Communication method, user equipment, and network node

Abstract

A communication method executed by user equipment in a mobile communication system, the method comprising: receiving, from a network node, information for identifying a combination of a first measurement target for which the user equipment actually measures reception quality and a second measurement target for which the user equipment can infer a measurement result of reception quality on the basis of a measurement result for the first measurement target; and, after a first measurement result is obtained by measuring the first measurement target, inferring a second measurement result for the second measurement target on the basis of the first measurement result using an artificial intelligence or machine learning (AI / ML) model.

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 device in a mobile communication system, comprising: receiving information from a network node for identifying a combination of a first measurement object for which the user device actually measures reception quality and a second measurement object for which the user device can infer a measurement result of reception quality based on a measurement result of the first measurement object; and, after obtaining a first measurement result by measuring the first measurement object, inferring a second measurement result of the second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model.

[0005] A user device according to a second aspect is a user device used in a mobile communication system, and includes a receiving unit that receives, from a network node, information for identifying a combination of a first measurement object for which the user device actually measures reception quality and a second measurement object for which the user device can infer a measurement result of reception quality based on the measurement result of the first measurement object, and a control unit that, after obtaining a first measurement result by measuring the first measurement object, infers a second measurement result of the second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model.

[0006] A network node according to a third aspect is a network node used in a mobile communication system, and includes a transmitter that transmits to the user device information for identifying a combination of a first measurement object for which a user device actually measures reception quality and a second measurement object for which the user device can infer the measurement result of reception quality using an artificial intelligence or machine learning (AI / ML) model based on the measurement result of the first measurement object.

[0007] A fourth aspect of the present invention relates to a communication method executed by a user equipment in a mobile communication system, the method comprising: measuring a reception quality of a first measurement object to obtain a first measurement result; inferring a second measurement result of the reception quality of a second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model; and transmitting the first measured measurement result and the inferred second measurement result to a network node, wherein the user equipment transmits identification information to the network node to identify whether each of the transmitted measurement results is a measured value or an inferred value.

[0008] A user equipment according to a fifth aspect is a user equipment for use in a mobile communication system, the user equipment comprising: a control unit that measures a reception quality of a first measurement object to obtain a first measurement result, and then infers a second measurement result of the reception quality of a second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model; and a transmission unit that transmits the measured first measurement result and the inferred second measurement result to a network node, the transmission unit transmitting identification information to the network node for identifying whether each of the transmitted measurement results is a measured value or an inferred value.

[0009] A network node according to a sixth 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: a first measurement result obtained by the user equipment measuring a reception quality of a first measurement object; and a second measurement result obtained by the user equipment inferring a second measurement result of the reception quality of a second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model. The receiving unit receives from the user equipment identification information for identifying whether each measurement result transmitted from the user equipment is a measured value or an inferred value.

[0010] 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 mobile communication system according to a first embodiment. FIG. 10 is a diagram showing a specific example of the operation of a mobile communication system according to a second embodiment.

[0011] 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.

[0012] The present disclosure aims to utilize AI / ML technology in mobile communication systems.

[0013] 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.

[0014] (1) First Embodiment The first embodiment will be described.

[0015] (1.1) 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 5th Generation System (5GS) of the 3GPP standard. In the following description, 5GS is used as an example, but 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.

[0016] 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.

[0017] 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).

[0018] 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").

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 4 is a diagram showing the configuration of a protocol stack of a radio interface of a user plane that handles data.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] The PDCP layer performs header compression / decompression, encryption / decryption, and the like.

[0036] 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.

[0037] 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).

[0038] 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.

[0039] 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.

[0040] 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").

[0041] (1.2) 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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

[0050] 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.

[0051] 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.

[0052] Note that if the filter coefficient k is set to 0 (zero), no L3 filtering is applied.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] (1.3) Overview of AI / ML Technology An overview of the AI / ML technology will be described. A mobile communication system 1 according to an embodiment applies the AI / ML technology to wireless communication (i.e., air interface).

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] The data processing unit A4 receives the inference result data and performs processing that utilizes the inference result data.

[0062] 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.

[0063] (1.4) 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 that switches the serving cell (primary cell) of the UE 100 under the initiative of the RRC layer.

