CN-122029874-A - Wireless communication method and wireless communication equipment

Abstract

The disclosure provides a wireless communication method, which comprises the steps that user equipment 120 sends radio link failure RLF prediction information predicted based on an artificial intelligence/machine learning AI/ML model to a base station, wherein the RLF prediction information comprises at least one of a model ID, an RLF type, an RLF time, prediction accuracy, an identification ID corresponding to the predicted RLF and a predicted serving cell radio resource management RRM measurement result before the RLF occurs, so that on one hand, the probability of successful handover is improved, on the other hand, the base station better pre-judges the radio link failure of the user equipment 120, the RLF is avoided, and the user experience is improved.

Inventors

• CHEN ZHE

Assignees

• 深圳TCL新技术有限公司

Dates

Publication Date

20260512

Application Date

20240307

Claims (20)

1. A method of wireless communication, performed at a user equipment, the method comprising: And transmitting Radio Link Failure (RLF) prediction information predicted based on the artificial intelligence/machine learning (AI/ML) model, wherein the RLF prediction information comprises at least one of a model ID, an RLF type, an RLF time, a prediction accuracy, an identification ID corresponding to the predicted RLF and a serving cell Radio Resource Management (RRM) measurement result predicted before the RLF occurs.
2. The method of claim 1, wherein the model ID is used to identify an AI/ML model used to obtain the RLF prediction information, the RLF type refers to a predicted RLF type, the RLF time refers to how long RLF is predicted, the accuracy of the prediction refers to the accuracy of a prediction result, and an identification ID corresponding to the predicted RLF is used to identify the RLF prediction information obtained by a specific prediction.
3. The method according to claim 1 or2, wherein the method further comprises: Configuration information is received.
4. The method of claim 3, wherein the configuration information includes a time interval and/or measurement results of a plurality of cells reported by the user equipment, and the RLF prediction information further includes a predicted neighbor RRM measurement result, where the predicted neighbor RRM measurement result refers to RRM of a neighbor at a current time.
5. The method of claim 3, wherein the configuration information comprises a time interval and/or RLF prediction information is reported when a measurement event is satisfied, wherein the measurement event is used to trigger transmission of the RLF prediction information, wherein the measurement event comprises at least one of an A1 event, an A2 event, an A3 event, an A4 event, an A5 event, an A6 event, a B1 event, and a B2 event.
6. The method of claim 5, wherein the RLF prediction information further comprises a current time RRM measurement.
7. A method according to claim 3, wherein the configuration information comprises a time interval and/or N cells indicating reporting, where N is greater than or equal to 1 and N are the N cells with the best signal quality.
8. The method of claim 7, wherein the RLF prediction information further comprises RRM measurements for N best candidate neighbors before a radio link failure occurs.
9. The method of any one of claims 1-8, wherein the method further comprises: and reporting auxiliary information, wherein the auxiliary information comprises a constant list and a timer list suggested by user equipment, and the constant list and the timer list are determined based on an AI/ML model.
10. The method of claim 9, wherein the timer list comprises one or more of T310 and T311, and the constant list comprises one or more of N310 and N311.
11. The method of any one of claims 1-10, wherein the method further comprises: And reporting capability information, wherein the capability information is used for representing the capability of the user equipment for supporting the prediction of RLF based on the AI/ML model.
12. The method of claim 11, wherein the capability information comprises at least one of node information describing that the capability of predicting RLF only supports prediction capability for a specific node, resource information describing that the capability of predicting RLF only supports prediction capability for a specific resource, and AI/ML model information, which is model ID, the node comprising at least one of a cell, a transmission reception point, a tracking area, a scene, and an area.
13. The method of any of claims 1-12, wherein the method further comprises reporting a report of performance monitoring of the RLF prediction information, wherein the report of performance monitoring of the RLF prediction information comprises at least one of a service outage time, a time from a predicted RLF to an occurrence of the RLF, a RLF cause, an identification ID corresponding to the predicted RLF, and information that no RLF has occurred, the service outage time being a time from the occurrence of the RLF to a successful RRC reestablishment.
14. The method of claim 13, wherein the RLF cause comprises at least one of a listen before talk failure, a number of out-of-sync conditions, an RLC reaching a maximum number of retransmissions.
15. The method of any of claims 13-14, wherein the method further comprises reporting a report of performance monitoring of the RLF prediction information based on a first trigger condition, wherein the first trigger condition is successful re-establishment to the first base station or the second base station after the RLF occurs by the user equipment.
16. The method of claim 15, wherein the first base station is a base station to which the user equipment was connected before RLF occurred, the configuration information further comprising a first timer, the method further comprising starting the first timer.
17. The method of claim 16, wherein the method further comprises reporting a report of performance monitoring of the RLF prediction information if RLF occurs before the first timer expires.
18. The method of claim 15, wherein the second base station is a neighbor base station corresponding to a user equipment before RLF occurs.
19. The method of claim 18, wherein the reporting of performance monitoring of the RLF prediction information further comprises RRM measurements when RLF occurs.
20. The method of any of claims 13-14, wherein the method further comprises reporting a report of performance monitoring of the RLF prediction information based on a second trigger condition, wherein the second trigger condition is that no handover occurs for the user equipment and RLF is avoided or that the user equipment is switched and RLF is avoided by the handover.

