WO2025211279A1 - Communication method, user equipment, and network node - Google Patents

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

This disclosure relates to a communication method, user equipment, and network node used in a mobile communication system.

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

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

A communication method according to a first aspect is a communication method executed by a user device in a mobile communication system, and includes 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 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.

The user equipment according to the second aspect is a user equipment used in a mobile communications system, and includes a receiver that receives, from a network node, information for identifying a combination of a first measurement object whose reception quality the user equipment actually measures and a second measurement object whose reception quality measurement result the user equipment can infer based on the 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.

The network node according to the third aspect is a network node used in a mobile communications system, and has 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.

A communication method according to a fourth aspect is a communication method executed by a user device in a mobile communication system, and includes the steps of 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. 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.

A user device according to a fifth aspect is a user device used in a mobile communications system, and includes: 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. 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.

A network node according to a sixth aspect is a network node used in a mobile communications system, and includes a receiving unit that receives from the 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. The receiving unit receives identification information from the user device for identifying whether each measurement result transmitted from the user device is a measured value or an inferred value.

1 is a diagram illustrating a configuration example of a mobile communication system according to an embodiment. FIG. 1 is a diagram illustrating a configuration example of a UE (user equipment) according to an embodiment. A diagram showing an example configuration of a gNB (network node) according to an embodiment. FIG. 10 is a diagram showing the configuration of a protocol stack of a radio interface of a user plane that handles data. FIG. 1 is a diagram showing the configuration of the protocol stack of the radio interface of the control plane that handles signaling (control signals). FIG. 10 is a diagram illustrating a configuration related to measurements by a UE according to an embodiment. FIG. 1 is a diagram showing a functional block configuration of AI/ML technology in a mobile communication system according to an embodiment. FIG. 2 is a diagram illustrating an example of an operation scenario of the mobile communication system according to the embodiment. FIG. 2 is a diagram showing a specific example of the operation of the mobile communication system according to the first embodiment. FIG. 10 is a diagram showing a specific example of the operation of the mobile communication system according to the second embodiment.

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

The purpose of this disclosure is to utilize AI/ML technology in mobile communication systems.

The mobile communication system according to the embodiment will be described with reference to the drawings. In the drawings, the same or similar parts are denoted by the same or similar reference numerals.

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

(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.

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. The 5GC 20 may also be simply referred to as the core network (CN) 20. The RAN 10 and the CN 20 constitute the network 5 of the mobile communication system 1.

UE100 is a mobile wireless communication device. UE100 may be any device used by a user. For example, UE100 is 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). The link in the transmission direction from UE100 to network 5 is called the uplink (UL), and the link in the transmission direction from network 5 to UE100 is called the downlink (DL).

NG-RAN10 includes base stations (referred to as "gNBs" in 5G systems) 200, which are a type of network node. gNBs 200 are connected to each other via an Xn interface, which is an interface between base stations. gNBs 200 manage one or more cells. gNBs 200 perform wireless communication with UEs 100 that have established a connection with their own cell. gNBs 200 have radio resource management (RRM) functions, user data (hereinafter simply referred to as "data") routing functions, measurement control functions for mobility control and scheduling, and more. "Cell" is used as a term to indicate the smallest unit of a wireless communication area. "Cell" is also used as a term to indicate functions or resources for wireless communication with UEs 100. One cell belongs to one carrier frequency (hereinafter simply referred to as "frequency").

In addition, gNBs can also connect to the EPC (Evolved Packet Core), which is the LTE core network. LTE base stations can also connect to 5GC. LTE base stations and gNBs can also be connected via a base station-to-base station interface.

5GC20 includes an AMF (Access and Mobility Management Function) and a UPF (User Plane Function) 300. The AMF performs various mobility controls for UE100. The AMF manages the mobility of UE100 by communicating with UE100 using NAS (Non-Access Stratum) signaling. The UPF controls data forwarding. The AMF and UPF are connected to gNB200 via the NG interface, which is an interface between the base station and the core network.

