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WO2025211432A1 - Procédé de communication, équipement d'utilisateur et nœud de réseau - Google Patents

Procédé de communication, équipement d'utilisateur et nœud de réseau

Info

Publication number
WO2025211432A1
WO2025211432A1 PCT/JP2025/013698 JP2025013698W WO2025211432A1 WO 2025211432 A1 WO2025211432 A1 WO 2025211432A1 JP 2025013698 W JP2025013698 W JP 2025013698W WO 2025211432 A1 WO2025211432 A1 WO 2025211432A1
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WO
WIPO (PCT)
Prior art keywords
connection failure
communication method
failure prediction
model
message
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/JP2025/013698
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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 WO2025211432A1 publication Critical patent/WO2025211432A1/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
    • H04W24/04—Arrangements for maintaining operational condition
    • 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
    • H04W88/02—Terminal devices

Definitions

  • This disclosure relates to a communication method, user equipment, and network node used in a mobile communication system.
  • the communication method is a communication method executed by a user device in a mobile communication system, and includes: performing connection failure prediction, which predicts whether a wireless connection failure will occur in the future, using an artificial intelligence or machine learning (AI/ML) model in a radio resource control (RRC) connected state in which the user device is connected to a network node; and transmitting a message regarding the result of the connection failure prediction to the network node.
  • connection failure prediction which predicts whether a wireless connection failure will occur in the future, using an artificial intelligence or machine learning (AI/ML) model in a radio resource control (RRC) connected state in which the user device is connected to a network node.
  • RRC radio resource control
  • a user device is a user device used in a mobile communication system, and includes: a control unit that, in a radio resource control (RRC) connected state in which the user device is connected to a network node, performs connection failure prediction using an artificial intelligence or machine learning (AI/ML) model to predict whether a wireless connection failure will occur in the future; and a transmission unit that transmits a message regarding the result of the connection failure prediction to the network node.
  • RRC radio resource control
  • AI/ML artificial intelligence or machine learning
  • the network node is a network node used in a mobile communication system, and includes a receiving unit that receives a message regarding the result of a connection failure 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 connection failure prediction is a process of predicting whether a wireless connection failure will occur in the future.
  • FIG. 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.
  • 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. 10 is a diagram showing an operation sequence according to the embodiment.
  • AI/ML technology could be applied to predict wireless connection failures such as handover failures or radio link failures (also known as "HOF/RLF").
  • HAF/RLF radio link failures
  • a specific mechanism for applying AI/ML technology to mobility control for user devices 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
  • 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 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 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).
  • 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 Radio Resource Control
  • NAS Non-Access Stratum
  • 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 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.
  • 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.
  • 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
  • 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 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.
  • 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.
  • Radio link monitoring examples include RLF and HOF, which are detected based on radio link monitoring (RLM).
  • the UE 100 in the RRC connected state performs radio link monitoring (RLM) in an active BWP (Bandwidth Part) based on a reference signal (RS) and a signal quality threshold set by the network 5.
  • the reference signal may be an SSB (Synchronization Signal and PBCH block) or a CSI (Channel state information)-RS.
  • UE100 basically declares (detects) RLF in response to the expiration of a radio problem timer that is started after notification of a radio problem from the physical layer (if the radio problem is resolved before the timer expires, UE100 stops the timer).
  • UE100 detects RLF for the serving cell, it remains in the RRC connected state, selects an appropriate cell, and initiates RRC re-establishment. If UE100 does not find an appropriate cell within a certain period of time after RLF detection, it transitions to the RRC idle state.
  • the UE 100 makes the RLF report available to the network 5.
  • the UE 100 stores the latest RLF report until the RLF report is obtained by the network 5 or for a certain period of time after the radio connection failure is detected.
  • the UE 100 indicates the availability of the RLF report to the network 5 and provides the RLF report in response to a request from the network 5.
  • HOF handover failure