[0064] The UE 100 has an AI / ML model for inferring the measurement results of one measurement target from the measurement results of another measurement target. The AI / ML model according to the embodiment is a model that uses the measurement results of one measurement target as inference data (input parameters) and outputs the measurement results of another measurement target. The measurement target may be synonymous with the measurement object.

[0065] Here, 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 SSB (synchronization signal (SS) / physical broadcast channel (PBCH)), a channel state information (CSI)-RS, a demodulation (DM)-RS, or the like. The reference signal (RS) may be a positioning-RS. The reference signal (RS) may be another reference signal. In the embodiment, an example in which the measurement object is a cell (or a frequency) will be mainly described.

[0066] The "measurement result" may be a set of an ID of a measurement target and its measurement value. The "measurement value" may be a reference signal received power (RSRP), a reference signal radio quality (RSRQ), a signal-to-interference-and-noise ratio (SINR), a bit error rate (BER), a block error rate (BLER), or the like.

[0067] The "inference data" may include other parameters, with the measurement result of a certain measurement object as one of the input parameters. The other parameters may be information on the geographical location of the UE100, information on the moving speed of the UE100, information on the receiver 111 used for the measurement, and / or information on the attributes of the measurement object (e.g., frequency, etc.). The AI / ML model possessed by the UE100 is assumed to have learned the correlation between these parameters through model learning. At least a partially trained AI / ML model may be set to the UE100 by the gNB200. For example, the AI / ML model may be transferred from the gNB200 to the UE100 when communication between the gNB200 and the UE100 starts. Alternatively, the UE100 may generate a trained AI / ML model by performing model learning in various environments.

[0068] FIG. 8 is a diagram showing an example of an operation scenario of the mobile communication system 1 according to the embodiment.

[0069] 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.

[0070] In step S1 (measurement configuration), gNB200 transmits an RRC message including a measurement configuration to UE100. UE100 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.

[0071] 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 each cell belonging to each frequency specified in the measurement object.

[0072] In step S3 (model inference), the UE 100 uses the measurement result of a certain measurement target (for example, cell a) as inference data to infer the measurement result of another cell (for example, cell b) using the AI / ML model 101.

[0073] In step S4 (measurement report), the UE 100 transmits the measurement results of each cell (including the inferred measurement results) to the gNB 200 by an RRC message (measurement report) at a timing determined according to the reporting setting associated with the measurement object. The gNB 200 receives the measurement report. The gNB 200 performs mobility control of the UE 100 based on the measurement results of each cell included in the measurement report. For example, the gNB 200 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.

[0074] In the model inference (step S3) in such an operation, it may be difficult for the UE 100 to appropriately determine which measurement object to actually measure and which measurement object to infer. Also, if the combination of the measured measurement object and the measurement object whose measurement result is inferred based on the measurement result of the measured measurement object (i.e., the combination of the measured measurement object and the inferred measurement object) is inappropriate, it is difficult to perform the model inference accurately.

[0075] (1.5) Operation According to First Embodiment First, an overview of the operation according to the first embodiment will be described. In the first embodiment, an operation capable of performing appropriate model inference when performing model inference of measurement results at, for example, a cell level (cell unit) using a UE-side model will be described.

[0076] In the first embodiment, in step S1 (measurement setting) of Figure 8, UE100 receives information from gNB200 for identifying a combination of a first measurement object for which UE100 actually measures reception quality and a second measurement object for which UE100 can infer the measurement result of the reception quality based on the measurement result of the first measurement object. Then, in steps S2 (measurement) and S3 (model inference) of Figure 8, UE100 obtains a first measurement result by measuring the first measurement object, and then infers a second measurement result of the second measurement object based on the first measurement result using AI / ML model 101.

[0077] Thus, according to the first embodiment, the UE 100 receives from the gNB 200 information for identifying a combination of a first measurement object for which the UE 100 actually measures reception quality and a second measurement object for which the UE 100 can infer the measurement result of the reception quality based on the measurement result of the first measurement object. Therefore, it is possible to use an appropriate combination as a combination of a measurement object to be measured and a measurement object to be inferred. Therefore, the UE 100 can perform model inference with high accuracy.