FIG. 2 is a diagram showing an example configuration of a UE 100 (user equipment) according to an embodiment. The UE 100 has a receiving unit 110, a transmitting unit 120, and a control unit 130. The receiving unit 110 and the transmitting unit 120 constitute a wireless communication unit 140 that performs wireless communication with the gNB 200.

The receiving unit 110 performs various types of reception under the control of the control unit 130. The receiving unit 110 includes an antenna and a receiver. The receiver converts the radio signal received by the antenna into a baseband signal (received signal) and outputs it to the control unit 130.

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 the baseband signal (transmission signal) output by the control unit 130 into a radio signal and transmits it from the antenna.

The control unit 130 performs various controls and processes in the UE 100. Such processes include the processes of each layer described below. The operations of the UE 100 described above and below may be operations under the control of 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.

Figure 3 is a diagram showing an example configuration of a gNB200 (network node) according to an embodiment. The gNB200 has a transmitter 210, a receiver 220, a controller 230, and a network communication unit 240. The transmitter 210 and receiver 220 constitute a wireless communication unit 250 that performs wireless communication with the UE100. The network communication unit 240 has a transmitter 241 that performs transmission and a receiver 242 that performs reception.

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 the baseband signal (transmission signal) output by the control unit 230 into a radio signal and transmits it from the antenna.

The receiving unit 220 performs various types of reception under the control of the control unit 230. The receiving unit 220 includes an antenna and a receiver. The receiver converts the radio signal received by the antenna into a baseband signal (received signal) and outputs it to the control unit 230.

The control unit 230 performs various controls and processes in the gNB 200. Such processes include the processes of 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.

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 may be composed of a CU (Central Unit) and a DU (Distributed Unit) (i.e., functionally divided), and the two units may be connected via an F1 interface, which is a fronthaul interface.

Figure 4 shows the protocol stack configuration of the user plane radio interface that handles data.

The user plane radio interface protocol has 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.

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 the 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 CRC (Cyclic Redundancy Code) parity bits scrambled by the RNTI added.

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 UE100 and the MAC layer of gNB200 via a transport channel. The MAC layer of gNB200 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 UE100.

The RLC layer uses the functions of the MAC layer and PHY layer to transmit data to the RLC layer on the receiving side. Data and control information are transmitted between the RLC layer of UE100 and the RLC layer of gNB200 via logical channels.

The PDCP layer performs header compression/decompression, encryption/decryption, etc.

The SDAP layer maps IP flows, which are the units by which the core network controls QoS (Quality of Service), to radio bearers, which are the units by which the AS (Access Stratum) controls QoS. Note that if the RAN is connected to the EPC, SDAP is not required.

Figure 5 shows the protocol stack configuration of the radio interface of the control plane, which handles signaling (control signals).

The protocol stack for the radio interface of the control plane has an RRC (Radio Resource Control) layer and a NAS (Non-Access Stratum) layer instead of the SDAP layer shown in Figure 4.

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 in accordance with 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.

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 UE100 and the NAS layer of AMF300A. In addition to the radio interface protocol, UE100 also has an application layer, etc. The layer below the NAS layer is called the AS layer (also simply referred to as "AS").

(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.

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 it 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 (measurement report types): event-triggered reporting, periodic reporting, and event-triggered periodic reporting for measurement reports.

The association between a measurement object (measurement object ID) and a reporting configuration (measurement configuration ID) is made by a measurement identity (measurement ID). A measurement ID links one measurement object and one reporting configuration of the same RAT. Using multiple measurement IDs (one per measurement object and reporting configuration pair) makes it possible to associate 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.

UE100 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 that cell. In doing so, UE100 is configured to consider a subset of the detected beams. Here, filtering, which is measurement averaging, is performed at two different levels. UE100 first derives beam quality using L1 filtering, which is filtering at the physical layer (PHY, Layer 1 (L1)), and then derives cell quality from multiple beams using L3 filtering, which is filtering at the RRC layer (Layer 3 (L3)) level. Note that cell quality from beam measurements is derived in the same way for serving and non-serving cells. UE100 may include measurement results of the X best beams in the L3 measurement report, depending on the configuration by gNB200.

