Communication method, user device, and network node

Abstract

This communication method for use in a mobile communication system includes: receiving, from a network node having an artificial intelligence or a machine learning (AI / ML) model for use in mobility control and by a user device that is in a radio resource control (RRC)-connected state and is connected to the network node, settings for measuring reception quality regarding a plurality of measurement objects; and transmitting, to the network node and by the user device, a plurality of measurement results obtained by measuring the plurality of measurement objects in accordance with the settings. The settings include a first setting for setting measurement timings for the plurality of measurement objects and / or a second setting for setting a receiver for use in measuring the plurality of measurement objects.

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] BACKGROUND ART 3GPP (Third Generation Partnership Project) (registered trademark; the same applies hereinafter), a standardization project for mobile communication systems, is studying the application of artificial intelligence or machine learning (also referred to as "AI: Artificial Intelligence / ML: Machine Learning") 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 for use in a mobile communication system, comprising: a user equipment (UE) in a Radio Resource Control (RRC) connected state connected to a network node having an artificial intelligence or machine learning (AI / ML) model used for mobility control, receiving from the network node a configuration for measuring reception quality of a plurality of measurement objects, and transmitting to the network node a plurality of measurement results obtained by performing the measurements of the plurality of measurement objects in accordance with the configuration, the configuration including at least one of a first configuration for setting measurement timings for each of the plurality of measurement objects and a second configuration for setting a receiver to be used for each measurement of the plurality of measurement objects.

[0005] A user equipment according to a second aspect is a user equipment for use in a mobile communication system, the user equipment being in a Radio Resource Control (RRC) connected state connected to a network node having an artificial intelligence or machine learning (AI / ML) model used for mobility control, the user equipment comprising: a receiver configured to receive from the network node configurations for measuring reception quality of a plurality of measurement objects, and a transmitter configured to transmit to the network node a plurality of measurement results obtained by performing the measurements of the plurality of measurement objects in accordance with the configurations, the configurations including at least one of a first configuration for setting measurement timings for each of the plurality of measurement objects, and a second configuration for setting a receiver to be used for measuring each of the plurality of measurement objects.

[0006] A network node according to a third aspect is a network node used in a mobile communication system, the network node including: a transmitter that transmits, to a user equipment in a radio resource control (RRC) connected state connected to the network node, configurations for measuring reception quality of a plurality of measurement objects, a receiver that receives from the user equipment a plurality of measurement results obtained by performing the measurements of the plurality of measurement objects in accordance with the configurations, and a controller that performs mobility control of the user equipment using an artificial intelligence or machine learning (AI / ML) model based on the plurality of measurement results and the configurations, the configurations including at least one of a first configuration for setting measurement timings for the plurality of measurement objects and a second configuration for setting a receiver to be used for measuring each of the plurality of measurement objects.

[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 a specific example of the operation of a first operation pattern according to an embodiment. FIG. 10 is a diagram showing a specific example of the operation of a second operation pattern according to an embodiment.

[0008] One possible use case of the AI / ML technology is mobility control of user equipment. For example, it is possible to apply the AI / ML technology to control the 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 mobile communication systems.

[0009] The present disclosure utilizes AI / ML techniques 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 is a mobile phone terminal (which may be a smartphone), 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 called an uplink (UL), and a link in the transmission direction from the network 5 to the UE 100 is called 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 the UE 100 and the PHY layer of the gNB 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 gNB 200. Specifically, the UE 100 performs blind decoding of the PDCCH using a radio network temporary identifier (RNTI) and acquires the 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). The measurement object may 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] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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

[0046] 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.

[0047] 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.

[0048] Note that if the filter coefficient k is set to 0 (zero), no L3 filtering is applied.

[0049] 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.

[0050] 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.

[0051] 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 the UE 100 to the gNB 200.

[0052] (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).

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] The data processing unit A4 receives the inference result data and performs processing that utilizes the inference result data.

[0058] 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.

[0059] (4) Example of Mobility Control Using Network-Side Model In the embodiment, the AI / ML technology is applied to mobility control of the UE 100. Specifically, when the UE 100 is in an RRC-connected state, the AI / ML technology is applied to handover that switches the serving cell (primary cell) of the UE 100 under the initiative of the RRC layer.

