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CN-122028097-A - Communication method and communication device

CN122028097ACN 122028097 ACN122028097 ACN 122028097ACN-122028097-A

Abstract

The application provides a communication method and a communication device, which can be applied to the technical field of wireless communication, wherein the method comprises the steps that when a measurement result based on a first beam set meets an event for indicating performance monitoring of an artificial intelligent AI model, a first message is sent to network equipment, the first message is used for requesting the performance monitoring of the AI model, and/or the first message is used for indicating that the event is met, after the network equipment receives the first message, a second message is sent to the terminal equipment, and the second message indicates the terminal equipment to monitor the performance of the AI model. By implementing the application, the network equipment can instruct the terminal equipment to monitor the performance of the AI model after receiving the first message, so that the terminal equipment does not need to monitor the performance of the AI model in a periodic monitoring mode, and signaling and resource expenditure caused by frequent interaction between the terminal equipment and the network equipment can be effectively reduced.

Inventors

  • GUO DELIN

Assignees

  • 荣耀终端股份有限公司

Dates

Publication Date
20260512
Application Date
20241108

Claims (16)

  1. 1. A method of communication, the method comprising: Transmitting a first message to a network device based on a measurement result of a first beam set meeting an event for indicating performance monitoring of an artificial intelligence AI model, the first message for requesting performance monitoring of the AI model, and/or the first message for indicating that the measurement result of the first beam set meets the event, the AI model for predicting a measurement result of a third beam set based on a measurement result of a second beam set, the second beam set being the same as or different from the third beam set, the first beam set comprising K predicted beams in the third beam set, the K predicted beams dynamically varying with the measurement result of the second beam set, K being a positive integer; and receiving a second message sent by the network equipment, wherein the second message is used for indicating to monitor the performance of the AI model.
  2. 2. The method of claim 1, wherein the measurements of the second set of beams comprise measured values of signal quality for beams in the second set of beams; the predicted measurement of the third set of beams comprises a predicted value of the signal quality of the beams in the third set of beams and/or a predicted probability that the beams in the third set of beams are optimal beams.
  3. 3. The method according to claim 1 or 2, wherein the K predicted beams comprise K large beams before a predicted value of signal quality in the third set of beams or the K predicted beams comprise K large beams before a predicted probability of an optimal beam in the third set of beams.
  4. 4. A method according to any of claims 1-3, characterized in that the measurement results of the first set of beams comprise measured values of the signal quality of the K predicted beams; The measurement result based on the first beam set satisfies an event for indicating performance monitoring of the artificial intelligence AI model, and the method comprises the steps of sending a first message to a network device, wherein the first message comprises: And sending the first message to the network equipment based on the maximum measured value of the signal quality of the K predicted beams meets an event for indicating performance monitoring of an artificial intelligence AI model.
  5. 5. The method of claim 4, wherein the event is that M1 maximum measured values of signal quality measured M1 consecutive times for the K predicted beams are each less than a first threshold, M1 being a positive integer.
  6. 6. The method of claim 4, wherein the event is the first ratio being greater than a second threshold; The first proportion is a proportion occupied by a largest measured value smaller than a third threshold value in M2 largest measured values of signal quality obtained by measuring the K predicted beams for M2 times continuously, and M2 is a positive integer.
  7. 7. A method according to claim 2 or 3, characterized in that the measurement results of the first set of beams comprise measured values of the signal quality of the K predicted beams; The measurement result based on the first beam set satisfies an event for indicating performance monitoring of the artificial intelligence AI model, and the method comprises the steps of sending a first message to a network device, wherein the first message comprises: Determining, based on a first beam of the K predicted beams and a second beam of the K predicted beams, an event that satisfies a performance monitoring requirement for indicating an artificial intelligence AI model, where the second beam is a beam with a maximum measured value of signal quality among the K predicted beams, the first beam is a beam with a maximum predicted value of signal quality in the third beam set, or the first beam is a beam with a maximum predicted probability of an optimal beam in the third beam set; and sending the first message to the network equipment.
  8. 8. The method of claim 7, wherein the event is that the second ratio is less than a fourth threshold; the second proportion is a proportion occupied by the same event as the first wave beam and the second wave beam which are determined for M3 times continuously, and M3 is a positive integer.
  9. 9. The method of claim 7, wherein the event is M4 consecutive determinations of M4 first prediction errors having an average greater than a fifth threshold; Wherein M4 is a positive integer, and the first prediction error is determined based on an actual measurement of the signal quality of the first beam and an actual measurement of the signal quality of the second beam.
  10. 10. A method according to claim 2 or 3, characterized in that the measurement results of the first set of beams comprise measured values of the signal quality of the beams in the first set of beams; the event that the measurement result based on the first beam set meets the performance monitoring of the artificial intelligence AI model, sends a first message to the network device, and includes: determining a second prediction error based on the measured values and predicted values of the signal quality of the K predicted beams; and sending the first message to the network equipment based on the event that the second prediction error meets the performance monitoring of the AI model.
  11. 11. The method of claim 10, wherein the average of M5 of the second prediction errors determined for M5 consecutive times of the event is greater than a sixth threshold, M5 being a positive integer.
  12. 12. A method of communication, the method comprising: Receiving a first message sent by a terminal device, where the first message is used for requesting to monitor performance of an AI model, and/or the first message is used for indicating that a measurement result of a first beam set meets an event used for indicating performance monitoring of the AI model, the AI model is used for predicting a measurement result of a third beam set based on a measurement result of a second beam set, the second beam set is the same as or different from the third beam set, the first beam set includes K predicted beams in the third beam set, the K predicted beams dynamically change along with the measurement result of the second beam set, and K is a positive integer; and sending a second message to the terminal equipment, wherein the second message is used for indicating to monitor the performance of the AI model.
  13. 13. A communications device comprising one or more processors, one or more memories, wherein the one or more memories are coupled to the one or more processors, the one or more memories to store computer instructions that, when executed by the one or more processors, cause the communications device to perform the method of any of claims 1-11 or 12.
  14. 14. A chip system comprising at least one processor and an interface for receiving computer instructions and transmitting to the at least one processor, the at least one processor executing the computer instructions to cause a communication device to perform the method of any one of claims 1-11 or 12.
  15. 15. A computer readable storage medium having stored therein computer instructions which, when executed by a processor, cause a communication device to perform the method of any of claims 1-11 or 12.
  16. 16. A computer program product comprising computer instructions which, when executed by a processor, cause a communication device to perform the method of any of claims 1-11 or 12.