Figure 6 is a diagram showing the configuration for measurements by a UE 100 according to an embodiment. The control unit 130 of the UE 100 has 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 the wireless quality of a beam measured by one of the receivers 111 in the receiving unit 110. In the example shown, there are two receivers 111 (receiver 111a and receiver 111b), but there may be one receiver 111, or three or more receivers 111. Below, the receiver 111 is also referred to as an "RF (Radio Frequency) chain." The multiple receivers 111 may support different frequencies.

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 ^A1 after L1 filtering to the beam combining/selecting unit 12 and the L3 beam filter 15.

The beam integrating/selecting unit 12 integrates the beam-specific measurement results ^A1 , derives the cell radio quality (cell quality) B, and outputs the cell quality B to the L3 filter 13. The operation of the beam integrating/selecting unit 12 is configured by RRC signaling from the gNB 200.

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

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)×F _n-1 +a×M _n ...(1)
where M _n is the latest measurement from the physical layer (L1), _{F n} is the updated filtered measurement, used for evaluation of reporting criteria or L3 measurement reporting, and F _n-1 is the old filtered measurement, F ₀ is set to M ₁ when the first measurement is received from the physical layer (L1).

When MeasObjectNR is configured in RRC, a = 1/2 ^(ki/4) , where k _i is the filter coefficient (filterCoefficient) of the corresponding measurement of the i-th QuantityConfigNR in the quantityConfigNR-List, and i is indicated by the quantityConfigIndex in MeasObjectNR. For other measurements, a = 1/2 ^(k/4) , where k is the filter coefficient of the corresponding measurement received by the quantityConfig.

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

Note that if the filter coefficient k is set to 0 (zero), L3 filtering is not applied.

The evaluation unit 14 evaluates whether an L3 measurement report D to the gNB 200 is necessary. This evaluation can be made based on a comparison of multiple measurement flows at reference point C, for example, different measurement results. This is shown by input C and input ^C1 . The evaluation unit 14 performs a measurement reporting event evaluation corresponding to the reporting criteria at least every time a new measurement result is reported at points C and ^C1 . 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.

The L3 beam filter 15 filters the k measurement results A ¹ (i.e., beam-specific measurement results) on a per-beam basis and outputs k measurement results E (i.e., beam-specific measurement results) to the beam selection unit 16. The measurement results E are used as input for selecting the X measurement results to be reported.

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.

(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).

FIG. 7 is a diagram showing the functional block configuration of AI/ML technology in a mobile communication system 1 according to an 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.

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

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 "model" or "AI/ML model") through machine learning using learning data, derives (generates, 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,
y = ax + b
In this sense, 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 training data. Unsupervised learning is a method that does not use correct answer data as 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.

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 inference result data to the data processing unit A4. For example,
y = ax + b
In this case, x corresponds to the inference data and y corresponds to the inference result data. Note that "y = ax + b" is a model. A model with optimized slope and intercept, 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.

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

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.

(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.

UE100 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 of this 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.

Here, the "measurement target" is one of a cell, frequency, beam, reference signal, and measurement object. The reference signal (RS) may be SSB (synchronization signal (SS)/physical broadcast channel (PBCH)), channel state information (CSI)-RS, demodulation (DM)-RS, etc. The reference signal (RS) may be a positioning-RS. The reference signal (RS) may also be another reference signal. In the embodiment, an example in which the measurement target is a cell (or frequency) will be mainly described.

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

The "inference data" may include the measurement results of a certain measurement object as one of the input parameters, and other parameters. The other parameters may be information on the geographical location of UE100, information on the movement speed of 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 UE100 is assumed to have learned the correlation between these parameters through model learning. An at least partially learned AI/ML model may be set to UE100 by gNB200. For example, the AI/ML model may be transferred from gNB200 to UE100 when communication between gNB200 and UE100 begins. Alternatively, UE100 may generate a learned AI/ML model by performing model learning in various environments.

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

In the example shown, UE100 is in an RRC connected state with cell a of gNB200 as the serving cell. There are multiple neighboring cells (cell b, cell c, and cell d) that partially overlap with this serving cell. The neighboring cells may be managed by the same gNB200 as the serving cell. The neighboring cells may also be managed by a gNB200 different from the serving cell. The frequencies of each cell may be the same or different.