[0060] The gNB 200 that performs handover control has an AI / ML model. The AI / ML model according to the embodiment is a model that uses a measurement report from the UE 100 as inference data (input) to infer measurement results of measurement targets not included in the measurement report. The measurement target may be synonymous with the measurement object. The measurement target may be a measurement target determined according to the measurement object. The measurement target may be a cell. The measurement target may be a frequency. The measurement target may be a beam. The measurement target may be a reference signal. The reference signal (RS) may be SSB (synchronization signal (SS) / physical broadcast channel (PBCH)), channel state information (CSI)-RS, demodulation (DM)-RS, or the like. In the embodiment, an example in which the measurement target is a cell (or frequency) will be mainly described.

[0061] FIG. 8 is a diagram showing an example of an operation scenario of the mobile communication system 1 according to the embodiment.

[0062] In the illustrated example, the UE 100 is in an RRC connected state with the cell of the gNB 200 as the serving cell. There are multiple neighboring cells 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 the cells may be the same. The frequencies of the cells may be different.

[0063] 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, a reporting configuration, and a measurement ID.

[0064] In step S2 (measurement), the UE 100 performs measurement on a measurement target determined according to the measurement object. For example, the UE 100 measures the reception quality of a cell belonging to each frequency specified in the measurement object.

[0065] In step S3 (measurement report), the UE 100 transmits a measurement report, which is an RRC message including measurement results for each cell, to the gNB 200 at a timing determined in the reporting configuration corresponding to the measurement object. The gNB 200 receives the measurement report. The measurement result may be a set of an ID of the measurement object and its measurement value. The measurement value may be reference signal received power (RSRP), reference signal radio quality (RSRQ), signal-to-interference-and-noise ratio (SINR), bit error rate (BER), block error rate (BLER), or the like.

[0066] In step S4 (model inference), the gNB200 uses the AI / ML model 201 to infer the measurement results of cells not included in the measurement report from the measurement results of each cell included in the measurement report. The gNB200 performs mobility control for the UE100 based on the measurement results of each cell included in the measurement report and the measurement results of the inferred cell. For example, the gNB200 determines a target cell for handover and performs control to switch the serving cell from the current serving cell (source cell) to the target cell.

[0067] In the illustrated example, gNB200 (AI / ML model 201) infers the measurement results of cell x, which is not included in the measurement report, by model inference from the measurement results of cells a, b, ... included in the measurement report. Prior to such model inference, gNB200 (AI / ML model 201) is assumed to have learned the correlation between the measurement results of each cell included in the measurement report from each UE100 by model learning.

[0068] Such AI / ML processing (model learning and model inference) is premised on the assumption that there is a correlation between the measurement results of each cell at a certain point (the position of UE 100). Here, since the radio environment may change from moment to moment due to the movement of UE 100, etc., the measurement results of multiple cells at a certain timing (or timing close in time) may become important. This is because it is considered that the correlation between the measurement results of multiple cells at significantly different measurement timings is low due to fluctuations in the radio environment.

[0069] However, especially in the case of inter-frequency measurement, it is difficult to synchronize the measurement timing of each frequency (each cell). For example, the UE 100 tunes its own receiver 111 or switches its own receiver 111 to measure different frequencies sequentially, making it difficult to simultaneously measure multiple frequencies (each cell).

[0070] In addition, a UE 100 having multiple receivers 111 may be able to measure multiple frequencies simultaneously using the multiple receivers 111, but the correlation between multiple measurement results measured using different receivers 111 may be low.

[0071] In the following embodiments, a first operation pattern and a second operation pattern that can improve inference accuracy when performing model inference of measurement results at, for example, a cell level (cell unit) using a network-side model will be described.

[0072] In the first operation pattern, in order to accurately infer the measurement results using the network side model, UE100 reports information on the measurement timing of each measurement result and / or information on the receiver 111 used for each measurement to gNB200. In contrast, in the second operation pattern, in order to accurately infer the measurement results using the network side model, gNB200 sets (specifies) to UE100 the measurement timing of each measurement target and / or the receiver 111 used for each measurement.

[0073] (4.1) First Operation Pattern First, an overview of the first operation pattern according to the embodiment will be described with reference to FIG.

