Communication method, user device, and network node

Abstract

This communication method to be executed by a user device in a mobile communication system involves: performing event prediction for predicting whether a measurement report event triggering transmission of a measurement report to a network node will occur in the future or not, by using an artificial intelligence or machine learning (AI / ML) model in a radio resource control (RRC) connected state in which the user device is connected to the network node; and the user device transmitting a message pertaining to the event prediction results to the network node in response to a prediction that the measurement report event will occur in the future.

Description

COMMUNICATION METHOD, USER EQUIPMENT, AND NETWORK NODE

[0001] The present disclosure relates to a communication method, a user equipment, and a network node for use in a mobile communication system.

[0002] 3GPP (Third Generation Partnership Project) (registered trademark; the same applies hereinafter), a standardization project for mobile communication systems, is considering applying artificial intelligence or machine learning (also referred to as "AI / ML") technology to wireless communication (i.e., air interface) of mobile communication systems.

[0003] 3GPP contribution: RP-234055, “Study on Artificial Intelligence (AI) / Machine Learning (ML) for mobility in NR”

[0004] A communication method according to a first aspect is a communication method executed by a user equipment in a mobile communication system, the method comprising: in a Radio Resource Control (RRC) Connected state in which the user equipment is connected to a network node, performing event prediction using an artificial intelligence or machine learning (AI / ML) model to predict whether a measurement report event that will trigger transmission of a measurement report to the network node will occur in the future; and in response to the user equipment predicting that the measurement report event will occur in the future, transmitting a message regarding the result of the event prediction to the network node.

[0005] A user equipment according to a second aspect is a user equipment used in a mobile communication system, and in a radio resource control (RRC) connected state in which the user equipment is connected to a network node, the user equipment having: a control unit that performs event prediction using an artificial intelligence or machine learning (AI / ML) model to predict whether a measurement report event that will trigger the transmission of a measurement report to the network node will occur in the future; and a transmission unit that transmits a message regarding the result of the event prediction to the network node in response to predicting that the measurement report event will occur in the future.

[0006] A network node according to a third aspect is a network node for use in a mobile communication system, the network node comprising: a receiving unit configured to receive, from a user equipment in a Radio Resource Control (RRC) Connected state connected to the network node, a message relating to a result of event prediction using an artificial intelligence or machine learning (AI / ML) model; and a control unit configured to perform mobility control for the user equipment based on the message. The event prediction is a process of predicting whether a measurement report event that will trigger transmission of a measurement report to the network node will occur in the future. The message indicates that the measurement report event is predicted to occur in the future.

[0007] 1 is a diagram showing an example of the configuration of a mobile communication system according to an embodiment. FIG. 2 is a diagram showing an example of the configuration of a UE (user equipment) according to an embodiment. FIG. 3 is a diagram showing an example of the configuration of a gNB (network node) according to an embodiment. FIG. 4 is a diagram showing a protocol stack configuration of a radio interface of a user plane that handles data. FIG. 5 is a diagram showing a protocol stack configuration of a radio interface of a control plane that handles signaling (control signals). FIG. 6 is a diagram showing a configuration related to measurements by a UE according to an embodiment. FIG. 7 is a diagram showing a functional block configuration of AI / ML technology in a mobile communication system according to an embodiment. FIG. 8 is a diagram showing an example of an operation scenario of a mobile communication system according to an embodiment. FIG. 9 is a diagram showing another example of an operation scenario of a mobile communication system according to an embodiment. FIG. 10 is a diagram showing a specific example of operation according to an embodiment. FIG. 11 is a diagram for explaining operation according to a modified example.

[0008] The technology described in the background art above is considered to be a use case of the AI / ML technology for mobility control of user equipment. For example, it is considered to apply the AI / ML technology to control switching of a serving cell from a source cell to a target cell. However, a specific mechanism for applying the AI / ML technology to mobility control of user equipment has not yet been established, making it difficult to utilize the AI / ML technology in a mobile communication system.

[0009] The present disclosure aims to utilize AI / ML technology in mobile communication systems.

[0010] A mobile communication system according to an 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.

[0011] (1) Configuration of a Mobile Communication System FIG. 1 is a diagram showing an example of the configuration of a mobile communication system 1 according to an embodiment. The mobile communication system 1 conforms to the 3GPP standard 5th Generation System (5GS). While the following description uses 5GS as an example, the mobile communication system may also be at least partially based on an LTE (Long Term Evolution) system. The mobile communication system may also be at least partially based on a 6th Generation (6G) system.

[0012] The mobile communication system 1 includes a user equipment (UE) 100, a 5G radio access network (NG-RAN: Next Generation Radio Access Network) 10, and a 5G core network (5GC: 5G Core Network) 20. Hereinafter, the NG-RAN 10 may be simply referred to as the RAN 10. Furthermore, the 5GC 20 may be simply referred to as the core network (CN) 20. The RAN 10 and the CN 20 constitute a network 5 of the mobile communication system 1.

[0013] The UE 100 is a mobile wireless communication device. The UE 100 may be any device used by a user. For example, the UE 100 may be a mobile phone terminal (which may be a smartphone) and / or a tablet terminal, a notebook PC, a communication module (which may be 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). A link in the transmission direction from the UE 100 to the network 5 is referred to as an uplink (UL), and a link in the transmission direction from the network 5 to the UE 100 is referred to as a downlink (DL).

[0014] The NG-RAN 10 includes a base station (referred to as "gNB" in the 5G system) 200, which is a type of network node. The gNBs 200 are connected to each other via an Xn interface, which is an interface between base stations. The gNB 200 manages one or more cells. The gNB 200 performs wireless communication with the UE 100 that has established a connection with its own cell. The gNB 200 has a radio resource management (RRM) function, a routing function for user data (hereinafter simply referred to as "data"), a measurement control function for mobility control and scheduling, etc. 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 (hereinafter simply referred to as "frequency").

