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WO2025211433A1 - Procédé de communication, dispositif utilisateur et nœud de réseau - Google Patents

Procédé de communication, dispositif utilisateur et nœud de réseau

Info

Publication number
WO2025211433A1
WO2025211433A1 PCT/JP2025/013699 JP2025013699W WO2025211433A1 WO 2025211433 A1 WO2025211433 A1 WO 2025211433A1 JP 2025013699 W JP2025013699 W JP 2025013699W WO 2025211433 A1 WO2025211433 A1 WO 2025211433A1
Authority
WO
WIPO (PCT)
Prior art keywords
event
measurement report
measurement
future
occur
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/JP2025/013699
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English (en)
Japanese (ja)
Inventor
真人 藤代
光孝 秦
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Kyocera Corp
Original Assignee
Kyocera Corp
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Filing date
Publication date
Application filed by Kyocera Corp filed Critical Kyocera Corp
Publication of WO2025211433A1 publication Critical patent/WO2025211433A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
    • H04W88/08—Access point devices

Definitions

  • This disclosure relates to a communication method, user equipment, and network node used in a mobile communication system.
  • 3GPP Third Generation Partnership Project
  • AI/ML artificial intelligence or machine learning
  • a communication method is a communication method executed by a user equipment in a mobile communication system, and includes: performing, in a radio resource control (RRC) connected state in which the user equipment is connected to a network node, event prediction using an artificial intelligence or machine learning (AI/ML) model to predict whether a measurement report event that will trigger transmission of a measurement report to the network node will occur in the future; and, in response to the user equipment predicting that the measurement report event will occur in the future, transmitting a message regarding the result of the event prediction to the network node.
  • RRC radio resource control
  • AI/ML artificial intelligence or machine learning
  • a network node is a network node used in a mobile communication system, and includes a receiving unit that receives a message relating to the result of an event prediction using an artificial intelligence or machine learning (AI/ML) model from a user equipment in a radio resource control (RRC) connected state connected to the network node, and a control unit that performs mobility control for the user equipment based on the message.
  • the event prediction is a process of predicting whether a measurement report event that will trigger the transmission of a measurement report to the network node will occur in the future.
  • the message indicates that the measurement report event is predicted to occur in the future.
  • FIG. 2 is a diagram illustrating an example of an operation scenario of the mobile communication system according to the embodiment.
  • FIG. 10 is a diagram showing another example of an operation scenario of the mobile communication system according to the embodiment.
  • FIG. 10 is a diagram illustrating a specific example of an operation according to the embodiment.
  • FIG. 10 is a diagram for explaining an operation according to a modified example.
  • 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.
  • AI/ML technology 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.
  • 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.
  • FIG. 1 is a diagram showing an example of the configuration of a mobile communication system 1 according to an embodiment.
  • the mobile communication system 1 conforms to the 3GPP standard 5th Generation System (5GS). While the following description uses 5GS as an example, the mobile communication system may also be at least partially based on an LTE (Long Term Evolution) system. The mobile communication system may also be at least partially based on a 6th Generation (6G) system.
  • 5GS 3GPP standard 5th Generation System
  • LTE Long Term Evolution
  • 6G 6th Generation
  • 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.
  • RRM radio resource management
  • 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").
  • gNBs can also connect to the EPC (Evolved Packet Core), which is the LTE core network.
  • EPC Evolved Packet Core
  • 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.
  • 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.
  • FIG. 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.
  • PHY physical
  • MAC medium access control
  • RLC radio link control
  • PDCP packet data convergence protocol
  • SDAP service data adaptation protocol
  • 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).
  • DCI downlink control information
  • 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.
  • RNTI radio network temporary identifier
  • the DCI transmitted from gNB200 has CRC (Cyclic Redundancy Code) parity bits scrambled by the RNTI added.
  • 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).
  • 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.
  • RRC connection connection between the RRC of UE100 and the RRC of gNB200
  • UE100 is in an RRC connected state.