[0074] In the first operation pattern, in the measurement of step S2, UE100 in an RRC connected state connected to gNB200 measures the reception quality for multiple measurement targets (e.g., multiple cells belonging to one or multiple frequencies).

[0075] In the measurement report of step S3, UE 100 transmits the multiple measurement results obtained by the measurement of step S2 and auxiliary information used for AI / ML processing on the network side to gNB 200. The auxiliary information includes at least one of first auxiliary information indicating the measurement timing of each of the multiple measurement results and second auxiliary information indicating the receiver 111 used for each of the multiple measurement results.

[0076] In this way, in the first operation pattern, at least one of first auxiliary information indicating the measurement timing of each of the multiple measurement results and second auxiliary information indicating the receiver 111 used for each of the multiple measurement results is transmitted from the UE 100 to the gNB 200. This allows the gNB 200 to determine the degree of correlation for each measurement result, for example, taking into account these auxiliary information, thereby making it possible to appropriately perform AI / ML processing (model learning and / or model inference).

[0077] The UE 100 that performs such operations has a control unit 130 that measures the reception quality of multiple measurement targets in the RRC connected state, and a transmission unit 120 that transmits multiple measurement results obtained by the measurements and auxiliary information used for AI / ML processing on the network side to the gNB 200 (see Figure 2).

[0078] The gNB 200, which has received multiple measurement results and auxiliary information from the UE 100, performs mobility control of the UE 100 using the AI / ML model 201 based on the multiple measurement results and auxiliary information. The gNB 200 that performs such operation has a receiver 220 that receives multiple measurement results obtained by measuring the reception quality of multiple measurement targets from the UE 100 in an RRC connected state connected to the gNB 200, and auxiliary information used for AI / ML processing on the network side, and a control unit 230 that performs mobility control of the UE 100 using the AI / ML model 201 based on the multiple measurement results and auxiliary information (see FIG. 3).

[0079] For example, the control unit 230 of gNB200 uses AI / ML model 201 to infer the measurement results of a measurement object different from the measurement object corresponding to the multiple measurement results from multiple measurement results based on auxiliary information.

[0080] In the measurement report of step S3, UE100 may send an RRC message (also referred to as an "L3 measurement report message") including multiple measurement results and auxiliary information to gNB200.

[0081] In the measurement configuration of step S1, the UE 100 may receive configuration information that configures the transmission of the first auxiliary information from the gNB 200. The UE 100 may transmit auxiliary information including the first auxiliary information to the gNB 200 based on the configuration information.

[0082] The first auxiliary information may be information indicating the measurement timing of each of the multiple measurement results in absolute time. Alternatively, the first auxiliary information may be information indicating the measurement timing of each of the multiple measurement results in relative time with respect to a reference timing. Here, the reference timing may be the timing of the measurement setting in step S1 or the timing of the measurement report in step S3.

[0083] In the measurement configuration of step S1, the UE 100 may receive configuration information that configures the transmission of the second auxiliary information from the gNB 200. The UE 100 may transmit auxiliary information including the second auxiliary information to the gNB 200 based on the configuration information.

[0084] Next, a specific example of the operation of the first operation pattern according to the embodiment will be described with reference to FIG.

[0085] In step S101, UE100 is in an RRC connected state with the cell of gNB200 as the serving cell.

[0086] In step S102, the gNB 200 transmits an RRC message (e.g., an RRC Reconfiguration message) including a measurement configuration to the UE 100. The UE 100 receives the RRC message. The measurement configuration includes at least one of a measurement ID, a measurement object and its ID, and a reporting configuration and its ID.

[0087] The measurement configuration in step S102 may include configuration information for reporting measurement timing and / or for reporting the measurement RF chain (i.e., the receiver 111 used for the measurement). Such configuration may be performed for each measurement ID, each measurement object ID, or each reporting configuration ID.

[0088] In step S103, the UE 100 performs measurements of measurement targets (in the embodiment, a plurality of cells of one or a plurality of frequencies) determined according to the measurement object. The UE 100 also identifies the timing (measurement timing) at which each measurement was performed and / or the RF chain used for each measurement. The measurement timing may be any of the timings before passing through the L1 filter, after passing through the L1 filter, before passing through the L3 filter, and after passing through the L3 filter (see FIG. 6 ).