  • RLF occurs after UE 100 has been in the source cell for a long period of time.
  • UE 100 attempts to re-establish a radio link connection in another cell.
  • RLF occurs immediately after a successful handover from the source cell to the target cell, or if the handover fails during the handover procedure.
  • the UE 100 attempts to re-establish the radio link connection with the source cell.
  • RLF occurs immediately after a successful handover from the source cell to the target cell, or if the handover fails during the handover procedure.
  • the UE 100 attempts to re-establish a radio link connection with a cell other than the source cell or target cell.
  • a mobile communication system 1 applies 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.
  • 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 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.
  • the AI/ML technology is applied to mobility control of the UE 100. Specifically, the AI/ML technology is applied to predict the occurrence of a wireless connection failure.
  • the UE 100 has an AI/ML model for predicting (specifically, predicting using model inference) whether a wireless connection failure will occur in the future. That is, in the embodiment, a UE-side model is used.
  • a “radio connection failure” is a failure in a radio connection that is detected by monitoring a monitoring target (e.g., RLM).
  • a “monitoring target” is a cell, beam, or reference signal.
  • Radio connection failures detected for a cell include RLF and HOF.
  • a radio connection failure detected for a beam is a beam failure.
  • RLF and HOF are primarily considered as radio connection failures, but beam failure may also be included in radio connection failures.
  • “A radio connection failure will occur in the future” may mean that a radio connection failure will not occur in the current radio environment, but will occur in the near future.
  • Figure 8 is a diagram showing an example of an operation scenario of the mobile communication system 1 according to the embodiment.
  • UE100 is in an RRC connected state with cell a of gNB200 as the serving cell.
  • the neighboring cells may be managed by the same gNB200 as the serving cell.
  • the neighboring cells may be managed by a gNB200 different from the serving cell.
  • the frequencies of each cell may be the same. The frequencies may be different.
  • gNB200 transmits configuration information for setting connection failure prediction (hereinafter also referred to as "failure prediction configuration") to UE100.
  • gNB200 may also transmit measurement configuration information to UE100.
  • UE100 receives the configuration information.
  • the measurement configuration may include a measurement object (and its ID), a reporting configuration (and its ID), and a measurement ID.
  • step S3 UE 100 uses AI/ML model 101 to perform connection failure prediction, based on the failure prediction setting in step S1, to predict whether a wireless connection failure will occur in the future.
  • AI/ML model 101 outputs a prediction result regarding when a wireless connection failure will occur based on the connection failure prediction, as a connection failure prediction result.
  • the prediction result may include a value indicating the probability (likelihood) that HOF/RLF will occur in the future, i.e., a value indicating the prediction accuracy.
  • the AI/ML model 101 possessed by the UE 100 is assumed to have learned the correlation between these parameters through model learning.
  • the AI/ML model 101 may have learned the causal relationship between data such as time-series wireless quality fluctuations for each cell (actual measured values), UE 100's location data (actual measured values), UE 100's moving speed (actual measured values), and cell frequency (set value), and the HOF/RLF occurrence history for each cell (and the actual measured values of wireless quality at that time) through model learning.
  • an at least partially trained AI/ML model 101 may be set to the UE 100 by the gNB 200.
  • the AI/ML model 101 may be transferred from the gNB 200 to the UE 100 when communication between the gNB 200 and the UE 100 begins.
  • the UE 100 may generate the trained AI/ML model 101 by performing model learning in various environments.
  • step S4 UE100 transmits a message regarding the result of the connection failure prediction to gNB200.
  • gNB200 receives the message.
  • UE100 transmits a message to gNB200 regarding the results of connection failure prediction using AI/ML model 101.
  • This allows gNB200 to understand the possibility of a wireless connection failure occurring at UE100. Therefore, it becomes possible to perform mobility control (e.g., RRC reconfiguration, etc.) on UE100 that prevents wireless connection failures from occurring.
  • mobility control e.g., RRC reconfiguration, etc.
  • Connection failure prediction is a process of predicting whether a wireless connection failure will occur in the future.
  • the message indicates that a wireless connection failure is predicted to occur in the future.
  • the gNB 200 may further include a transmission unit 210 that transmits the failure prediction configuration to the UE 100.
  • the failure prediction setting in step S1 may include a setting that specifies a future time range for which the connection failure prediction will predict the occurrence of a wireless connection failure.
  • the gNB 200 may configure the UE 100 as to how far in advance in time the future connection failure prediction will be performed.
  • the UE100 may use AI/ML model 101 to obtain a value indicating the probability that a wireless connection failure will occur in the future.