  • RRC connection no connection between the RRC of UE100 and the RRC of gNB200
  • UE100 is in an RRC idle state.
  • 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.
  • UE100 also has an application layer, etc.
  • the layer below the NAS layer is called the AS layer (also simply referred to as "AS").
  • measurement targets also called “measurement objects”
  • a measurement object is, for example, a monitored frequency (carrier frequency), but it can also be a cell.
  • measurement objects 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 measurement report event type set in UE100 by the reporting configuration may be, for example, Event A1 (Serving becomes better than threshold), Event A2 (Serving becomes worse than threshold), or Event A3 (Neighborhood becomes amount of offset better than PCell/PSCell).
  • Event A1 is a measurement report event that indicates that the measurement result of the current serving cell has become better than the threshold.
  • Event A2 is a measurement report event that indicates that the measurement result of the current serving cell has become worse than the threshold.
  • Event A3 is a measurement report event that indicates that the measurement result of a neighboring cell has improved beyond the measurement result of the current serving cell (specifically, PCell/PSCell) plus an offset value.
  • the reporting configuration may include configuration of such event types and settings of the thresholds and/or offset values used for those event types.
  • the reporting settings for event-triggered reporting and event-triggered periodic reporting may include a TTT (Time To Trigger) setting.
  • UE100 triggers the transmission of a single or periodic measurement report when the measurement reporting event set for UE100 in the reporting settings remains satisfied for the TTT period.
  • "Occurrence of a measurement reporting event” may mean that the conditions for the measurement reporting event are satisfied and that this state continues for the TTT period.
  • 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.
  • UE100 is configured to consider a subset of the detected beams.
  • 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.
  • L1 filtering which is filtering at the physical layer (PHY, Layer 1 (L1)
  • L3 filtering which is filtering at the RRC layer (Layer 3 (L3)
  • 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 gNB
  • FIG. 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.
  • 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)
  • 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
  • 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).
  • 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.
  • 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.
  • 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.
  • 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 data processing unit A4 receives the inference result data and performs processing that utilizes the inference result data.
  • the AI/ML technique is applied to mobility control of the UE 100. Specifically, when the UE 100 is in an RRC-connected state, the AI/ML technique is applied to handover in which the RRC layer takes the lead in switching the serving cell (primary cell) of the UE 100.
  • UE100 has an AI/ML model for predicting (specifically, predicting using model inference) whether a measurement report event will occur in the future. That is, in this embodiment, a UE-side model is used.
  • a measurement report event will occur in the future may mean that a measurement report event will not occur in the current radio environment, but will occur in the near future.
  • current radio environment may mean the current measurement results for the measurement target.
  • 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.
  • SSB synchronization signal
  • PBCH physical broadcast channel
  • CSI channel state information
  • DM demodulation
  • gNB200 transmits 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.
  • Event-triggered reporting (or event-triggered periodic reporting) may be configured in UE100 by the reporting configuration.
  • Event A1, Event A2, and/or Event A3 may be configured in UE100 as the measurement report event type in the reporting configuration.
  • the measurement configuration may include configuration information for configuring event prediction (also referred to as "event prediction configuration").
  • the configuration information for configuring event prediction may be configured in UE100 in association with the measurement ID.
  • step S2 UE100 performs measurements on measurement targets (cells in this embodiment) 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 and/or each cell specified in the measurement object to obtain measurement results.
  • the "measurement results” may be a set of the ID of the measurement target and its measurement value.
  • the “measurement values” may be reference signal received power (RSRP), reference signal radio quality (RSRQ), and/or signal-to-interference-and-noise ratio (SINR), etc.
  • RSRP reference signal received power
  • RSRQ reference signal radio quality
  • SINR signal-to-interference-and-noise ratio
  • step S3 model inference
  • UE100 uses AI/ML model 101 to predict whether a measurement report event will occur in the future, based at least in part on the measurement results in step S2 (measurement). That is, UE100 uses AI/ML model 101 to perform event prediction, which predicts whether a measurement report event that triggers the transmission of a measurement report to gNB200 will occur in the future.