[0078] The UE 100 that performs such operations has a receiver 110 that receives information from the gNB 200 to identify a combination of a first measurement object for which the UE 100 actually measures reception quality and a second measurement object for which the UE 100 can infer the measurement result of the reception quality based on the measurement result of the first measurement object, and a control unit 130 that infers the second measurement result of the second measurement object based on the first measurement result after obtaining the first measurement result by measuring the first measurement object (see FIG. 2). On the other hand, the gNB 200 has a transmitter 210 that transmits to the UE 100 information for identifying a combination of a first measurement object for which the UE 100 actually measures reception quality and a second measurement object for which the UE 100 can infer the measurement result of the reception quality based on the measurement result of the first measurement object (see FIG. 3).

[0079] Each of the first measurement object and the second measurement object is one of a cell, a frequency, a beam, a reference signal, and a measurement object. For example, each measurement object is a cell (or a frequency).

[0080] The UE 100 may transmit proposal information indicating a combination of a first measurement object to be measured and a second measurement object to be inferred to the gNB 200. The combination may be a combination of measurement objects supported by the AI / ML model 101 of the UE 100.

[0081] The combination of the first measurement target to be measured and the second measurement target to be inferred may be a combination of the first measurement target and the second measurement target that transmit radio waves from the same location (co-location). Specifically, the combination of the first measurement target and the second measurement target that transmit radio waves from the same antenna or the same position can be considered to have the same or similar radio wave propagation path in space. The gNB 200 notifies the UE 100 of information on the combination. This makes it possible to improve the inference accuracy in the UE 100.

[0082] For example, in some implementations, different cells with different frequencies transmit from antennas that are considered to be physically located at the same location, such as when an 800 MHz cell and a 2 GHz cell are transmitting from the same antenna. In this case, although the channel characteristics vary depending on the frequency, these channel responses are expected to have a certain degree of correlation. Furthermore, since the radio wave path is the same for the same antenna, it is possible to infer the difference in propagation loss (path loss) and / or reflection loss / diffraction loss due to the difference in frequency.

[0083] Next, a specific example of the operation of the mobile communication system 1 according to the first embodiment will be described. Fig. 9 is a diagram showing a specific example of the operation of the mobile communication system 1 according to the first embodiment.

[0084] In step S101, UE100 is in an RRC connected state with the cell of gNB200 as the serving cell.

[0085] In step S102, the UE 100 may transmit to the gNB 200 proposal information (preference information) indicating input data (measurement targets to be measured) and output data (measurement targets to be inferred) supported by the AI ​​ / ML model 101 that the UE 100 possesses. The gNB 200 may receive the proposal information (preference information). The UE 100 may include the proposal information (preference information) in a UE Assistance Information message or a UE Capability message, which is a type of RRC message, and transmit the message to the gNB 200.

[0086] In step S103, 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. The RRC message (measurement configuration) of step S103 may include information for configuring (specifying) the AI / ML model 101 of the UE 100, for example, a model ID.

[0087] The RRC message (measurement setting) of step S103 includes information (hereinafter also referred to as "identification information") for identifying a combination of a first measurement object to be measured and a second measurement object to be inferred (a measurement object that can be inferred). The gNB 200 may set a first measurement object to be used as input data in model inference and a second measurement object to be used as an output (inference) result, taking into account the model already set in the UE 100. The gNB 200 may set a measurement object that is important (main information) in mobility control as the first measurement object to be measured. The gNB 200 may set a measurement object that is only reference information in mobility control as the second measurement object. The combination may be a combination of measurement objects that can be considered to be the same path, that is, a combination of measurement objects transmitting from the same antenna or the same position. The identification information may specify the measurement object to be measured and / or the measurement object to be inferred by a cell ID, a frequency ID (ARFCN: Absolute Radio-Frequency Channel Number), a measurement object ID, a measurement ID, and / or a reference signal ID.

[0088] In step S104, the UE 100 performs measurement on the first measurement object to be measured based on the identification information. Also, the UE 100 infers the second measurement result of the second measurement object to be inferred using the AI ​​ / ML model 101 based on the first measurement result of the first measurement object. For example, when the combination is "cell # 1 on Freq # 1" and "cell # 2 on Freq # 2", the UE 100 measures the RSRP (Reference Signal Received Power) of cell # 1, and then infers the RSRP of cell # 2 taking into account the detuning frequency (difference) between Freq # 1 and Freq # 2. Also, the UE 100 may perform model inference using the estimated information of the propagation path as input data by estimating whether it is within line of sight or whether there is reflection / diffraction, taking into account the timing advance value and the RSRP of cell # 1.