[0089] In step S104, UE100 transmits a message including each measurement result of step S103 and auxiliary information (first auxiliary information and / or second auxiliary information) to gNB200. gNB200 receives the message. In an embodiment, the message is an L3 measurement report message. Alternatively, UE100 may transmit each measurement result of step S103 in an L3 measurement report message and transmit the auxiliary information in another RRC message (e.g., a UE auxiliary information message).

[0090] The first auxiliary information may include a cell ID (and / or a frequency ID) of a measurement cell for each measurement timing. The first auxiliary information may include measurement timing for each measurement cell (and / or frequency).

[0091] The measurement timing may be expressed as absolute time. For example, the measurement timing may be a timestamp indicating the date and time. The measurement timing may also be an ID (number) of the time unit at which the measurement was performed. The time unit ID may be at least one of a hyper system frame number (H-SFN), a system frame number (SFN), a subframe number, a slot number, and a symbol number.

[0092] The measurement timing may be expressed as a relative time that is a difference from a certain reference timing. The reference timing may be, for example, an SFN number, and may be reported to the gNB 200. Alternatively, the reference timing may not be reported to the gNB 200. For example, the measurement timing of relative time zero may be used as the reference timing, or the reference timing may be specified by the gNB 200 to the UE 100.

[0093] The second auxiliary information may include a cell ID (and / or a frequency ID) of a measurement cell for each RF chain. The second auxiliary information may include an RF chain ID for each measurement cell (and / or frequency). The RF chain ID may be an ID assigned to each RF chain by the UE 100.

[0094] In step S105, gNB200 uses each measurement result and auxiliary information in the measurement report of step S104 to estimate the measurement results of cells (and / or frequencies) not reported in the measurement report by model inference.

[0095] For example, the gNB200 may perform model inference by performing a weighting process based on the first auxiliary information so as to prioritize measurement results that are closer to the current time than measurement results that are further away from the current time. The gNB200 may extract each measurement result of a cell (and / or frequency) that can be considered to have the same measurement timing as input data (data for inference) and perform model inference. The gNB200 may rearrange the measurement results in chronological order as input data (data for inference) and perform model inference. Here, the gNB200 may derive the measurement results of a timing at which measurement has not been performed by approximation from time series data (data before and after).

[0096] The gNB200 may perform model inference by performing a weighting process based on the second auxiliary information so as to give more importance to measurement results measured using the same RF chain than measurement results measured using different RF chains. The gNB200 may extract each measurement result measured using the same RF chain as input data (inference data) and perform model inference.

[0097] In step S106, gNB200 performs mobility control (e.g., handover control) of UE100 based on each measurement result received from UE100 and measurement results inferred by itself.

[0098] In this operation example, an example has been described in which gNB200 uses each measurement result and auxiliary information for model inference, but gNB200 may also use each measurement result and auxiliary information for model learning.

[0099] (4.2) Second Movement Pattern The second movement pattern according to the embodiment will be described, focusing on the differences from the first movement pattern. At least some of the movements of the second movement pattern may be combined with at least some of the movements of the first movement pattern.

[0100] First, an overview of the second operation pattern according to the embodiment will be described with reference to FIG.

[0101] In the second operation pattern, in the measurement setting of step S1, the UE 100 receives a setting for measuring the reception quality for a plurality of measurement targets (for example, a plurality of cells belonging to one or a plurality of frequencies) from the gNB 200. The setting includes at least one of a first setting for setting the measurement timing for each of the plurality of measurement targets and a second setting for setting the receiver 111 to be used for each measurement of the plurality of measurement targets.

[0102] In the measurement of step S2, the UE 100 performs measurement of a plurality of measurement targets according to the setting of step S1.

[0103] In the measurement report of step S3, UE100 transmits to gNB200 multiple measurement results obtained by performing the measurement of step S2.

[0104] Thus, in the second operation pattern, at least one of a first setting for setting the measurement timing of each of the multiple measurement targets and a second setting for setting the receiver 111 to be used for each measurement of the multiple measurement targets is transmitted from the gNB 200 to the UE 100. This allows the gNB 200 to perform settings that increase the correlation between each measurement result used for AI / ML processing (model learning and / or model inference), for example, thereby enabling the AI / ML processing to be performed appropriately.