  • the failure prediction setting in step S1 may include a setting that specifies a threshold value for the probability that a wireless connection failure must be met in order for the connection failure prediction to consider that a wireless connection failure will occur in the future.
  • the setting may include a threshold value for the probability (likelihood) of HOF/RLF occurrence.
  • the failure prediction settings in step S1 may include a setting that specifies any of 1) HOF only, 2) RLF only, or 3) both HOF and RLF as the type of failure predicted to occur by connection failure prediction.
  • the failure prediction settings in step S1 may include a setting that individually specifies any of 1) HOF, 2) RLF, or 3) beam failure as the type of failure predicted to occur by connection failure prediction.
  • the failure prediction settings in step S1 may include a setting that specifies a combination of two or more of 1) HOF, 2) RLF, or 3) beam failure as the type of failure predicted to occur by connection failure prediction.
  • the failure prediction setting in step S1 may include a setting that specifies the target cell for which a wireless connection failure is predicted to occur by the connection failure prediction.
  • the setting may include information that specifies which cell a HOF/RLF is predicted for.
  • the failure prediction settings in step S1 include settings that specify the AI/ML model 101 to be used for connection failure prediction.
  • the settings may include the model ID of the AI/ML model 101 to be used for connection failure prediction.
  • the message of step S4 may include information indicating the predicted timing at which a wireless connection failure is expected to occur in the future. For example, UE100 reports to gNB200 information on how far in the future HOF/RLF will occur.
  • the message of step S4 may include proposal information indicating mobility control for avoiding the occurrence of radio connection failure.
  • UE100 may notify gNB200 of proposal information regarding HOF/RLF avoidance measures.
  • UE100 may measure the reception quality of the serving cell and/or neighboring cells.
  • the message in step S4 may include information indicating the results of the measurement. For example, UE100 may report the current quality measurement value (latest measurement value) to gNB200 along with the report of the connection failure prediction result.
  • the message of step S4 may include the model ID of the AI/ML model 101 used to predict the connection failure.
  • Figure 9 is a diagram showing the operation sequence related to the embodiment.
  • step S101 UE100 is in an RRC connected state with gNB200.
  • step S102 gNB200 transmits a message including a failure prediction setting to UE100.
  • the message may be an RRC reconfiguration message.
  • UE100 receives the message (failure prediction setting).
  • the failure prediction setting may be time information for predicting a connection failure, for example, information about how far in advance a HOF/RLF is to be predicted (50 ms before, 1 s before, etc.).
  • the failure prediction setting may also be information about how far in the future a HOF/RLF will occur (50 ms later, 1 s later, etc.).
  • Failure prediction settings may also include at least one of the following: information specifying which (or both) of HOF and RLF to predict; information about the cell (cell ID, etc.) that is the target of connection failure prediction; a threshold for the likelihood (probability) of inference for connection failure prediction; information specifying the model ID of the AI/ML model 101 used for model inference (connection failure prediction); and information about whether to report the model ID of the AI/ML model 101 used for model inference.
  • the failure prediction settings may also include information specifying the beam and/or reference signal that is the target of connection failure prediction.
  • step S103 UE100 monitors (including measures) the monitoring target that is the target for predicting wireless connection failure.
  • UE100 performs connection failure prediction using model inference in accordance with the failure prediction settings. For example, UE100 performs model inference (connection failure prediction) of HOF/RLF occurrence for each cell using data such as time-series wireless quality fluctuations for each cell (actual measured values), UE100's location data (actual measured values), UE100's movement speed (actual measured values), and cell frequency (set value) as inference data.
  • model inference connection failure prediction
  • UE100 performs model inference (connection failure prediction) of HOF/RLF occurrence for each cell using data such as time-series wireless quality fluctuations for each cell (actual measured values), UE100's location data (actual measured values), UE100's movement speed (actual measured values), and cell frequency (set value) as inference data.
  • UE100 If the reporting conditions for reporting the connection failure prediction result to gNB200 are not met, UE100 returns the process to step S103. On the other hand, if the reporting conditions are met, UE100 proceeds to step S106.
  • the report may be a periodic report.
  • connection failure prediction inference processing
  • This may be when the AI/ML model 101 outputs the inference result.
  • Timing obtained by subtracting a set advance (or future) time from the predicted time of HOF/RLF occurrence has arrived: For example, if it is predicted that HOF/RLF will occur in 5 seconds and the setting is set to predict it 3 seconds in advance, UE100 reports it 3 seconds before (or 2 seconds after the present).