[0015] In addition, gNBs can also be connected to the Evolved Packet Core (EPC), which is the core network of LTE. LTE base stations can also be connected to 5GC. LTE base stations and gNBs can also be connected via an inter-base station interface.

[0016] The 5GC20 includes an AMF (Access and Mobility Management Function) and a UPF (User Plane Function) 300. The AMF performs various mobility controls for the UE 100. The AMF manages the mobility of the UE 100 by communicating with the UE 100 using NAS (Non-Access Stratum) signaling. The UPF controls data forwarding. The AMF and the UPF are connected to the gNB 200 via an NG interface, which is an interface between a base station and a core network.

[0017] 2 is a diagram illustrating an example configuration of a UE 100 (user equipment) according to an 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 wireless communication unit 140 that performs wireless communication with the gNB 200.

[0018] The receiving unit 110 performs various reception operations 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 operations of the UE 100 described above and below may be operations controlled by the control unit 230. 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 processing by the processor. The processor may include a baseband processor and a CPU (Central Processing Unit). The baseband processor performs modulation / demodulation and encoding / decoding of baseband signals. The CPU executes programs stored in the memory to perform various processes.

[0021] 3 is a diagram showing an example configuration of a gNB 200 (network node) according to an embodiment. The gNB 200 has a transmitter 210, a receiver 220, a controller 230, and a network communication unit 240. The transmitter 210 and the receiver 220 constitute a wireless communication unit 250 that performs wireless communication with the UE 100. The network communication unit 240 has a transmitter 241 that transmits and a receiver 242 that receives.

[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 gNB 200. Such processes include processes for each layer described below. The operations of the gNB 200 described above and below may be operations under the control of the control unit 230. 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 processing by the processor. The processor may include a baseband processor and a CPU. The baseband processor performs modulation / demodulation and encoding / decoding of baseband signals. The CPU executes programs stored in the memory to perform various processes.

[0025] The network communication unit 240 is connected to adjacent base stations via an Xn interface, which is an interface between base stations. The network communication unit 240 is connected to the AMF / UPF 300 via an NG interface, which is an interface between a base station and a core network. The gNB 200 is composed of a CU (Central Unit) and a DU (Distributed Unit) (i.e., functionally divided), and the two units may be connected by an F1 interface, which is a fronthaul interface.

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

[0027] The user plane radio 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 UE100 and the PHY layer of gNB200 via a physical channel. The PHY layer of UE100 receives downlink control information (DCI) transmitted from gNB200 on a physical downlink control channel (PDCCH). Specifically, UE100 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 gNB200 has a CRC (Cyclic Redundancy Code) parity bit scrambled by the RNTI added.

[0029] 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 the UE 100 and the MAC layer of the gNB 200 via a transport channel. The MAC layer of the gNB 200 includes a scheduler. The scheduler determines the uplink and downlink transport format (transport block size, modulation and coding scheme (MCS)) and the resource blocks to be allocated to the UE 100.

[0030] 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 the UE 100 and the RLC layer of the gNB 200 via a logical channel.

[0031] The PDCP layer performs header compression / decompression, encryption / decryption, and the like.

[0032] 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.

[0033] 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).

[0034] The protocol stack of the radio interface of the control plane has an RRC (Radio Resource Control) layer and an NAS (Non-Access Stratum) layer instead of the SDAP layer shown in FIG.

[0035] RRC signaling for various settings is transmitted between the RRC layer of UE100 and the RRC layer of gNB200. The RRC layer controls logical channels, transport channels, and physical channels according to the establishment, re-establishment, and release of radio bearers. When there is a connection (RRC connection) between the RRC of UE100 and the RRC of gNB200, UE100 is in an RRC connected state. When there is no connection (RRC connection) between the RRC of UE100 and the RRC of gNB200, UE100 is in an RRC idle state. When the connection between the RRC of UE100 and the RRC of gNB200 is suspended, UE100 is in an RRC inactive state.

[0036] The NAS layer (also simply referred to as "NAS") located above the RRC layer performs session management, mobility management, etc. NAS signaling is transmitted between the NAS layer of the UE 100 and the NAS layer of the AMF 300A. Note that the UE 100 has an application layer and the like in addition to the radio interface protocol. Also, a layer lower than the NAS layer is referred to as the AS layer (also simply referred to as "AS").

[0037] (2) Measurement by UE The UE 100 in the RRC connected state performs measurements used for mobility control. Measurements include intra-frequency measurements within the same frequency as the current serving cell and inter-frequency measurements within a frequency different from that of the current serving cell. Measurements also include inter-RAT measurements for a RAT (Radio Access Technology) different from that of the current serving cell. The gNB 200 transmits a measurement configuration (measurement command) to the UE 100 via RRC to instruct the UE 100 to start, change, or stop the measurement.

[0038] For each measurement type, one or more measurement targets (also called "measurement objects") can be defined. A measurement object is, for example, a monitored frequency (carrier frequency), but can also be a cell. For each measurement object, one or more reporting configurations can be defined, which define the reporting criteria. There are three reporting criteria (types of measurement reports): event-triggered reporting, periodic reporting, and event-triggered periodic reporting.

[0039] Such a measurement report is also referred to as an L3 measurement report or an RRC measurement report. In the embodiment, event-triggered reporting and event-triggered periodic reporting are mainly assumed. In the event-triggered reporting, the UE 100 triggers the transmission of a single measurement report in response to a measurement report event configured for the UE 100 in the reporting configuration being satisfied. In the event-triggered periodic reporting, the UE 100 triggers the transmission of a periodic measurement report in response to a measurement report event configured for the UE 100 in the reporting configuration being satisfied.