Description

Communication method and communication device Technical Field The present application relates to the field of wireless communications technologies, and in particular, to a communication method and a communication device. Background The communication network may employ artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) techniques to reduce the resource overhead of beam management, thereby enhancing the user experience. For example, under the conventional beam management framework, the base station needs to configure the terminal to measure a large number of beams, however, in the AI-assisted beam management, the base station may configure the terminal to measure a smaller number of beams, and the terminal inputs the AI model after measuring the measurement results of the beams, so as to predict the measurement results of a larger number of beams. Currently, the AI model is trained based on statistics collected at a particular time, place, and/or network, etc. collection scenario. Since the generalization capability of the AI model is limited, when the application scene of the AI model differs greatly from the collection scene of the AI model, the performance of the AI model may be reduced. In order to ensure the effect of AI auxiliary beam management, the base station can configure the terminal to monitor the performance of the AI model, and one common performance monitoring mode is to configure the terminal to monitor periodically. However, when the configured monitoring period is small, frequent interaction between the base station and the terminal is required, resulting in a large signaling and resource overhead. Disclosure of Invention The embodiment of the application provides a communication method and a communication device, which can effectively reduce signaling and resource expenditure. In a first aspect, the embodiment of the application provides a communication method, which is applied to a terminal device, or a chip in the terminal device, or a device used in combination with the terminal device, or a device used for realizing functions of the terminal device, and the like, and the communication method comprises the steps of sending a first message to a network device, wherein the first message is used for requesting to monitor the performance of an AI model, and/or the first message is used for indicating that the measurement result of the first beam set meets the event, the AI model is used for predicting the measurement result of a third beam set based on the measurement result of a second beam set, the second beam set is the same as or different from the third beam set, the first beam set comprises K predicted beams in the third beam set, the K predicted beams dynamically change along with the measurement result of the second beam set, K is a positive integer, and receiving a second message sent by the network device, and the second message is used for indicating to monitor the performance of the AI model. In the method described in the first aspect, when the measurement result based on the first beam set meets an event for indicating performance monitoring of the artificial intelligent AI model, the terminal device sends a first message to the network device, and the network device sends a second message to the terminal device after receiving the first message, thereby triggering the terminal device to monitor the performance of the AI model. Therefore, the terminal equipment does not need to monitor the performance of the AI model in a periodic monitoring mode, and signaling and resource expenditure caused by frequent interaction between the terminal equipment and the network equipment can be effectively reduced. In a possible implementation, the measurement results of the second set of beams comprise actual measurement values of the signal quality of the beams in the second set of beams, and the measurement results of the predicted third set of beams comprise predicted values of the signal quality of the beams in the third set of beams, and/or the predicted probability that the beams in the third set of beams are optimal beams. In one possible implementation, the K predicted beams include K large beams before the predicted value of the signal quality in the third set of beams, or the K predicted beams include K large beams before the predicted probability of the optimal beam in the third set of beams. In one possible implementation, the measurement result of the first beam set includes actual measurement values of signal quality of K predicted beams, the measurement result based on the first beam set satisfies an event for indicating performance monitoring of the artificial intelligence AI model, and the method for sending the first message to the network device specifically includes sending the first message to the network device based on the maximum actual measurement values of signal quality of K predicted beams satisfies the event for indicating performance monitoring of the artific