In step S1 (measurement configuration), gNB200 sends an RRC message including 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.

In step S2 (measurement), UE100 performs measurements on the measurement target defined according to the measurement object. For example, UE100 measures the reception quality of each cell belonging to each frequency specified in the measurement object.

In step S3 (model inference), UE100 uses the measurement results of a certain measurement target (e.g., cell a) as inference data and infers the measurement results of another cell (e.g., cell b) using AI/ML model 101.

In step S4 (measurement report), UE100 transmits the measurement results of each cell (including the inferred measurement results) to gNB200 via an RRC message (measurement report) at a timing determined according to the reporting settings associated with the measurement object. gNB200 receives the measurement report. gNB200 performs mobility control for UE100 based on the measurement results of each cell included in the measurement report. For example, 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.

In model inference (step S3) during such operation, it may be difficult for UE100 to appropriately determine which measurement objects to actually measure and which measurement objects to infer. Furthermore, if the combination of the measured measurement object and the measurement object whose measurement results are inferred based on the measurement results of the measured measurement object (i.e., the combination of the measurement object to be measured and the measurement object to be inferred) is inappropriate, it is difficult to perform model inference accurately.

(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.

In the first embodiment, in step S1 (measurement setting) of FIG. 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 reception quality based on the measurement result of the first measurement object. Then, in steps S2 (measurement) and S3 (model inference) of FIG. 8, UE100 obtains the first measurement result by measuring the first measurement object, and then infers the second measurement result of the second measurement object based on the first measurement result using AI/ML model 101.

In this way, according to the first embodiment, UE100 receives from gNB200 information 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 reception quality based on the measurement result of the first measurement object, thereby making it possible to use an appropriate combination as the combination of measurement object to be measured and measurement object to be inferred. Therefore, UE100 can perform model inference with high accuracy.

The UE100 that performs such operations has a receiver 110 that receives from the gNB200 information for identifying a combination of a first measurement object for which the UE100 actually measures the reception quality and a second measurement object for which the UE100 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, 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 the AI/ML model 101 (see Figure 2). On the other hand, the gNB200 has a transmitter 210 that transmits to the UE100 information for identifying a combination of the first measurement object for which the UE100 actually measures the reception quality and the second measurement object for which the UE100 can infer the measurement result of the reception quality using the AI/ML model 101 based on the measurement result of the first measurement object (see Figure 3).

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).

UE100 may transmit to gNB200 proposal information indicating a combination of a first measurement object to be measured and a second measurement object to be inferred. The combination may be a combination of measurement objects supported by UE100's AI/ML model 101.

The combination of a first measurement target to be measured and a second measurement target to be inferred may be a combination of a first measurement target and a second measurement target that transmit radio waves from the same location (co-location). Specifically, a combination of a first measurement target and a second measurement target that transmit radio waves from the same antenna or the same location can be considered to have spatially identical or similar radio wave propagation paths. The gNB200 notifies the UE100 of information about this combination. This makes it possible to improve the inference accuracy in the UE100.

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

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

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

In step S102, UE100 may transmit to gNB200 proposal information (preference information) indicating the input data (measurement targets to be measured) and output data (measurement targets to be inferred) supported by its own AI/ML model 101. gNB200 may receive the proposal information (preference information). UE100 may include the proposal information (preference information) in a UE Assistance Information message or a UE Capability message, which are types of RRC messages, and transmit the message to gNB200.

In step S103, gNB200 transmits an RRC message (e.g., an RRC Reconfiguration message) including measurement settings to UE100. UE100 receives the RRC message. The measurement settings include at least one of a measurement ID, a measurement object and its ID, and a reporting setting and its ID. The RRC message (measurement settings) of step S103 may include information for configuring (specifying) the AI/ML model 101 of UE100, such as a model ID.