  • UE100 transmits a message (report) regarding the result of the connection failure prediction.
  • gNB200 receives the message (report).
  • the message may be a MAC CE or an RRC message.
  • the RRC message may be a UE Assistance Information message.
  • the message may include at least one of the following information:
  • HOF/RLF type (information on which type occurs): It may be HOF only, RLF only, or both.
  • HOF/RLF occurrence timing information It may also be information on the timing (50 ms later, 1 second later, etc.) at which the occurrence of HOF/RLF is predicted.
  • the information may be 50% after 1 second, 80% after 2 seconds, and so on.
  • the information may be information on which cell to handover to (or change of primary secondary cell (PS cell)) and/or information on which cell the user should be located in.
  • PS cell primary secondary cell
  • Model ID of the model used for inference For example, the gNB200 may determine the likelihood (accuracy) of the connection failure prediction (model inference) by obtaining model performance information from a model database based on the model ID.
  • the gNB200 may identify the model by the model ID.
  • handover has been described as an example of mobility control, but the present invention is not limited to handover and can be applied to any mobility control.
  • the operation according to the above-described embodiment may be applied to setting a handover execution condition in a conditional handover.
  • the operation according to the above-described embodiment may be applied to LTM (L1/L2 Triggered Mobility), which is cell switching initiated by Layer 1 and/or Layer 2 (L1/L2).
  • LTM L1/L2 Triggered Mobility
  • the above-described measurement report may be read as an L1 measurement report.
  • the present invention may be applied to PSCell change, which switches the primary/secondary cell (PSCell) of the UE 100 initiated by the RRC layer.
  • PSCell primary/secondary cell
  • 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.
  • 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).
  • 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.”
  • 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.
  • ⁇ Appendix 2 The communication method according to claim 1, wherein the radio connection failure is a handover failure (HOF) or a radio link failure (RLF).
  • the radio connection failure is a handover failure (HOF) or a radio link failure (RLF).
  • Appendix 5 the user equipment obtains a value indicating a probability that the wireless connection failure will occur in the future using the AI/ML model;
  • ⁇ Appendix 6 The communication method according to any one of Supplementary Notes 3 to 5, wherein the setting information includes a setting that specifies, as a type of failure predicted by the connection failure prediction, one of HOF only, RLF only, or both HOF and RLF.
  • Appendix 8 The communication method according to any one of Supplementary Notes 3 to 7, wherein the setting information includes a setting that specifies the AI/ML model to be used for the connection failure prediction.
  • Appendix 10 The communication method according to any one of Supplementary Notes 1 to 9, wherein the message includes information indicating a probability of occurrence of the wireless connection failure at each of a plurality of future timings.
  • Appendix 12 The communication method according to claim 11, wherein the proposal information includes information indicating a cell that is recommended as a serving cell for the user equipment.
  • Appendix 13 The method further comprises the user equipment measuring reception quality of a serving cell and/or a neighboring cell; 13.
  • Appendix 14 The communication method according to any one of Supplementary Notes 1 to 13, wherein the message includes a model ID of the AI/ML model used for the connection failure prediction.
  • Appendix 15 A user device for use in a mobile communication system, comprising: a control unit that performs connection failure 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 wireless connection failure will occur in the future; a transmitter configured to transmit a message to the network node regarding a result of the connection failure prediction.
  • a control unit that performs connection failure 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 wireless connection failure will occur in the future
  • RRC radio resource control

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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 est exécuté par un équipement utilisateur dans un système de communication mobile et consiste à : effectuer une prédiction de défaillance de connexion pour prédire si une défaillance de connexion radio aura lieu dans le futur, à l'aide d'un modèle d'intelligence artificielle ou d'apprentissage automatique (IA/ML) dans un état connecté de commande de ressources radio (RRC) dans lequel l'équipement utilisateur est connecté à un nœud de réseau ; et transmettre un message relatif à un résultat de la prédiction de défaillance de connexion au nœud de réseau.
PCT/JP2025/013698 2024-04-04 2025-04-04 Procédé de communication, équipement d'utilisateur et nœud de réseau Pending WO2025211432A1 (fr)

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JP2024-060852 2024-04-04
JP2024060852 2024-04-04

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WO2025211432A1 true WO2025211432A1 (fr) 2025-10-09

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