[0105] UE100 performing such operation has a receiving unit 110 that receives settings from gNB200 in an RRC connected state for measuring the reception quality of multiple measurement targets, and a transmitting unit 120 that transmits multiple measurement results obtained by measuring multiple measurement targets in accordance with the settings to gNB200 (see Figure 2).

[0106] The gNB 200, which receives multiple measurement results from the UE 100, performs mobility control of the UE 100 using the AI / ML model 201 based on the multiple measurement results and settings. The gNB 200 that performs such operation has a transmitter 210 that transmits settings for measuring reception quality for multiple measurement targets to the UE 100 in an RRC connected state connected to the gNB 200, a receiver 220 that receives from the UE 100 multiple measurement results obtained by measuring the multiple measurement targets according to the settings, and a control unit 230 that performs mobility control of the UE 100 using the AI / ML model 201 based on the multiple measurement results and settings (see FIG. 3).

[0107] For example, based on the configuration, gNB200 uses AI / ML model 201 to infer from multiple measurement results the measurement results of a measurement object that is different from the measurement object corresponding to the multiple measurement results.

[0108] In the measurement configuration of step S1, UE100 may receive an RRC message (e.g., an RRC Reconfiguration message) including the configuration from gNB200.

[0109] The first setting may include information for setting two or more measurement objects to be measured within one time range and information for setting one time range. The first setting may include information for setting two or more measurement objects to be continuously measured within one time range. The time range may be a measurement gap set by the gNB200. The measurement gap is a period during which the UE100 suspends communication between the gNB200 and the UE100 to perform measurements (particularly, measurements of a frequency other than that of the serving cell). The time range may be determined by an upper limit of the time (elapsed time) from the measurement of the first measurement object to the measurement of the last measurement object among multiple measurement objects set by the gNB200. The time range may be determined by a time unit ID (e.g., a system frame number (SFN), a subframe number, or a slot number).

[0110] The second setting may include information for setting two or more measurement targets to be measured using the same receiver 111 (same RF chain).

[0111] Next, a specific example of the operation of the second operation pattern according to the embodiment will be described with reference to FIG.

[0112] In step S201, UE100 is in an RRC connected state with the cell of gNB200 as the serving cell.

[0113] In step S202, the gNB 200 transmits an RRC message (e.g., an RRC Reconfiguration message) including a measurement configuration to the UE 100. The UE 100 receives the RRC message. The measurement configuration includes at least one of a measurement ID, a measurement object and its ID, and a reporting configuration and its ID.

[0114] In the second operation pattern, the measurement configuration in step S202 may include a first configuration specifying each measurement timing of multiple measurement objects (in the embodiment, multiple cells belonging to one or multiple frequencies). The first configuration may be a combination of a time range considered to be the same timing and measurement objects to be measured in that time range. The first configuration may be a combination of measurement objects to be measured consecutively in a certain time range. The first configuration may be information specifying a measurement object to be measured at the closest (last) measurement timing relative to the measurement report timing.

[0115] Here, the measurement object may be expressed (specified) by at least one of a cell ID, a frequency ID (e.g., AFRFCN), a measurement object ID, and a measurement ID. The time range may be a measurement gap. The time range may be an upper limit of the elapsed time between the measurement of the first measurement object and the measurement of the last measurement object. The unit of the elapsed time may be seconds, SFN, etc.

[0116] The measurement configuration in step S202 may include a second configuration specifying an RF chain to be used for each measurement of the plurality of measurement objects. The second configuration may specify a set of measurement objects (a plurality of measurement objects) to be measured on the same RF chain. Here, the measurement object may be expressed (specified) by at least one of a cell ID, a frequency ID (e.g., AFRFCN), a measurement object ID, and a measurement ID.

[0117] In step S203, the UE 100 performs measurement of each measurement target in accordance with the measurement configuration in step S202.

[0118] In step S204, the UE 100 transmits a message including each measurement result of step S203 to the gNB 200. The gNB 200 receives the message. In an embodiment, the message is an L3 measurement report message.

[0119] In step S205, gNB200 estimates the measurement results of cells (and / or frequencies) not reported in the measurement report by model inference based on the settings in step S202 and each measurement result in the measurement report in step S204.