[0089] In step S105, the UE 100 transmits a message including the first measurement result measured in step S104 and the inferred measurement result to the gNB 200. The gNB 200 receives the message. In an embodiment, the message is an L3 measurement report message, but the message may also be a UE Assistance Information message.

[0090] In step S106, gNB200 performs mobility control (e.g., handover control) of UE100 based on the measurement results received from UE100 in step S105.

[0091] (2) Second Embodiment The second embodiment will be described mainly focusing on the differences from the first embodiment. The operation of the second embodiment may be based on the operation of the first embodiment. Alternatively, the operation of the second embodiment may not be based on the operation of the first embodiment.

[0092] In the second embodiment, the UE 100 can identify a combination of a first measurement object to be measured and a second measurement object to be inferred by the operation according to the first embodiment or another method. The other method may be, for example, a method using a UE-side model (AI / ML model 101), but the inference accuracy may be lower than that of the operation according to the first embodiment.

[0093] As described above, in the measurement report, the UE 100 transmits the measured first measurement result and the inferred second measurement result to the gNB 200. Here, if the gNB 200 cannot clearly identify whether each measurement result received from the UE 100 is a measured value or an inferred value, there is a concern that the gNB 200 may not be able to perform appropriate mobility control. Furthermore, if the measurement result is an inferred value, there is a concern that the gNB 200 may not be able to perform appropriate mobility control unless the gNB 200 can grasp the inference accuracy.

[0094] In the second embodiment, such a problem is solved by adding information about model inference in the UE 100 to the measurement report. When the UE 100 transmits a measurement report that is an RRC message to the gNB 200, the UE 100 may include the measured first measurement result, the inferred second measurement result, and additional information (auxiliary information) in the measurement report.

[0095] First, an overview of the operation according to the second embodiment will be described with reference to Fig. 8. In the second embodiment, when model inference of measurement results at the cell level (cell unit) is performed using a UE-side model, for example, the gNB 200 performs appropriate mobility control.

[0096] In step S2 (measurement), the UE 100 measures the reception quality of the first measurement object and obtains a first measurement result.

[0097] In step S3 (model inference), the UE 100 uses the AI / ML model 101 to infer a second measurement result of the reception quality of the second measurement object based on the first measurement result.

[0098] In step S4 (measurement report), the UE 100 transmits the measured first measurement result and the inferred second measurement result to the gNB 200. Here, the UE 100 transmits to the gNB 200 identification information for identifying whether each measurement result to be transmitted is a measured value or an inferred value.

[0099] Thus, according to the first embodiment, the UE 100 transmits identification information for identifying whether each measurement result to be transmitted is a measured value or an inferred value to the gNB 200. This allows the gNB 200 to clearly identify whether each measurement result received from the UE 100 is a measured value or an inferred value, thereby enabling appropriate mobility control.

[0100] The UE 100 that performs such operations has a control unit 130 that measures the reception quality of a first measurement object to obtain a first measurement result, and then uses the AI / ML model 101 to infer a second measurement result of the reception quality of a second measurement object based on the first measurement result, and a transmission unit 120 that transmits the measured first measurement result, the inferred second measurement result, and identification information to the gNB 200 (see FIG. 2). On the other hand, the gNB 200 has a receiving unit 220 that receives the first measurement result, the second measurement result, and identification information from the UE 100 (see FIG. 3).

[0101] The UE 100 may transmit information indicating the inference accuracy of the inferred second measurement result to the gNB 200. The inference accuracy may be a likelihood indicating the likelihood of the estimation. The inference accuracy may be output from the AI / ML model 101 during model inference. This allows the gNB 200 to grasp the inference accuracy when the measurement result is an inferred value, thereby enabling the gNB 200 to perform appropriate mobility control.

[0102] The UE 100 may transmit the inferred second measurement result to the gNB 200 only if a value indicating the inference accuracy of the inferred second measurement result exceeds a threshold. The threshold may be set by the gNB 200 to the UE 100. This prevents inappropriate measurement results (inference results) from being reported to the gNB 200, enabling the gNB 200 to perform appropriate mobility control.