Description

Wireless communication method and wireless communication equipment Technical Field The present disclosure relates to the field of wireless communications, and in particular, to a wireless communication method and a wireless communication device. Background In the prior art, artificial intelligence/machine learning (ARTIFICIAL INTELLIGENCE/MACHINE LEARNING, AI/ML) is a system that can implement an alternative human work by computational learning. AI/ML can be used to solve various problems such as natural human language processing, computing, graphics processing, etc. In recent years, AI/ML has been applied in the field of communications. However, AI/ML applications have a pending problem in radio resource allocation and mobility management enhancements in the communication field. Therefore, there is a need to propose a method of wireless communication and a wireless communication device, so as to improve the prior art. Disclosure of Invention The technical problem to be solved by the invention is to provide a wireless communication method aiming at the defects in the prior art, and aims to solve the problems in the prior art. According to one aspect of the present disclosure, there is provided a method of wireless communication, performed at a user equipment 120, the method comprising: And transmitting Radio Link Failure (RLF) prediction information predicted based on the artificial intelligence/machine learning (AI/ML) model, wherein the RLF prediction information comprises at least one of a model ID, an RLF type, an RLF time, a prediction accuracy, an identification ID corresponding to the predicted RLF and a serving cell Radio Resource Management (RRM) measurement result predicted before the RLF occurs. According to one aspect of the present disclosure, there is provided a method of wireless communication, performed at a first base station, the method comprising: Radio link failure, RLF, prediction information based on artificial intelligence/machine learning, AI/ML, model prediction is received, the RLF prediction information including at least one of a model ID, an RLF type, an RLF time, accuracy of the prediction, an identification ID corresponding to the predicted RLF, and a serving cell radio resource management, RRM, measurement result predicted before the RLF occurred. According to one aspect of the present disclosure, there is provided a wireless communication device comprising a processor and a memory for storing a computer program, the processor being adapted to invoke and run the computer program stored in the memory to perform the steps of the method of data processing as claimed in any of the preceding claims. The method and the device have the beneficial effects that the user equipment 120 sends the radio link failure RLF prediction information based on the artificial intelligence/machine learning AI/ML model prediction to the base station, so that on one hand, the probability of successful switching is improved, on the other hand, the base station better pre-judges the radio link failure of the user equipment 120, the RLF is avoided, and the user experience is improved. Drawings In order to more clearly illustrate the embodiments of the present disclosure or related art, the following drawings will be briefly described in the embodiments. It is evident that the drawings are only some embodiments of the present disclosure from which one of ordinary skill in the art could obtain other drawings without undue effort. Fig. 1 illustrates a schematic diagram of a framework of a wireless communication system provided by the present disclosure. Fig. 2 illustrates a schematic diagram of a user equipment provided by the present disclosure being covered by a plurality of cells. Fig. 3 illustrates one of the flowcharts of the method of wireless communication provided by the present disclosure. Fig. 4 illustrates a second flowchart of a method of wireless communication provided by the present disclosure. Fig. 5 illustrates one of signaling interactions of a method of wireless communication provided by the present disclosure. Fig. 6 illustrates a second signaling interaction diagram of a method of wireless communication provided by the present disclosure. Fig. 7 illustrates a third signaling interaction diagram of a method of wireless communication provided by the present disclosure. Fig. 8 illustrates a fourth signaling interaction diagram of a method of wireless communication provided by the present disclosure. Fig. 9 illustrates a fifth signaling interaction diagram of a method of wireless communication provided by the present disclosure. Fig. 10 illustrates one of the flowcharts of performance reporting for a radio link failure prediction provided by the present disclosure. Fig. 11 illustrates a second flowchart of performance reporting for another radio link failure prediction provided by the present disclosure. Fig. 12 illustrates a third flowchart of performance reporting for another radio link fa