[0040] The measurement report event type set in the UE 100 by the reporting configuration may be, for example, Event A1 (Serving becomes better than threshold), Event A2 (Serving becomes worse than threshold), or Event A3 (Neighborhood becomes amount of offset better than PCell / PSCell). Event A1 is a measurement report event that the measurement result of the current serving cell has become better than a threshold. Event A2 is a measurement report event that the measurement result of the current serving cell has become worse than a threshold. Event A3 is a measurement report event that indicates that the measurement result of a neighboring cell has become better than the measurement result of the current serving cell (specifically, PCell / PSCell) plus an offset value. The reporting configuration may include a configuration of such an event type and a configuration of a threshold and / or offset value used for the event type.

[0041] The reporting configuration of the event-triggered reporting and the event-triggered periodic reporting may include a TTT (Time To Trigger) configuration. The UE 100 triggers the transmission of a single or periodic measurement report when a state in which a measurement reporting event set in the UE 100 in the reporting configuration is satisfied continues for the TTT time. "Occurrence of a measurement reporting event" may mean that the condition of the measurement reporting event is satisfied and the state continues for the TTT time.

[0042] The association between a measurement object (measurement object ID) and a reporting configuration (measurement configuration ID) is made by a measurement identity (measurement ID). The measurement ID links one measurement object and one reporting configuration of the same RAT. Using multiple measurement IDs (one per measurement object-reporting configuration pair) allows associating multiple reporting configurations with one measurement object or one reporting configuration with multiple measurement objects. The measurement ID is also used when reporting measurement results.

[0043] The UE 100 in the RRC connected state measures at least one beam of a cell and averages the measurement results (power values) to derive the radio quality (also referred to as "reception quality") for the cell. In this case, the UE 100 is configured to consider a subset of the detected beams. Here, filtering, which is measurement averaging, is performed at two different levels. The UE 100 first derives beam quality by L1 filtering, which is filtering at the physical layer (PHY, Layer 1 (L1)), and then derives cell quality from multiple beams by L3 filtering, which is filtering at the RRC layer (Layer 3 (L3)) level. Note that the cell quality from beam measurements is derived in the same way for serving and non-serving cells. Depending on the configuration by the gNB 200, the UE 100 may include measurement results of the X best beams in the L3 measurement report.

[0044] 6 is a diagram showing a configuration related to measurements by the UE 100 according to the embodiment. The control unit 130 of the UE 100 includes an L1 filter 11, a beam combining / selecting unit 12, an L3 filter 13, an evaluation unit 14, an L3 beam filter 15, and a beam selecting unit 16. The control unit 130 receives input of the radio quality of a beam measured by any of the receivers 111 in the receiving unit 110. In the illustrated example, there are two receivers 111 (receiver 111a and receiver 111b), but there may be one receiver 111, or three or more receivers 111. Hereinafter, the receiver 111 is also referred to as an "RF (Radio Frequency) chain." The multiple receivers 111 may support different frequencies.

[0045] The L1 filter 11 includes K L1 filters 11 corresponding to the K beams. K measurement results A obtained by the UE 100 (receiving unit 110) measuring the radio quality for each of the K beams are input to the L1 filter 11. The K measurement results A for the K beams are measurement results (beam-specific samples) within the physical layer, and are measurement results of SSB (SS / PBCH block) or CSI (Channel State Information) reference signal resources detected by the UE 100 (receiving unit 110) in L1. The L1 filter 11 performs L1 filtering on the K measurement results A for the K beams in L1, and outputs the beam-specific measurement results A after the L1 filtering. １ are output to the beam combining / selecting unit 12 and the L3 beam filter 15.

[0046] The beam integration / selection unit 12 outputs the beam-specific measurement result A １ to derive the cell radio quality (Cell quality) B, and output the cell quality B to the L3 filter 13. The operation setting of the beam combining / selecting unit 12 is provided by RRC signaling from the gNB 200.

[0047] The L3 filter 13 filters the measurement result (cell quality B) output by the beam combining / selecting unit 12 at L3 and outputs the measurement result C after L3 filtering to the evaluation unit 14. The configuration of the operation of the L3 filter 13 is provided by RRC signaling from the gNB 200. The measurement result C after L3 filtering is used as input for one or more evaluations of an L3 measurement report from the UE 100 to the gNB 200.

[0048] The L3 filter 13 filters the measurement results for each cell measurement and each beam measurement by the following equation (1) before using them for evaluation of reporting criteria or for L3 measurement reporting: F ｎ = (1 - a) x F ｎ－１ + a × M ｎ ...(1) where M ｎ is the latest measurement result from the physical layer (L1). ｎF is the updated filtered measurement result, which is used for evaluation of reporting criteria or L3 measurement reporting. ｎ－１ is the old filtered measurement, F is the measurement result when the first measurement is received from the physical layer (L1). ０ M １ is set to

[0049] When MeasObjectNR is set in RRC, a = 1 / 2 （ｋｉ／４） Here, k ｉ is the filter coefficient of the corresponding measurement of the ith QuantityConfigNR in the quantityConfigNR-List, where i is indicated by the quantityConfigIndex in the MeasObjectNR. For other measurements, a=½ （ｋ／４） where k is the filter coefficient of the corresponding measurement received by quantityConfig.

[0050] The L3 filter 13 adapts the filter so that its time characteristics are preserved at different input rates, while the filter coefficient k assumes a sample rate equal to X ms, where the value of X corresponds to one intra-frequency L1 measurement period assuming non-DRX operation and is frequency range dependent.

[0051] Note that if the filter coefficient k is set to 0 (zero), no L3 filtering is applied.

[0052] The evaluation unit 14 evaluates whether an L3 measurement report D to the gNB 200 is necessary. This evaluation can be performed based on a comparison of multiple measurement flows at the reference point C, for example, different measurement results. This is done by comparing input C and input C １ The evaluation unit 14 determines whether at least the new measurement results are at points C, C １Each time a measurement report is made, a measurement reporting event evaluation corresponding to the reporting criteria is performed. The reporting criteria setting is provided by RRC signaling from the gNB 200. The L3 measurement report D represents measurement report information (RRC message) transmitted from the UE 100 to the gNB 200. The L3 measurement report D includes the measurement ID of the associated measurement setting that triggered the report.