The RRC message (measurement setting) of step S103 includes information (hereinafter also referred to as "identification information") for identifying the combination of the first measurement object to be measured and the second measurement object to be inferred (measurement object that can be inferred). The gNB200 may set the first measurement object to be used as input data in model inference and the second measurement object to be used as the output (inference) result, taking into account the model already set in the UE100. The gNB200 may set a measurement object that is important (main information) in mobility control as the first measurement object to be measured. The gNB200 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, i.e., a combination of measurement objects transmitting from the same antenna or the same location. The identification information may specify the measurement target to be measured and/or the measurement target to be inferred using a cell ID, frequency ID (ARFCN: Absolute Radio-Frequency Channel Number), measurement object ID, measurement ID, and/or reference signal ID.

In step S104, UE100 performs measurements on the first measurement object to be measured based on the identification information. Furthermore, UE100 infers the second measurement result of the second measurement object to be inferred using AI/ML model 101 based on the first measurement result of the first measurement object. For example, if the combination is "cell #1 on Freq #1" and "cell #2 on Freq #2," UE100 may measure the RSRP (Reference Signal Received Power) of cell #1 and then infer the RSRP of cell #2 by taking into account the detuning frequency (difference) between Freq #1 and Freq #2. Furthermore, UE100 may perform model inference using the estimated information of the propagation path as input data, by estimating whether the propagation path is within line of sight or whether there is reflection/diffraction, taking into account the timing advance value and the RSRP of cell #1.

In step S105, UE100 transmits a message to gNB200 including the first measurement result measured in step S104 and the inferred measurement result. gNB200 receives the message. In the embodiment, the message is an L3 measurement report message, but the message may also be a UE Assistance Information message.

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

(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.

In the second embodiment, the UE 100 is capable of identifying the combination of the first measurement object to be measured and the second measurement object to be inferred, using 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.

As described above, in the measurement report, UE100 transmits the measured first measurement result and the inferred second measurement result to gNB200. Here, if gNB200 cannot clearly distinguish whether each measurement result received from UE100 is a measured value or an inferred value, there is a concern that it will not be able to perform appropriate mobility control. Furthermore, if the measurement result is an inferred value, there is a concern that gNB200 will not be able to perform appropriate mobility control if it cannot grasp the accuracy of the inference.

In the second embodiment, this problem is solved by adding information about model inference in UE100 to the measurement report. When UE100 transmits a measurement report, which is an RRC message, to gNB200, UE100 may include the measured first measurement result, the inferred second measurement result, and additional information (auxiliary information) in the measurement report.

First, an overview of the operation according to the second embodiment will be described with reference to Figure 8. In the second embodiment, we will describe the operation that enables the gNB200 to perform appropriate mobility control when performing model inference of measurement results, for example, at the cell level (cell unit) using a UE-side model.

In step S2 (measurement), UE100 measures the reception quality of the first measurement target and obtains a first measurement result.

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

In step S4 (measurement report), UE100 transmits the measured first measurement result and the inferred second measurement result to gNB200. Here, UE100 transmits identification information to gNB200 to identify whether each transmitted measurement result is a measured value or an inferred value.

In this way, according to the first embodiment, UE100 transmits to gNB200 identification information for identifying whether each measurement result to be transmitted is a measured value or an inferred value. This allows gNB200 to clearly identify whether each measurement result received from UE100 is a measured value or an inferred value, thereby enabling appropriate mobility control.

UE100, which 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 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 gNB200 (see Figure 2). Meanwhile, gNB200 has a reception unit 220 that receives the first measurement result, the second measurement result, and identification information from UE100 (see Figure 3).

UE100 may transmit information indicating the inference accuracy of the inferred second measurement result to gNB200. The inference accuracy may be a likelihood indicating the likelihood of the estimation. The inference accuracy may be output from AI/ML model 101 during model inference. This allows gNB200 to grasp the inference accuracy when the measurement result is an inferred value, enabling gNB200 to perform appropriate mobility control.

UE100 may transmit the inferred second measurement result to gNB200 only if the value indicating the inference accuracy of the inferred second measurement result exceeds a threshold. The threshold may be set by gNB200 to UE100. This prevents inappropriate measurement results (inference results) from being reported to gNB200, enabling gNB200 to perform appropriate mobility control.