[0120] For example, the gNB200 may perform model inference by performing a weighting process based on the first setting so that measurement results closer in time to the current time are given more importance than measurement results further in time from the current time. The gNB200 may extract each measurement result of a cell (and / or frequency) that can be considered to have the same measurement timing as input data (data for inference) and perform model inference. The gNB200 may rearrange the measurement results in chronological order as input data (data for inference) and perform model inference. Here, the gNB200 may derive the measurement results of a timing at which measurement has not been performed by approximation from time series data (data before and after).

[0121] The gNB200 may perform model inference by performing a weighting process based on the second setting so that each measurement result measured using the same RF chain is given more importance than a measurement result measured using a different RF chain. The gNB200 may extract each measurement result measured using the same RF chain as input data (inference data) and perform model inference.

[0122] In step S206, gNB200 performs mobility control (e.g., handover control) of UE100 based on each measurement result received from UE100 and measurement results inferred by itself.

[0123] In this operation example, an example has been described in which gNB200 uses each measurement result and auxiliary information for model inference, but gNB200 may also use each measurement result and auxiliary information for model learning.

[0124] (5) Other Embodiments In the above embodiment, an example in which the measurement target is a cell and / or a frequency has been described, but the measurement target may be a beam or a reference signal. The gNB 200 may infer the measurement results of beams (or reference signals) not included in the measurement report from the measurement results of each beam (or reference signal) included in the measurement report.

[0125] In the above-described embodiment, handover has been described as an example of mobility control, but 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.

[0126] 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 Medium Access Control Element (CE), which is signaling of the MAC layer (i.e., Layer 2). The AI / ML-related 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 downlink AI / ML-related 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., AI / ML layer) dedicated to artificial intelligence or machine learning.

[0127] The above-described operational flows are not limited to being implemented independently, but can also be implemented by combining two or more operational flows. For example, some steps of one operational flow may be added to another operational flow. 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.

[0128] 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.

[0129] 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.

[0130] A program may be provided that causes a computer to execute each process performed by the UE 100 or the gNB 200. 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 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).

[0131] 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 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.

[0132] 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.

[0133] 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.

[0134] This application claims priority from Japanese Patent Application No. 2024-060463 (filed April 3, 2024), the entire contents of which are incorporated herein by reference.

[0135] (6) Supplementary Notes The following are additional notes regarding the features of the above-described embodiment.

[0136] Supplementary Note 1: A communication method used in a mobile communication system, comprising: a user equipment in a Radio Resource Control (RRC) connected state connected to a network node having an artificial intelligence or machine learning (AI / ML) model used for mobility control, receiving from the network node a configuration for measuring reception quality of a plurality of measurement objects; and transmitting to the network node a plurality of measurement results obtained by performing the measurements of the plurality of measurement objects in accordance with the configuration, wherein the configuration includes at least one of a first configuration for setting measurement timing for each of the plurality of measurement objects, and a second configuration for setting a receiver to be used for measuring each of the plurality of measurement objects.

[0137] Supplementary Note 2: The communication method according to Supplementary Note 1, further comprising the network node receiving the plurality of measurement results from the user equipment performing mobility control of the user equipment using an AI / ML model based on the plurality of measurement results and the configuration.

[0138] Supplementary Note 3: The communication method according to Supplementary Note 2, wherein the network node infers, from the plurality of measurement results based on the configuration, measurement results of a measurement object different from a measurement object corresponding to the plurality of measurement results using the AI / ML model.

[0139] Supplementary Note 4: The communication method according to any one of Supplementary Notes 1 to 3, wherein the user equipment receives an RRC message including the configuration from the network node.

[0140] Supplementary Note 5: The communication method according to any one of Supplementary Notes 1 to 4, wherein the first setting includes information for setting two or more measurement targets to be measured within one time range, and information for setting the one time range.

[0141] Supplementary Note 6: The communication method according to any one of Supplementary Notes 1 to 5, wherein the first setting includes information for setting two or more measurement targets to be measured consecutively within one time range.

[0142] Supplementary Note 7: The communication method according to any one of Supplementary Notes 1 to 6, wherein the second setting includes information for setting two or more measurement targets to be measured using the same receiver.