[0053] The L3 beam filter 15 receives k measurement results A １ (i.e., beam-specific measurement results) are filtered on a per-beam basis, and k measurement results E (i.e., beam-specific measurement results) are output to the beam selector 16. The measurement results E are used as input to select the X measurement results to be reported.

[0054] The beam selection unit 16 selects X measurement results F from the k measurement results E and outputs the X measurement results F. The X measurement results F are beam measurement information included in the measurement report information (RRC message) transmitted from E100 to gNB200.

[0055] (3) Overview of AI / ML Technology An overview of AI / ML technology will be described. A mobile communication system 1 according to an embodiment applies AI / ML technology to wireless communication (i.e., air interface).

[0056] 7 is a diagram showing a functional block configuration of the AI / ML technology in the mobile communication system 1 according to the embodiment. The functional block configuration includes a data collection unit A1, a model learning unit A2, a model inference unit A3, and a data processing unit A4.

[0057] The data collection unit A1 collects input data, specifically, learning data and inference data, outputs the learning data to the model learning unit A2, and outputs the inference data to the model inference unit A3. The data collection unit A1 may acquire data in the device on which the data collection unit A1 is provided as input data. The data collection unit A1 may also acquire data in another device as input data.

[0058] The model learning unit A2 performs model learning (also referred to as "learning processing"). Specifically, the model learning unit A2 optimizes parameters of a learning model (hereinafter also referred to as a "model" or an "AI / ML model") through machine learning using learning data, derives (generates and updates) a learned model, and outputs the learned model to the model inference unit A3. The model is a data-driven algorithm that applies AI / ML technology to generate a set of outputs based on a set of inputs. For example, considering y = ax + b, a (slope) and b (intercept) are parameters, and optimizing these corresponds to machine learning. Generally, machine learning is classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a method that uses correct answer data as learning data. Unsupervised learning is a method that does not use correct data for training data. For example, in unsupervised learning, feature points are memorized from a large amount of training data and the correct answer is determined (range estimation). Reinforcement learning is a method that assigns a score to the output result and learns how to maximize the score.

[0059] The model inference unit A3 performs model inference (also referred to as "inference processing"). Specifically, the model inference unit A3 infers an output from inference data using a trained model and outputs the inference result data to the data processing unit A4. For example, in the case of y = ax + b, x corresponds to the inference data and y corresponds to the inference result data. Note that "y = ax + b" is a model. A model in which the slope and intercept are optimized, for example, "y = 5x + 3", is a trained model. There are various modeling techniques, including linear regression analysis, neural networks, and decision tree analysis. The above "y = ax + b" can be considered a type of linear regression analysis. The model inference unit A3 may provide model performance feedback to the model learning unit A2.

[0060] The data processing unit A4 receives the inference result data and performs processing that utilizes the inference result data.

[0061] A case in which model learning and / or model inference is performed on the network 5 side, i.e., a case in which the network 5 has an AI / ML model, is referred to as a network-sided model. A case in which model learning and / or model inference is performed on the UE 100 side, i.e., a case in which the UE 100 has an AI / ML model, is referred to as a UE-sided model.

[0062] (4) Mobility Control Using UE-Side Model In the embodiment, the AI / ML technique is applied to mobility control of the UE 100. Specifically, when the UE 100 is in an RRC-connected state, the AI / ML technique is applied to handover in which the RRC layer takes the lead in switching the serving cell (primary cell) of the UE 100.

[0063] The UE 100 has an AI / ML model for predicting (specifically, predicting using model inference) whether a measurement report event will occur in the future. That is, in the embodiment, a UE-side model is used.

[0064] "A measurement report event will occur in the future" may mean that a measurement report event will not occur in the current radio environment, but will occur in the near future. Here, the "current radio environment" may mean a current measurement result for a measurement object. The "measurement object" is one of a cell, a frequency, a beam, a reference signal, and a measurement object. The reference signal (RS) may be an SSB (synchronization signal (SS) / physical broadcast channel (PBCH)), a channel state information (CSI)-RS, a demodulation (DM)-RS, or the like. In the embodiment, an example in which the measurement object is a cell (or a frequency) will be mainly described. Also, as described above, "a measurement report event will occur" may mean that a condition for a measurement report event is satisfied and that this state continues for the time TTT.

[0065] FIG. 8 is a diagram showing an example of an operation scenario of the mobile communication system 1 according to the embodiment.

[0066] In the illustrated example, the UE 100 is in an RRC connected state with cell a of the gNB 200 as the serving cell. There are multiple neighboring cells (cell b, cell c, cell d) that partially overlap with this serving cell. The neighboring cells may be managed by the same gNB 200 as the serving cell. The neighboring cells may be managed by a gNB 200 different from the serving cell. The frequencies of each cell may be the same or different.

[0067] In step S1 (measurement configuration), the gNB 200 transmits an RRC message including a measurement configuration to the UE 100. The UE 100 receives the measurement configuration and starts measurement according to the measurement configuration. The measurement configuration may include a measurement object (and its ID), a reporting configuration (and its ID), and a measurement ID. An event-triggered report (or an event-triggered periodic report) may be configured in the UE 100 by the reporting configuration. For example, Event A1, Event A2, and / or Event A3 may be configured in the UE 100 as the measurement report event type in the reporting configuration. In an embodiment, the measurement configuration (reporting configuration) may include configuration information for configuring event prediction (also referred to as "event prediction configuration"). The configuration information for configuring event prediction may be configured in the UE 100 in association with the measurement ID.

[0068] In step S2 (measurement), the UE 100 performs measurement on a measurement target (in the embodiment, a cell) determined according to the measurement object. For example, the UE 100 measures the reception quality of each cell belonging to each frequency specified in the measurement object and / or each cell specified in the measurement object to obtain a measurement result. The "measurement result" may be a set of an ID of the measurement target and its measurement value. The "measurement value" may be a reference signal received power (RSRP), a reference signal radio quality (RSRQ), and / or a signal-to-interference-and-noise ratio (SINR), etc.