[0103] The UE 100 may transmit information (e.g., a model ID) for identifying the AI / ML model 101 used for the inference to the gNB 200. It is assumed that a UE 100 having multiple AI / ML models 101 selects one AI / ML model 101 from among them and uses it for the inference. Under such an assumption, the UE 100 notifies the gNB 200 of information for identifying the AI / ML model 101 used for the inference, so that the gNB 200 can grasp the inference accuracy of the inferred second measurement result (inference result), etc.

[0104] Next, a specific example of the operation of the mobile communication system 1 according to the second embodiment will be described below. Fig. 10 is a diagram showing a specific example of the operation of the mobile communication system 1 according to the second embodiment.

[0105] In step S201, UE100 is in an RRC connected state with the cell of gNB200 as the serving cell.

[0106] 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.

[0107] The RRC message (measurement settings) in step S202 may include at least one of the following settings: 1) a setting of whether to provide information identifying whether the value is an actual measurement or an inferred value; 2) a setting of whether to report the accuracy of the inference result; 3) a threshold for the inference accuracy; and 4) a setting of whether to report the model ID used for the inference.

[0108] In step S203, UE 100 performs measurement on a first measurement object to be measured. Furthermore, UE 100 infers a second measurement result of a second measurement object to be inferred based on a first measurement result of the first measurement object using AI / ML model 101. UE 100 may obtain the inference accuracy of the inferred second measurement result from AI / ML model 101, compare a value indicating the inference accuracy with a threshold, and discard (i.e., exclude from reporting) second measurement results (inferred values) below the threshold.

[0109] In step S204, the UE 100 transmits a message including the first measurement result measured in step S204, the inferred measurement result, and additional information (auxiliary information) to the gNB 200. The gNB 200 receives the message. In an embodiment, the message is an L3 measurement report message, but the message may also be a UE Assistance Information message.

[0110] The additional information in step S204 may be provided for each measurement ID or each measurement result, and may include at least one of a) identification information for identifying whether the value is an actual measurement or an inferred value, b) information on the accuracy of the inference result (e.g., [%]), and c) a model ID used for the inference.

[0111] In step S205, gNB200 performs mobility control (e.g., handover control) of UE100 based on the measurement results received from UE100 in step S205.

[0112] The gNB200 may make various decisions (e.g., handover decisions) by prioritizing (trusting) the actual measured value over the inferred value based on a) identification information for identifying whether the value is an actual measured value or an inferred value. For example, the gNB200 may make various decisions by prioritizing the actual measured value under the assumption that the inference accuracy is low.

[0113] The gNB200 may make various decisions based on information on the accuracy of the inference result (e.g., [%]) and prioritize the actual measured value if the inference accuracy is low. If the inference accuracy does not meet a certain standard, the gNB200 may discard the corresponding measurement result (inferred value).

[0114] The gNB200 may determine the inference accuracy of the inference value based on the model ID used for the inference. For example, the gNB200 may use a model database to obtain model performance information from the model ID and determine the inference accuracy.

[0115] (3) Other Embodiments The first and second embodiments described above may be implemented independently, or at least a portion of the operation according to the first embodiment may be implemented in combination with at least a portion of the operation according to the second embodiment.

[0116] 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. Furthermore, the present invention is not limited to mobility control in the RRC connected state, but may also be applied to mobility control (e.g., cell reselection) in the RRC idle state or the RRC inactive state.

[0117] In the above-described embodiment, an example in which signaling related to the AI / ML technology is an RRC message, which is signaling of the RRC layer (i.e., Layer 3), has been mainly described. However, the AI / ML-related signaling may be MAC CE, which is signaling of the MAC layer (i.e., Layer 2), or 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 or 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] This application claims priority from Japanese Patent Application No. 2024-060715 (filed April 4, 2024), the entire contents of which are incorporated herein by reference.

[0126] (4) Supplementary Notes The following are additional notes regarding the features of the above-described embodiment.