  • AI/ML model 101 outputs the prediction result of when the measurement report event will occur through event prediction.
  • the prediction result may include a value indicating the probability (likelihood) that the measurement report event will occur in the future, i.e., a value indicating the prediction accuracy.
  • the AI/ML model 101 possessed by UE100 is assumed to have learned the correlation between these parameters through model learning.
  • the AI/ML model 101 has learned the causal relationship between parameters including measurement results for each measurement target and the measurement report event occurrence history for each cell through model learning.
  • at least a partially trained AI/ML model 101 may be set to UE100 by gNB200.
  • the AI/ML model 101 may be transferred from gNB200 to UE100 when communication between gNB200 and UE100 begins.
  • UE100 may generate a trained AI/ML model 101 by performing model learning in various environments.
  • step S4 UE100 transmits a measurement report to gNB200.
  • UE100 transmits the measurement results of each cell to gNB200 via an RRC message (measurement report) at a timing determined according to the reporting setting associated with the measurement object, for example, at the timing when a measurement report event occurs.
  • UE100 transmits a measurement report to gNB200 in response to the prediction that a measurement report event will occur in the future based on the model inference (event prediction) of step S3.
  • the measurement report is an example of a message regarding the result of the event prediction.
  • the measurement report includes a measurement ID associated with the measurement report event predicted to occur in the future.
  • the gNB200 that receives the measurement report performs mobility control for the UE100 based on the measurement report. For example, the gNB200 determines a target cell for handover and performs mobility control to switch the serving cell from the current serving cell (source cell) to the target cell.
  • UE100 in response to the prediction that a measurement report event will occur in the future through the model inference (event prediction) of step S3, triggers the transmission of a measurement report before the measurement report event occurs and/or upon assuming that the measurement report event has occurred.
  • This allows gNB200 to recognize the need for mobility control (e.g., handover) of UE100 at an earlier stage, thereby improving mobility control.
  • UE100 performing such operations has, in an RRC connected state connected to gNB200, a control unit 130 that performs event prediction using AI/ML model 101 to predict whether a measurement report event that will trigger the transmission of a measurement report to gNB200 will occur in the future, and a transmission unit 120 that transmits a message regarding the result of the event prediction to gNB200 in response to the prediction that a measurement report event will occur in the future (see Figure 2).
  • gNB200 has a reception unit 220 that receives a message regarding the result of the event prediction using AI/ML model 101 from UE100 in the RRC connected state, and a control unit 230 that performs mobility control for UE100 based on the message (see Figure 3).
  • gNB200 may further have a transmission unit 210 that transmits configuration information for configuring event prediction to UE100.
  • step S101 UE100 is in an RRC connected state with the cell of gNB200 as the serving cell.
  • gNB200 transmits the measurement configuration and event prediction configuration to UE100.
  • UE100 receives the measurement configuration and event prediction configuration.
  • the measurement configuration and event prediction configuration may be transmitted from gNB200 to UE100 in an RRC message (e.g., an RRC Reconfiguration message).
  • the event prediction configuration may be included in the measurement configuration as part of the measurement configuration (e.g., part of the reporting configuration).
  • the event prediction configuration may be associated with a measurement ID.
  • the event prediction setting may include a setting for specifying a measurement report event type to which the event prediction applies, and the setting may be information specifying which type of measurement report event (e.g., Event A1, Event A2, or Event A3) the prediction corresponds to.
  • the event prediction setting may include a setting for specifying a model ID of the AI/ML model 101 to be used for event prediction.
  • the UE 100 may select the AI/ML model 101 with the specified model ID and perform event prediction using the selected AI/ML model 101.
  • step S103 UE100 performs measurements on each measurement target (each cell) based on the measurement settings in step S102.