[0069] In step S3 (model inference), UE 100 predicts whether a measurement report event will occur in the future based at least in part on the measurement result in step S2 (measurement) using AI / ML model 101. That is, UE 100 uses AI / ML model 101 to perform event prediction that predicts whether a measurement report event that triggers transmission of a measurement report to gNB 200 will occur in the future. AI / ML model 101 outputs a prediction result of when a measurement report event will occur by event prediction as an event prediction result. The prediction result may include a value indicating the probability (likelihood) that the measurement report event will occur in the future, i.e., a value indicating the prediction accuracy.

[0070] For example, UE100 uses the AI / ML model 101 to predict whether a measurement report event will occur in the future, using parameters such as time-series measurement results for each cell, UE100's location data (measured values), UE100's movement speed (measured values), and cell frequency (set value) as inference data.

[0071] The AI / ML model 101 possessed by the UE 100 is assumed to have learned the correlation between these parameters through model learning. For example, the AI / ML model 101 learns the causal relationship between parameters including measurement results for each measurement target and the measurement report event occurrence history for each cell through model learning. Note that at least a partially trained AI / ML model 101 may be set from the gNB 200 to the UE 100. For example, the AI / ML model 101 may be transferred from the gNB 200 to the UE 100 when communication between the gNB 200 and the UE 100 starts. Alternatively, the UE 100 may generate the trained AI / ML model 101 by performing model learning in various environments.

[0072] In step S4 (measurement report), UE100 transmits a measurement report to gNB200. In the prior art, UE100 transmits the measurement results of each cell to gNB200 by an RRC message (measurement report) at a timing determined according to a reporting setting associated with a measurement object, for example, at a timing when a measurement report event occurs.

[0073] In contrast, in the embodiment, the UE 100 transmits a measurement report to the gNB 200 in response to the prediction that a measurement report event will occur in the future by the model inference (event prediction) of step S3. The measurement report is an example of a message regarding the result of the event prediction. The measurement report includes a measurement ID associated with the measurement report event predicted to occur in the future.

[0074] The UE 100 may trigger transmission of a measurement report before the measurement report event occurs in response to prediction that a measurement report event will occur in the future by the model inference (event prediction) of step S3. Alternatively, the UE 100 may trigger transmission of a measurement report by regarding the measurement report event as having occurred in response to prediction that a measurement report event will occur in the future by the model inference (event prediction) of step S3.

[0075] The gNB 200 that receives the measurement report performs mobility control of the UE 100 based on the measurement report. For example, the gNB 200 determines a target cell for handover and performs mobility control to switch the serving cell from the current serving cell (source cell) to the target cell.

[0076] Thus, according to the embodiment, the UE 100, in response to the model inference (event prediction) of step S3 predicting that a measurement report event will occur in the future, triggers the transmission of a measurement report before the measurement report event occurs and / or assuming that the measurement report event has occurred. This allows the gNB 200 to recognize the need for mobility control (e.g., handover) of the UE 100 at an earlier stage, thereby improving mobility control.

[0077] The UE 100 that performs such an operation has a control unit 130 that performs event prediction using the AI / ML model 101 in an RRC connected state connected to the gNB 200 to predict whether a measurement report event that triggers the transmission of a measurement report to the gNB 200 will occur in the future, and a transmission unit 120 that transmits a message regarding the result of the event prediction to the gNB 200 in response to the prediction that the measurement report event will occur in the future (see FIG. 2). Meanwhile, the gNB 200 has a reception unit 220 that receives a message regarding the result of the event prediction using the AI / ML model 101 from the UE 100 in the RRC connected state, and a control unit 230 that performs mobility control for the UE 100 based on the message (see FIG. 3). The gNB 200 may further have a transmission unit 210 that transmits configuration information for setting the event prediction to the UE 100.

[0078] In the example of FIG. 8 , the AI / ML model 101 outputs an event prediction result indicating when a measurement report event will occur, based at least in part on the measurement result of each cell in step S2 (measurement). However, as shown in FIG. 9 , the AI / ML model 101 may output a predicted value (prediction result) of the measurement result of each cell in the future, based at least in part on the measurement result of each cell in step S2 (measurement). The prediction result may be a time-series prediction result for each cell. The evaluation unit 14 may output an event prediction result indicating when a measurement report event will occur, based on the prediction result for each cell output by the AI / ML model 101.

[0079] A specific example of the operation according to the embodiment will be described. Fig. 10 is a diagram showing a specific example of the operation according to the embodiment. Here, the overlapping description of the operation that overlaps with the above-mentioned operation will be omitted.

[0080] In step S101, UE100 is in an RRC connected state with the cell of gNB200 as the serving cell.

[0081] In step S102, gNB200 transmits measurement configuration and event prediction configuration to UE100. UE100 receives the measurement configuration and event prediction configuration. The measurement configuration and event prediction configuration may be transmitted from gNB200 to UE100 in an RRC message (e.g., an RRC Reconfiguration message). The event prediction configuration may be included in the measurement configuration as part of the measurement configuration (e.g., part of the reporting configuration). The event prediction configuration may be associated with a measurement ID.

[0082] The event prediction setting may include a setting specifying a future time range for predicting occurrence of a measurement report event in the event prediction, and the setting may be information specifying how far in advance (how far in the future) the occurrence of a measurement report event is predicted (e.g., 50 ms ahead, 100 ms ahead, etc.).

[0083] The event prediction setting may include a setting that specifies a probability threshold (e.g., 80%) that must be met for the event prediction to consider a measurement reporting event to occur in the future. If the probability value output by the AI / ML model 101 exceeds the threshold, the UE 100 may consider a measurement reporting event to occur in the future.