[0143] Supplementary Note 8: A user equipment for use in a mobile communication system, the user equipment comprising: a receiver that receives, in a Radio Resource Control (RRC) connected state connected to a network node having an artificial intelligence or machine learning (AI / ML) model used for mobility control, a configuration for measuring reception qualities of a plurality of measurement objects from the network node; and a transmitter that transmits, to the network node, a plurality of measurement results obtained by performing the measurements of the plurality of measurement objects in accordance with the configuration, the configuration including at least one of a first configuration for setting measurement timings for the plurality of measurement objects and a second configuration for setting a receiver to be used for measuring each of the plurality of measurement objects.

[0144] Supplementary Note 9: A network node used in a mobile communication system, comprising: a transmitter that transmits, to a user equipment in a radio resource control (RRC) connected state connected to the network node, a configuration for measuring reception qualities of a plurality of measurement objects; a receiver that receives from the user equipment a plurality of measurement results obtained by performing the measurements of the plurality of measurement objects in accordance with the configuration; and a controller that performs mobility control of the user equipment using an artificial intelligence or machine learning (AI / ML) model based on the plurality of measurement results and the configuration, wherein the configuration includes at least one of a first configuration for setting measurement timing for each of the plurality of measurement objects and a second configuration for setting a receiver to be used for measuring each of the plurality of measurement objects.

[0145] 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 110: Receiving unit 111: Receiver (RF chain) 120: Transmitting unit 130: Control unit 140: Wireless communication unit 200: gNB 201: AI / ML model 210: Transmitting unit 220: Receiving unit 230: Control unit 240: Network communication unit 241: Transmitting unit 242: Receiving unit 250: Wireless communication unit 300: AMF / UPF A1: Data collection unit A2: Model learning unit A3: Model inference unit A4: Data processing unit

Claims

1. A communications method used in a mobile communications system, comprising: a user equipment in a Radio Resource Control (RRC) connected state connected to a network node having an artificial intelligence or machine learning (AI / ML) model used for mobility control; receiving from the network node a configuration for measuring reception quality for a plurality of measurement objects; and transmitting to the network node a plurality of measurement results obtained by measuring the plurality of measurement objects in accordance with the configuration; wherein the configuration includes at least one of a first configuration for setting measurement timing for each of the plurality of measurement objects, and a second configuration for setting a receiver to be used for measuring each of the plurality of measurement objects.

2. The communication method according to claim 1, further comprising the step of: the network node receiving the plurality of measurement results from the user equipment performs mobility control of the user equipment using an AI / ML model based on the plurality of measurement results and the configuration.

3. The communication method according to claim 2, wherein the network node uses the AI / ML model to infer, from the plurality of measurement results, measurement results of a measurement object different from the measurement object corresponding to the plurality of measurement results, based on the setting.

4. A communication method according to any one of claims 1 to 3, wherein the user equipment receives an RRC message including the configuration from the network node.

5. A communication method according to any one of claims 1 to 3, wherein the first setting includes information for setting two or more measurement targets to be measured within one time range, and information for setting the one time range.

6. A communication method according to any one of claims 1 to 3, wherein the first setting includes information for setting two or more measurement targets to be measured consecutively within one time range.

7. A communication method according to any one of claims 1 to 3, wherein the second setting includes information for setting two or more measurement targets to be measured using the same receiver.

8. A user equipment used in a mobile communication system, in a Radio Resource Control (RRC) connected state connected to a network node having an artificial intelligence or machine learning (AI / ML) model used for mobility control, comprising: a receiver that receives from the network node a configuration for measuring reception quality for multiple measurement objects; and a transmitter that transmits to the network node multiple measurement results obtained by measuring the multiple measurement objects in accordance with the configuration, wherein the configuration includes at least one of a first configuration for setting the measurement timing for each of the multiple measurement objects and a second configuration for setting a receiver to be used for measuring each of the multiple measurement objects.

9. A network node used in a mobile communication system, comprising: a transmitter that transmits settings for measuring reception quality of multiple measurement objects to a user equipment in a radio resource control (RRC) connected state connected to the network node; a receiver that receives from the user equipment multiple measurement results obtained by performing the measurements of the multiple measurement objects in accordance with the settings; and a controller that performs mobility control of the user equipment using an artificial intelligence or machine learning (AI / ML) model based on the multiple measurement results and the settings, wherein the settings include at least one of a first setting for setting measurement timing for each of the multiple measurement objects and a second setting for setting a receiver to be used for measuring each of the multiple measurement objects.

Images