  • step S104 UE 100 performs event prediction using AI/ML model 101 based on the measurement results of step S103. If a threshold is set in the event prediction setting, UE 100 may predict that a measurement report event will occur when the probability value output by AI/ML model 101 exceeds the threshold. UE 100 may also predict what type of measurement report event will occur.
  • UE100 may predict whether the state in which the conditions for the measurement report event are met will continue for the time TTT. For example, UE100 infers whether the conditions will be met for the time TTT when the conditions for Event A3 are met and timing of TTT begins (i.e., when the radio environment first meets the conditions). If UE100 predicts that the conditions will be met for the time TTT, it may predict that a measurement report event will occur even if the time TTT has not yet elapsed.
  • step S105 If UE100 does not predict that a measurement report event will occur (step S105: NO), UE100 returns to step S103.
  • step S106 UE100 triggers the transmission of a measurement report before the measurement report event occurs or assumes that a measurement report event has occurred, and transmits the measurement report to gNB200.
  • the measurement report includes a measurement ID associated with the measurement report event that is predicted to occur.
  • the measurement report may include the measurement results of each measurement target measured in step S103.
  • the measurement results may include the measurement results of each measurement target predicted in step S104.
  • step S107 gNB200, which has received the measurement report from UE100, performs mobility control for UE100 based on the received measurement report. For example, gNB200 hands over UE100 to an appropriate cell or changes the primary/secondary cell (PS cell) of UE100.
  • PS cell primary/secondary cell
  • the notification information (message) of step S206 may include the cell ID of the target cell of the measurement report event predicted to occur in the future. That is, the notification information may include a cell ID for identifying which cell the measurement report event is intended to occur for. For example, if a measurement report event of Event A1 (Serving becomes better than threshold) or Event A2 (Serving becomes worse than threshold) is predicted to occur, UE100 may include the cell ID of the current serving cell in the notification information. If a measurement report event of Event A3 (Neighbor becomes amount of offset better than PCell/PSCell) is predicted to occur, UE100 may include the cell ID of the neighboring cell (and the cell ID of PCell/PSCell) in the notification information.
  • the notification information (message) of step S206 may include information indicating the prediction accuracy of the event prediction. This information may be a value indicating the likelihood (probability) that a measurement report event will occur.
  • 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.
  • 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.
  • UE100 may be an MT (Mobile Termination) of an IAB node.
  • 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.
  • IAB-MT other examples of MT include NCR (Network Controlled Repeater)-MT and RIS (Reconfigurable Intelligent Surface)-MT.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • ⁇ Appendix 1 A communication method performed by a user device in a mobile communication system, comprising: In a Radio Resource Control (RRC) Connected state in which the user equipment is connected to a network node, performing event prediction using an artificial intelligence or machine learning (AI/ML) model to predict whether a measurement reporting event that will trigger transmission of a measurement report to the network node will occur in the future; In response to the user equipment predicting that the measurement reporting event will occur in the future, sending a message to the network node regarding a result of the event prediction.
  • RRC Radio Resource Control
  • AI/ML artificial intelligence or machine learning
  • ⁇ Appendix 2 2. The communication method of claim 1, wherein the message is the measurement report including a measurement ID associated with the measurement report event predicted to occur in the future.
  • ⁇ Appendix 3 The communication method according to Supplementary Note 1 or 2, wherein the user equipment triggers transmission of the measurement report before the measurement report event occurs in response to predicting that the measurement report event will occur in the future.
  • ⁇ Appendix 4 The communication method according to Supplementary Note 1 or 2, wherein the user equipment, in response to predicting that the measurement report event will occur in the future, considers the measurement report event to have occurred and triggers transmission of the measurement report.
  • Appendix 5 The communication method according to any one of Supplementary Notes 1 to 4, wherein the user equipment, in response to predicting that the measurement report event will occur in the future, transmits the message to the network node, the message including notification information indicating a result of the event prediction.