[0127] Supplementary Note 1: A communication method executed by a user device in a mobile communication system, comprising: receiving, from a network node, information for identifying a combination of a first measurement object for which the user device actually measures reception quality and a second measurement object for which the user device can infer a measurement result of reception quality based on a measurement result of the first measurement object; and, after obtaining a first measurement result by measuring the first measurement object, inferring a second measurement result of the second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model.

[0128] Supplementary Note 2: The communication method according to Supplementary Note 1, wherein each of the first measurement object and the second measurement object is one of a cell, a frequency, a beam, a reference signal, and a measurement object.

[0129] Supplementary Note 3: The communication method according to Supplementary Note 1 or 2, further comprising transmitting proposal information indicating the combination to the network node.

[0130] Supplementary Note 4: The communication method according to any one of Supplementary Notes 1 to 3, wherein the combination is a combination of the first target object and the second target object that transmit radio waves from the same location.

[0131] Supplementary Note 5: The communication method according to any one of Supplementary Notes 1 to 4, wherein the user equipment receives a Radio Resource Control (RRC) message from the network node, the RRC message including information for identifying the combination as a measurement configuration.

[0132] Supplementary Note 6: The communication method according to any one of Supplementary Notes 1 to 5, further comprising transmitting the measured first measurement result and the inferred second measurement result to the network node.

[0133] Supplementary Note 7. The communication method according to any one of Supplementary Notes 1 to 6, wherein the user equipment transmits a measurement report, which is a Radio Resource Control (RRC) message, to the network node, the measurement report including the measured first measurement result and the inferred second measurement result.

[0134] Supplementary Note 8: A user equipment for use in a mobile communication system, comprising: a receiver that receives, from a network node, information for identifying a combination of a first measurement object for which the user equipment actually measures reception quality and a second measurement object for which the user equipment can infer a measurement result of reception quality based on a measurement result of the first measurement object; and a controller that, after obtaining a first measurement result by measuring the first measurement object, infers a second measurement result of the second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model.

[0135] Supplementary Note 9: A network node used in a mobile communication system, comprising: a transmitter that transmits to the user equipment information for identifying a combination of a first measurement object for which a user equipment actually measures reception quality and a second measurement object for which the user equipment can infer a measurement result of reception quality using an artificial intelligence or machine learning (AI / ML) model based on a measurement result of the first measurement object.

[0136] Supplementary Note 10: A communication method executed by a user equipment in a mobile communication system, comprising: measuring a reception quality of a first measurement object to obtain a first measurement result; inferring a second measurement result of the reception quality of a second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model; and transmitting the measured first measurement result and the inferred second measurement result to a network node, wherein the user equipment transmits identification information to the network node to identify whether each of the transmitted measurement results is a measured value or an inferred value.

[0137] Supplementary Note 11: The communication method according to Supplementary Note 10, wherein each of the first measurement object and the second measurement object is one of a cell, a frequency, a beam, a reference signal, and a measurement object.

[0138] Supplementary Note 12: The communication method according to Supplementary Note 10 or 11, wherein the user equipment transmits information indicating an inference accuracy of the inferred second measurement result to the network node.

[0139] Supplementary Note 13: The communication method according to any one of Supplementary Notes 10 to 12, wherein the user equipment transmits the inferred second measurement result to the network node only if a value indicating an inference accuracy of the inferred second measurement result exceeds a threshold.

[0140] Supplementary Note 14. The communication method of Supplementary Note 10, wherein the user device transmits information to the network node to identify the AI / ML model used for the inference.

[0141] Supplementary Note 15. The communication method according to any of Supplementary Notes 10 to 14, wherein the user equipment transmits a measurement report, which is a Radio Resource Control (RRC) message, to the network node, the measurement report including the measured first measurement result, the inferred second measurement result, and the identification information.

[0142] Supplementary Note 16: A user equipment for use in a mobile communication system, comprising: a control unit that measures a reception quality of a first measurement object to obtain a first measurement result, and then infers a second measurement result of the reception quality of a second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model; and a transmission unit that transmits the measured first measurement result and the inferred second measurement result to a network node, wherein the transmission unit transmits identification information to the network node to identify whether each of the transmitted measurement results is a measured value or an inferred value.