[0084] The event prediction setting may include a setting for specifying a measurement report event type to which the event prediction is to be applied. The setting may be information for specifying which type of measurement report event (e.g., Event A1, Event A2, or Event A3) the prediction corresponds to. The event prediction setting may include a setting for specifying a model ID of the AI / ML model 101 to be used for the event prediction. When the UE 100 has a plurality of AI / ML models 101, the UE 100 may select the AI / ML model 101 with the specified model ID and perform event prediction using the selected AI / ML model 101.

[0085] In step S103, the UE 100 performs measurement of each measurement target (each cell) based on the measurement configuration in step S102.

[0086] In step S104, the UE 100 performs event prediction using the AI / ML model 101 based on the measurement result of step S103. When a threshold is set in the event prediction setting, the UE 100 may predict that a measurement report event will occur when the probability value output by the AI / ML model 101 exceeds the threshold. The UE 100 may predict which type of measurement report event will occur.

[0087] In step S104, when the condition of the measurement report event is satisfied, the UE 100 may predict whether the state in which the condition of the measurement report event is satisfied will continue for a time TTT. For example, the UE 100 infers whether the condition will be satisfied for a time TTT when the condition of Event A3 is satisfied and timing of the TTT is started (i.e., when the radio environment first satisfies the condition). Then, when the UE 100 predicts that the condition will be satisfied for a time TTT, the UE 100 may predict that a measurement report event will occur even if the time TTT has not elapsed.

[0088] If the UE 100 does not predict that a measurement report event will occur (step S105: NO), the UE 100 returns the process to step S103.

[0089] On the other hand, if it is predicted that a measurement report event will occur (step S105: YES), in step S106, the UE 100 triggers transmission of a measurement report before the measurement report event occurs or assumes that the measurement report event has occurred, and transmits the measurement report to the gNB 200. The measurement report includes a measurement ID associated with the measurement report event predicted to occur. The measurement report may include measurement results of each measurement object measured in step S103. The measurement results may include measurement results of each measurement object predicted in step S104.

[0090] In step S107, the gNB 200 that has received the measurement report from the UE 100 performs mobility control of the UE 100 based on the received measurement report. For example, the gNB 200 hands over the UE 100 to an appropriate cell or changes the primary / secondary cell (PS cell) of the UE 100.

[0091] (5) Modification Example A modification example of the operation according to the above embodiment will be described. In this modification example, the UE 100, in response to predicting that a measurement report event will occur in the future through event prediction, transmits a message including notification information indicating the result of the event prediction to the gNB 200. The message may be an RRC message different from the measurement report, for example, a UE Assistance Information message or a new message for AI / ML. Alternatively, the message may be a new MAC control element (MAC CE) for AI / ML. The gNB 200 may perform reporting configuration (measurement configuration) including measurement report event configuration for the UE 100 based on the notification information.

[0092] 11 is a diagram for explaining the operation according to this modified example. Here, the overlapping explanation of the operation that overlaps with the operation according to the above-described embodiment will be omitted.

[0093] The operations in steps S201 to S205 are the same as those in the above-described embodiment (see FIG. 10).

[0094] When it is predicted that a measurement report event will occur (step S205: YES), in step S206, the UE 100 transmits a message including notification information indicating the result of the event prediction in step S204 to the gNB 200. The gNB 200 receives the notification information (message).

[0095] The notification information (message) of step S206 may include a measurement ID associated with the measurement report event predicted to occur in the future, and / or information indicating the event type of the measurement report event predicted to occur in the future (e.g., Event A1, Event A2, or Event A3).

[0096] The notification information (message) of step S206 may include a cell ID of a target cell of a measurement report event predicted to occur in the future. That is, the notification information may include a cell ID for identifying which cell the measurement report event is targeted for. For example, when a measurement report event of Event A1 (Serving becomes better than threshold) or Event A2 (Serving becomes worse than threshold) is predicted to occur, the UE 100 may include the cell ID of the current serving cell in the notification information. When it is predicted that a measurement report event of Event A3 (Neighbor becomes amount of offset better than PCell / PSCell) will occur, the UE 100 may include the cell ID of the neighboring cell (and the cell ID of PCell / PSCell) in the notification information.

[0097] The notification information (message) in step S206 may include information indicating the prediction accuracy of the event prediction, which may be a value of the likelihood (probability) that the measurement report event will occur.

[0098] The notification information (message) in step S206 may include information indicating the timing at which the measurement report event is predicted to occur in the future, which may indicate how far in the future the measurement report event will occur (e.g., 50 ms or 100 ms).

[0099] The notification information (message) of step S206 may include the model ID of the AI / ML model 101 used for the event prediction. The gNB 200 may determine the prediction accuracy of the UE 100 based on the model ID and a database containing performance information of each AI / ML model 101.

[0100] In step S207, the gNB 200, which has received notification information (message) from the UE 100, performs mobility control for the UE 100 based on the received notification information. For example, the gNB 200 may transmit to the UE 100 a measurement configuration (reporting configuration) that configures the UE 100 with a measurement report event that is predicted to occur. Alternatively, the gNB 200 may hand over the UE 100 to an appropriate cell or change the primary / secondary cell (PS cell) of the UE 100.

[0101] At least one of the pieces of information included in the notification information (message) of step S206 may be included in the measurement report according to the above-described embodiment. That is, the message format of the measurement report may be extended or a new measurement report message may be defined, and at least one of the pieces of information included in the notification information (message) of step S206 may be included in the message.