  • ⁇ Appendix 6 The communication method according to any one of Supplementary Notes 1 to 5, wherein the message includes information indicating a measurement ID associated with the measurement report event predicted to occur in the future, and/or an event type of the measurement report event predicted to occur in the future.
  • Appendix 7 The communication method according to any one of Supplementary Notes 1 to 6, wherein the message includes a cell ID of a target cell of the measurement report event predicted to occur in the future.
  • Appendix 8 The communication method according to any one of Supplementary Notes 1 to 7, wherein the message includes information indicating prediction accuracy of the event prediction.
  • Appendix 9 The communication method according to any one of Supplementary Notes 1 to 8, wherein the message includes information indicating a timing at which the measurement report event is predicted to occur in the future.
  • Appendix 10 10. The communication method according to any one of Supplementary Notes 1 to 9, wherein the message includes a model ID of the AI/ML model used for the event prediction.
  • Appendix 11 receiving configuration information from the network node for configuring the event prediction; The communication method according to any one of Supplementary Notes 1 to 10, wherein the user device performs the event prediction based on the setting information.
  • Appendix 12 The communication method according to claim 11, wherein the configuration information includes a setting that specifies a future time range for which the event prediction is to predict the occurrence of the measurement report event.
  • Appendix 13 The user equipment obtains a value indicating a probability that the measurement report event will occur in the future using the AI/ML model; 13.
  • Appendix 14 The communication method according to any one of Supplementary Notes 11 to 13, wherein the configuration information includes a setting for specifying a measurement report event type to which the event prediction is to be applied.
  • Appendix 15 The communication method according to any one of appendices 11 to 14, wherein the setting information includes a setting for specifying a model ID of the AI/ML model used for the event prediction.
  • Appendix 16 A user device for use in a mobile communication system, comprising: a control unit that performs event prediction using an artificial intelligence or machine learning (AI/ML) model in a radio resource control (RRC) connected state in which the user equipment is connected to a network node to predict whether a measurement report event that triggers transmission of a measurement report to the network node will occur in the future; a transmitter configured to, in response to predicting that the measurement reporting event will occur in the future, transmit a message to the network node regarding a result of the event prediction.
  • a control unit that performs event prediction using an artificial intelligence or machine learning (AI/ML) model in a radio resource control (RRC) connected state in which the user equipment is connected to a network node to predict whether a measurement report event that triggers transmission of a measurement report to the network node will occur in the future
  • RRC radio resource control
  • a network node for use in a mobile communication system comprising: a receiving unit configured to receive a message relating to a result of an event prediction using an artificial intelligence or machine learning (AI/ML) model from a user equipment in a radio resource control (RRC) connected state connected to the network node; a control unit that performs mobility control for the user equipment based on the message, the event prediction is a process of predicting whether a measurement report event that triggers transmission of a measurement report to the network node will occur in the future; The message indicates to a network node that the measurement reporting event is predicted to occur in the future.
  • AI/ML artificial intelligence or machine learning
  • RRC radio resource control
  • Appendix 18 18.
  • 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

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Ce procédé de communication à exécuter par un dispositif utilisateur dans un système de communication mobile consiste à : réaliser une prédiction d'événement pour prédire si un événement de rapport de mesure déclenchant la transmission d'un rapport de mesure à un nœud de réseau va se produire dans le futur ou non, à l'aide d'un modèle d'intelligence artificielle ou d'apprentissage automatique (IA/ML) dans un état connecté de gestion des ressources radio (RRC) dans lequel le dispositif utilisateur est connecté au nœud de réseau ; et le dispositif utilisateur transmet un message concernant les résultats de prédiction d'événement au nœud de réseau en réponse à une prédiction selon laquelle l'événement de rapport de mesure va se produire dans le futur.
PCT/JP2025/013699 2024-04-04 2025-04-04 Procédé de communication, dispositif utilisateur et nœud de réseau Pending WO2025211433A1 (fr)

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