[0143] Supplementary Note 17: A network node used in a mobile communication system, comprising: a receiving unit that receives from the user equipment: a first measurement result obtained by the user equipment measuring a reception quality of a first measurement object; and a second measurement result obtained by the user equipment inferring a second measurement result of the reception quality of a second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model, wherein the receiving unit receives from the user equipment identification information for identifying whether each measurement result transmitted from the user equipment is a measured value or an inferred value.

[0144] 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 device in a mobile communication system, comprising: receiving information from a network node to identify a combination of a first measurement object for which the user device actually measures reception quality and a second measurement object for which the user device can infer a measurement result of reception quality based on the measurement result of the first measurement object; and, after obtaining a first measurement result by measuring the first measurement object, inferring a second measurement result of the second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model.

2. The communication method according to claim 1, wherein each of the first measurement object and the second measurement object is one of a cell, a frequency, a beam, a reference signal, and a measurement object.

3. The communication method according to claim 1, further comprising transmitting proposal information indicating the combination to the network node.

4. A communication method according to any one of claims 1 to 3, wherein the combination is a combination of the first measurement target and the second measurement target that transmit radio waves from the same location.

5. The communication method according to any one of claims 1 to 3, wherein the user equipment receives a radio resource control (RRC) message from the network node, the radio resource control (RRC) message including information for identifying the combination as a measurement configuration.

6. A communication method according to any one of claims 1 to 3, further comprising transmitting the measured first measurement result and the inferred second measurement result to the network node.

7. A communication method according to any one of claims 1 to 3, wherein the user equipment transmits a measurement report, which is a Radio Resource Control (RRC) message, to the network node, the measurement report including the measured first measurement result and the inferred second measurement result.

8. A user equipment for use in a mobile communication system, comprising: a receiving unit that receives, from a network node, information for identifying a combination of a first measurement object for which the user equipment actually measures reception quality and a second measurement object for which the user equipment can infer a measurement result of reception quality based on the measurement result of the first measurement object; and a control unit that, after obtaining a first measurement result by measuring the first measurement object, infers a second measurement result of the second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model.

9. A network node used in a mobile communication system, comprising a transmitter that transmits to the user equipment information for identifying a combination of a first measurement object for which a user equipment actually measures reception quality and a second measurement object for which the user equipment can infer the measurement result of reception quality using an artificial intelligence or machine learning (AI / ML) model based on the measurement result of the first measurement object.

10. A communication method executed by a user device in a mobile communication system, comprising: measuring the reception quality of a first measurement object to obtain a first measurement result; inferring a second measurement result of the reception quality of a second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model; and transmitting the measured first measurement result and the inferred second measurement result to a network node, wherein the user device transmits identification information to the network node to identify whether each of the transmitted measurement results is a measured value or an inferred value.

11. The communication method according to claim 10, wherein each of the first measurement object and the second measurement object is one of a cell, a frequency, a beam, a reference signal, and a measurement object.

12. The communication method according to claim 10, wherein the user equipment transmits information indicating the inference accuracy of the inferred second measurement result to the network node.

13. The communication method according to claim 10, wherein the user equipment transmits the inferred second measurement result to the network node only if a value indicating the inference accuracy of the inferred second measurement result exceeds a threshold.

14. The communication method according to claim 10, wherein the user device transmits information to the network node to identify the AI / ML model used in the inference.

15. A communication method according to any one of claims 10 to 14, wherein the user equipment transmits a measurement report, which is a Radio Resource Control (RRC) message, to the network node, the measurement report including the measured first measurement result, the inferred second measurement result and the identification information.

16. A user equipment for use in a mobile communication system, comprising: a control unit that measures the reception quality of a first measurement object to obtain a first measurement result, and then infers a second measurement result of the reception quality of a second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model; and a transmission unit that transmits the measured first measurement result and the inferred second measurement result to a network node, wherein the transmission unit transmits identification information to the network node to identify whether each of the transmitted measurement results is a measured value or an inferred value.

17. A network node used in a mobile communication system, comprising: a receiving unit that receives from a user device: a first measurement result obtained by the user device measuring the reception quality of a first measurement object; and a second measurement result obtained by the user device inferring a second measurement result of the reception quality of a second measurement object based on the first measurement result using an artificial intelligence or machine learning (AI / ML) model; and the receiving unit receives from the user device identification information for identifying whether each measurement result transmitted from the user device is a measured value or an inferred value.

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