[0102] (6) Other Embodiments In the above-described embodiments, handover has been described as an example of mobility control. However, the present invention is not limited to handover and can be applied to any mobility control. For example, the operation according to the above-described embodiment may be applied to setting a handover execution condition in a conditional handover. Alternatively, the operation according to the above-described embodiment may be applied to LTM (L1 / L2 Triggered Mobility), which is cell switching initiated by Layer 1 and / or Layer 2 (L1 / L2). In this case, the above-described measurement report may be read as an L1 measurement report. Alternatively, the present invention may be applied to PSCell change, which switches the primary / secondary cell (PSCell) of the UE 100 initiated by the RRC layer. Furthermore, the present invention is not limited to mobility control in the RRC connected state, and may be applied to mobility control (e.g., cell reselection) in the RRC idle state or the RRC inactive state.

[0103] In the above embodiment, an example of predicting the occurrence of a measurement report event by event prediction has been described, but in the case of conditional handover, the UE 100 may predict the occurrence of a handover execution condition (handover execution event) set from the gNB 200. Also, in the case of conditional LTM, the UE 100 may predict the occurrence of a cell switching execution condition (cell switching event) set from the gNB 200.

[0104] In the above-described embodiment, an example has been described in which the signaling related to the AI / ML technology is an RRC message, which is signaling of the RRC layer (i.e., Layer 3). However, the AI / ML-related signaling may be a MAC CE, which is signaling of the MAC layer (i.e., Layer 2). The signaling may be downlink control information (DCI) and / or uplink control information (UCI), which are signaling of the PHY layer (i.e., L1). The downlink AI / ML-related signaling may be UE-dedicated signaling. The signaling may be broadcast signaling (e.g., SIB (System Information Block)). The AI / ML-related signaling may be signaling in a new layer (e.g., the AI / ML layer) dedicated to artificial intelligence or machine learning.

[0105] 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.

[0106] In the above-described embodiments and examples, 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.

[0107] 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.

[0108] A program that causes a computer to execute each process performed by the UE 100 or the gNB 200 may be provided. The program may be recorded on a computer-readable medium. Using a computer-readable medium, the program can be installed on a computer. Here, the computer-readable medium on which the program is recorded may be a non-transitory recording medium. The non-transitory recording medium is not particularly limited, and may be, for example, a recording medium such as a CD-ROM and / or a DVD-ROM. Furthermore, circuits that execute each process performed by the UE 100 or the gNB 200 may be integrated, and at least a portion of the UE 100 or the gNB 200 may be configured as a semiconductor integrated circuit (chip set, SoC: System on a chip).

[0109] The functions performed by the UE 100 or the gNB 200 may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), a CPU (a Central Processing Unit), conventional circuits, and / or combinations thereof, programmed to perform the described functions. A processor includes transistors and / or 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 memory. In this specification, circuitry, unit, or means refers to hardware that is programmed to perform the described functions or hardware that executes them. 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.

[0110] 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.

[0111] The above describes the embodiments in detail with reference to the drawings, but the specific configuration is not limited to that described above, and various design changes can be made within the scope that does not deviate from the gist of the invention.

[0112] This application claims priority from Japanese Patent Application No. 2024-060867 (filed April 4, 2024), the entire contents of which are incorporated herein by reference.

[0113] (7) Supplementary Notes The following are additional notes regarding the features of the above-described embodiment.

[0114] Supplementary Note 1: A communication method executed by a user equipment in a mobile communication system, the communication method comprising: in a Radio Resource Control (RRC) Connected state in which the user equipment is connected to a network node, performing event prediction using an artificial intelligence or machine learning (AI / ML) model to predict whether a measurement report event that will trigger transmission of a measurement report to the network node will occur in the future; and in response to the user equipment predicting that the measurement report event will occur in the future, transmitting a message regarding a result of the event prediction to the network node.

[0115] Supplementary Note 2: The communication method according to Supplementary Note 1, wherein the message is the measurement report including a measurement ID associated with the measurement report event predicted to occur in the future.

[0116] Supplementary Note 3: The communication method according to Supplementary Note 1 or 2, wherein the user equipment triggers transmission of the measurement report before the measurement report event occurs in response to predicting that the measurement report event will occur in the future.

[0117] Supplementary Note 4: The communication method according to Supplementary Note 1 or 2, wherein the user equipment, in response to predicting that the measurement report event will occur in the future, considers the measurement report event to have occurred and triggers transmission of the measurement report.

[0118] Supplementary Note 5: The communication method according to any one of Supplementary Notes 1 to 4, wherein the user equipment, in response to predicting that the measurement report event will occur in the future, transmits the message to the network node, the message including notification information indicating a result of the event prediction.

[0119] Supplementary Note 6: The communication method according to any one of Supplementary Notes 1 to 5, wherein the message includes information indicating a measurement ID associated with the measurement report event predicted to occur in the future and / or an event type of the measurement report event predicted to occur in the future.

[0120] Supplementary Note 7: The communication method according to any one of Supplementary Notes 1 to 6, wherein the message includes a cell ID of a target cell for the measurement report event predicted to occur in the future.

[0121] Supplementary Note 8: The communication method according to any one of Supplementary Notes 1 to 7, wherein the message includes information indicating prediction accuracy of the event prediction.

[0122] Supplementary Note 9: The communication method according to any one of Supplementary Notes 1 to 8, wherein the message includes information indicating a timing at which the measurement report event is predicted to occur in the future.

[0123] Supplementary Note 10: The communication method according to any one of Supplementary Notes 1 to 9, wherein the message includes a model ID of the AI / ML model used for the event prediction.

[0124] Supplementary Note 11: The communication method according to any one of Supplementary Notes 1 to 10, further comprising receiving configuration information for configuring the event prediction from the network node, wherein the user equipment performs the event prediction based on the configuration information.

[0125] Supplementary Note 12: The communication method according to Supplementary Note 11, wherein the configuration information includes a setting specifying a future time range for predicting occurrence of the measurement report event in the event prediction.

[0126] Supplementary Note 13: The communication method according to Supplementary Note 11 or 12, wherein the user equipment obtains a value indicating a probability that the measurement report event will occur in the future using the AI / ML model, and the configuration information includes a setting specifying a threshold value of the probability that must be met in order for the event prediction to consider the measurement report event to occur in the future.

