 1, wherein the event is the most appropriate event among events set by measurement settings in the user device.

4. A communication control method according to claim 1, wherein the event is an event that is predicted to occur in the user device in the future.

5. A communication control method according to claim 4, wherein said transmitting step includes said user equipment transmitting to said network node, together with said event, an expected time at which said event will occur and a probability of said event occurring.

6. A communication control method as described in claim 1, wherein the monitoring results include at least one of radio measurement values ​​for a serving cell and radio measurement values ​​for neighboring cells adjacent to the serving cell, location information of the user equipment, movement information of the user equipment, information measured by an application executed in the user equipment, and information regarding the neighboring cells.

7. A user equipment in a mobile communication system, comprising: a control unit that monitors a surrounding environment, inputs the monitoring results into an AI / ML model, and predicts an event indicating a condition for transmitting a measurement report; and a transmission unit that transmits the event to a network node.

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