UE100 may transmit information (e.g., a model ID) for identifying the AI/ML model 101 used for inference to gNB200. It is assumed that a UE100 having multiple AI/ML models 101 will select one AI/ML model 101 from among them to use for inference. Under such assumptions, by UE100 notifying gNB200 of information for identifying the AI/ML model 101 used for inference, gNB200 can grasp the inference accuracy, etc. of the inferred second measurement result (inference result).

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

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

In step S202, gNB200 transmits an RRC message (e.g., an RRC Reconfiguration message) including measurement settings to UE100. UE100 receives the RRC message. The measurement settings include at least one of a measurement ID, a measurement object and its ID, and a reporting setting and its ID.

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

In step S203, UE 100 performs a measurement on the first measurement object to be measured. UE 100 also infers a second measurement result of a second measurement object to be inferred based on the 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 the value indicating the inference accuracy with a threshold, and discard (i.e., exclude from reporting) second measurement results (inferred values) that fall below the threshold.

In step S204, UE100 transmits a message to gNB200 including the first measurement result measured in step S204, the inferred measurement result, and additional information (auxiliary information). gNB200 receives the message. In the embodiment, the message is an L3 measurement report message, but the message may also be a UE Assistance Information message.

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

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

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 distinguishing between an actual measured value and 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.

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

The gNB200 may c) 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.

(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.

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 type of mobility control. For example, the operations according to the above-described embodiment may be applied to setting handover execution conditions in conditional handover. Alternatively, the operations 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 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 RRC inactive state.

In the above-described embodiment, an example has been described in which signaling related to AI/ML technology is an RRC message, which is signaling of the RRC layer (i.e., Layer 3). However, AI/ML-related signaling may also 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). Downlink AI/ML-related signaling may be UE-specific signaling (dedicated signaling) or broadcast signaling (e.g., SIB (System Information Block)). AI/ML-related signaling may also be signaling in a new layer (e.g., the AI/ML layer) specialized for artificial intelligence or machine learning.

The above-mentioned operational flows do not necessarily have to be implemented separately and independently, but can also be implemented by combining two or more operational flows. For example, some steps in one operational flow may be added to another operational flow, or some steps in one operational flow may be replaced with some steps in another operational flow. In each flow, it is not necessary to execute all steps; only some steps may be executed. Furthermore, the order of steps in each flow may be changed as appropriate.

In the above-mentioned embodiments and examples, an example was described in which the base station is an NR base station (gNB), but the base station may also be an LTE base station (eNB) or a 6G base station. Furthermore, the base station may be a relay node such as an IAB (Integrated Access and Backhaul) node. The base station may also be a DU of an IAB node. Furthermore, UE100 may be an MT (Mobile Termination) of an IAB node. In other words, UE100 may be a terminal function unit (a type of communication module) that allows the base station to control a repeater that relays signals. Such a terminal function unit is referred to as an MT. In addition to IAB-MT, other examples of MT include NCR (Network Controlled Repeater)-MT and RIS (Reconfigurable Intelligent Surface)-MT.

Furthermore, the term "network node" primarily refers to a base station, but may also refer to a core network device or part of a base station (CU, DU, or RU). A network node may also be composed of a combination of at least part of a core network device and at least part of a base station.

A program may be provided that causes a computer to execute each process performed by UE100 or gNB200. The program may be recorded on a computer-readable medium. The computer-readable medium can be used to install the program on a computer. Here, the computer-readable medium on which the program is recorded may be a non-transitory recording medium. The non-transitory recording medium is not particularly limited, but may be, for example, a CD-ROM and/or DVD-ROM. Furthermore, circuits that execute each process performed by UE100 or gNB200 may be integrated, and at least a portion of UE100 or gNB200 may be configured as a semiconductor integrated circuit (chipset, SoC: System on a chip).

The functions performed by UE100 or gNB200 may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), CPUs (Central Processing Units), 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, which is considered a type of circuitry, the circuitry, means, or unit is a combination of hardware and software used to configure the hardware and/or processor.