[0127] Supplementary Note 14: The communication method according to any one of Supplementary Notes 11 to 13, wherein the configuration information includes a setting for specifying a measurement report event type to which the event prediction is to be applied.

[0128] Supplementary Note 15: The communication method according to any one of Supplementary Notes 11 to 14, wherein the setting information includes a setting for specifying a model ID of the AI / ML model used for the event prediction.

[0129] Supplementary Note 16: A user equipment for use in a mobile communication system, comprising: a controller configured to perform event prediction using an artificial intelligence or machine learning (AI / ML) model to predict whether a measurement report event that will trigger transmission of a measurement report to the network node will occur in the future, in a Radio Resource Control (RRC) Connected state in which the user equipment is connected to a network node; and a transmitter configured to transmit a message regarding a result of the event prediction to the network node in response to predicting that the measurement report event will occur in the future.

[0130] Supplementary Note 17: A network node used in a mobile communication system, comprising: a receiving unit that receives, from a user equipment in a Radio Resource Control (RRC) Connected state connected to said network node, a message relating to a result of an event prediction using an artificial intelligence or machine learning (AI / ML) model; and a control unit that performs mobility control for said user equipment based on said message, wherein said event prediction is a process of predicting whether a measurement report event that triggers transmission of a measurement report to said network node will occur in the future, and said message indicates that said measurement report event is predicted to occur in the future.

[0131] Supplementary Note 18. The network node of Supplementary Note 17, further comprising a transmitter configured to transmit configuration information for configuring said event prediction to said user equipment.

[0132] 1: Mobile communication system 5: Network 10: RAN (NG-RAN) 11: L1 filter 12: Beam integration / selection unit 13: L3 filter 14: Evaluation unit 15: L3 beam filter 16: Beam selection unit 20: CN (5GC) 100: UE 101: AI / ML model 110: Receiving unit 111: Receiver (RF chain) 120: Transmitting unit 130: Control unit 140: Wireless communication unit 200: gNB 210: Transmitting unit 220: Receiving unit 230: Control unit 240: Network communication unit 241: Transmitting unit 242: Receiving unit 250: Wireless communication unit A1: Data collection unit A2: Model learning unit A3: Model inference unit A4 : Data processing section

Claims

1. A communication method executed by a user equipment in a mobile communication system, the communication method comprising: in a radio resource control (RRC) connected state in which the user equipment is connected to a network node, performing event prediction using an artificial intelligence or machine learning (AI / ML) model to predict whether a measurement report event that will trigger the transmission of a measurement report to the network node will occur in the future; and in response to the user equipment predicting that the measurement report event will occur in the future, transmitting a message regarding the result of the event prediction to the network node.

2. The communication method according to claim 1, wherein the message is the measurement report including a measurement ID associated with the measurement report event predicted to occur in the future.

3. The communication method according to claim 1, wherein the user equipment triggers transmission of the measurement report before the measurement report event occurs in response to predicting that the measurement report event will occur in the future.

4. The communication method according to claim 1, wherein the user equipment triggers transmission of the measurement report by regarding the measurement report event as having occurred in response to predicting that the measurement report event will occur in the future.

5. The communication method according to claim 1, wherein the user equipment, in response to predicting that the measurement report event will occur in the future, transmits the message to the network node, the message including notification information indicating a result of the event prediction.

6. A communication method according to any one of claims 1 to 5, wherein the message includes a measurement ID associated with the measurement report event predicted to occur in the future, and / or information indicating the event type of the measurement report event predicted to occur in the future.

7. A communication method according to any one of claims 1 to 5, wherein the message includes a cell ID of a target cell for the measurement report event predicted to occur in the future.

8. The communication method according to any one of claims 1 to 5, wherein the message includes information indicating the prediction accuracy of the event prediction.

9. A communication method according to any one of claims 1 to 5, wherein the message includes information indicating a timing at which the measurement report event is predicted to occur in the future.

10. The communication method according to any one of claims 1 to 5, wherein the message includes a model ID of the AI / ML model used for the event prediction.

11. A communication method according to any one of claims 1 to 5, further comprising receiving configuration information for configuring the event prediction from the network node, wherein the user equipment performs the event prediction based on the configuration information.

12. The communication method according to claim 11, wherein the setting information includes a setting that specifies a future time range for which the event prediction is to predict the occurrence of the measurement report event.

13. The communication method of claim 11, wherein the user equipment obtains a value indicating the probability that the measurement report event will occur in the future using the AI / ML model, and the configuration information includes a setting that specifies a threshold value of the probability that must be met in order for the event prediction to consider the measurement report event to occur in the future.

14. The communication method according to claim 11, wherein the configuration information includes a setting that specifies a measurement report event type to which the event prediction is to be applied.

15. The communication method according to claim 11, wherein the setting information includes a setting that specifies a model ID of the AI / ML model used for the event prediction.

16. A user equipment for use in a mobile communication system, comprising: a control unit that, in a Radio Resource Control (RRC) Connected state in which the user equipment is connected to a network node, performs event prediction using an artificial intelligence or machine learning (AI / ML) model to predict whether a measurement report event that will trigger transmission of a measurement report to the network node will occur in the future; and a transmission unit that, in response to predicting that the measurement report event will occur in the future, transmits a message regarding the result of the event prediction to the network node.

17. A network node used in a mobile communication system, comprising: a receiving unit that receives a message relating to the result of an event prediction using an artificial intelligence or machine learning (AI / ML) model from a user equipment in a radio resource control (RRC) connected state connected to the network node; and a control unit that performs mobility control for the user equipment based on the message, wherein the event prediction is a process of predicting whether a measurement report event that will trigger the transmission of a measurement report to the network node will occur in the future, and the message indicates that the measurement report event is predicted to occur in the future.

18. The network node according to claim 17, further comprising a transmitter that transmits configuration information for configuring the event prediction to the user equipment.

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