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 including 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.

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 without departing from the spirit of the invention.

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.

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

・Appendix 1
A communication method performed 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 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;
measuring the first object to be measured to obtain a first measurement result, and then inferring a second measurement result of the second object to be measured based on the first measurement result using an artificial intelligence or machine learning (AI/ML) model.

・Appendix 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.

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

・Appendix 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.

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

Appendix 6
6. The communication method of claim 1, further comprising transmitting the measured first measurement result and the inferred second measurement result to the network node.

Appendix 7
the user equipment sending a measurement report to the network node as a Radio Resource Control (RRC) message;
7. The communication method according to any one of claims 1 to 6, wherein the measurement report includes the first measured measurement result and the second inferred measurement result.

Appendix 8
A user device 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 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.

Appendix 9
A network node for use in a mobile communication system, comprising:
A network node having a transmitter that transmits to a 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 a 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.

Appendix 10
A communication method performed 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;
transmitting the measured first measurement result and the inferred second measurement result to a network node;
The user equipment transmits to the network node identification information for identifying whether each of the transmitted measurement results is a measured value or an inferred value.

Appendix 11
11. The communication method of 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.

Appendix 12
The communication method according to claim 10 or 11, wherein the user equipment transmits information indicating an inference accuracy of the inferred second measurement result to the network node.

Appendix 13
13. The communication method according to any one of claims 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.

Appendix 14
11. The communication method of claim 10, wherein the user equipment transmits information to the network node to identify the AI/ML model used for the inference.

Appendix 15
the user equipment sending a measurement report to the network node as a Radio Resource Control (RRC) message;
15. The communication method according to any one of Supplementary Notes 10 to 14, wherein the measurement report includes the first measured measurement result, the second inferred measurement result, and the identification information.

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

Appendix 17
A network node for use in a mobile communication system, comprising:
A receiving unit receives from the 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;
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.

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: Transmitter 130: Controller 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 collecting unit A2: Model learning unit A3: Model inference unit A4: Data processing unit

Claims (17)

A communication method performed 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 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;
measuring the first object to be measured to obtain a first measurement result, and then inferring a second measurement result of the second object to be measured based on the first measurement result using an artificial intelligence or machine learning (AI/ML) model. 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. The communication method according to claim 1 , further comprising transmitting proposal information indicating the combination to the network node. The communication method according to claim 1 , wherein the combination is a combination of the first target object and the second target object that transmit radio waves from the same location. The communication method according to claim 1 , 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. The method of 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. the user equipment sending a measurement report to the network node as a Radio Resource Control (RRC) message;
The communication method according to claim 1 , wherein the measurement report includes the first measured measurement result and the second inferred measurement result. A user device 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 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. A network node for use in a mobile communication system, comprising:
A network node having a transmitter that transmits to a 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 a 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. A communication method performed 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;
transmitting the measured first measurement result and the inferred second measurement result to a network node;
The user equipment transmits to the network node identification information for identifying whether each of the transmitted measurement results is a measured value or an inferred value. 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. The communication method according to claim 10 , wherein the user equipment transmits information indicating an inference accuracy of the inferred second measurement result to the network node. 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 an inference accuracy of the inferred second measurement result exceeds a threshold. The communication method according to claim 10 , wherein the user equipment transmits information to the network node to identify the AI/ML model used for the inference. the user equipment sending a measurement report to the network node as a Radio Resource Control (RRC) message;
The communication method according to claim 10 , wherein the measurement report includes the first measured measurement result, the second inferred measurement result, and the identification information. A user device for use in a mobile communication system, comprising:
a control unit that measures the reception quality of a first object to be measured to obtain a first measurement result, and then infers a second measurement result of the reception quality of a second object to be measured based on the first measurement result using an artificial intelligence or machine learning (AI/ML) model;
a transmitter configured to transmit the measured first measurement result and the inferred second measurement result to a network node;
The transmitting unit transmits, to the network node, identification information for identifying whether each of the transmitted measurement results is a measured value or an inferred value. A network node for use in a mobile communication system, comprising:
A receiving unit receives from the 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;
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.