CN-122028093-A - Prediction processing method and device and readable storage medium

Abstract

The application discloses a prediction processing method, a prediction processing device and a readable storage medium, which relate to the technical field of communication and are used for reducing measurement of signal resources by a terminal. The method comprises the steps of obtaining prediction information, and executing prediction through an artificial intelligence/machine learning AI/ML model according to the prediction information, wherein the prediction information comprises one or more of time domain information, frequency domain information, reference signal information, frequency information and beam information. The embodiment of the application can realize the effect of automatic prediction of the signal resource by the terminal.

Inventors

• MIAO JINHUA
• YAN NAN
• TANG XUN
• WANG DA
• YAN XUE

Assignees

• 大唐移动通信设备有限公司

Dates

Publication Date

20260512

Application Date

20241108

Claims (20)

1. 1. The prediction processing method is characterized by being applied to a terminal and comprising the following steps of: acquiring prediction information, and executing prediction through an artificial intelligence/machine learning AI/ML model according to the prediction information; wherein the prediction information includes one or more of: Time domain information; frequency domain information; reference signal information; Frequency information; Beam information.
2. 2. The method of claim 1, wherein the time domain information comprises one or more of: period information and a first time offset; a second time offset; Bit map information The rate of decrease is measured.
3. 3. The method of claim 1, wherein the prediction information further comprises one or more of: Predicting an object; Predicting an index number of the object; reporting information of the prediction result.
4. 4. The method of claim 1, wherein the prediction information is contained within first information from which prediction is performed, the first information further comprising: Measurement information for indicating information measured by the terminal.
5. 5. The method of claim 4, wherein said performing prediction from said first information comprises: And determining a prediction time according to the measurement information and the time domain information, wherein the time domain information is the period information and the first time offset.
6. 6. The method of claim 4, wherein said performing prediction from said first information comprises: And determining the predicted time according to the measurement information and the second time offset, wherein the second time offset represents the time offset between the first measurement time and the predicted time in the measurement information.
7. 7. The method of claim 4, wherein said performing prediction from said first information comprises: and determining the predicted time according to the second measurement time and the bit mapping information.
8. 8. The method of claim 4, wherein said performing prediction from said first information comprises: and determining the predicted time according to the second measurement time and the measurement reduction rate.
9. 9. The method of claim 1, wherein said performing prediction from said first information comprises: And determining the predicted time according to the second measurement time and the beam information.
10. 10. The method according to any of the claims 7 to 9, characterized in that the second measurement instant is network side configured.
11. 11. The method of claim 1, wherein performing prediction by an artificial intelligence/machine learning AI/ML model comprises: AI prediction is performed by artificial intelligence/machine learning AI/ML model and predictive measurements are obtained.
12. 12. A predictive processing method, applied to a network device, comprising: Transmitting prediction information for performing prediction through an artificial intelligence/machine learning AI/ML model; wherein the prediction information includes one or more of: Time domain information; frequency domain information; reference signal information; Frequency information; Beam information.
13. 13. The method of claim 12, wherein the time domain information comprises one or more of: period information and a first time offset; a second time offset; bit map information; The rate of decrease is measured.
14. 14. The method of claim 12, wherein the prediction information further comprises one or more of: Predicting an object; Predicting an index number of the object; reporting information of the prediction result.
15. 15. The method according to claim 12, wherein the method further comprises: transmitting measurement information, wherein the measurement information is used for indicating information measured by a terminal, and the measurement information comprises the following components: A first measurement time for representing a measurement time in the measurement information; and the second measurement time is used for representing the measurement time configured by the network side.
16. 16. A prediction processing device is characterized by being applied to a terminal and comprising a memory, a transceiver and a processor: the system comprises a memory for storing a computer program, a transceiver for receiving and transmitting data under the control of the processor, and a processor for reading the computer program in the memory and performing the following operations: acquiring prediction information, and executing prediction through an artificial intelligence/machine learning AI/ML model according to the prediction information; wherein the prediction information includes one or more of: Time domain information; frequency domain information; reference signal information; Frequency information; Beam information.
17. 17. The apparatus of claim 16, wherein the time domain information comprises one or more of: period information and a first time offset; a second time offset; bit map information; The rate of decrease is measured.
18. 18. The apparatus of claim 16, wherein the prediction information further comprises one or more of: Predicting an object; Predicting an index number of the object; reporting information of the prediction result.
19. 19. The apparatus of claim 16, wherein the prediction information is contained within first information from which prediction is performed, the first information further comprising: Measurement information for indicating information measured by the terminal.
20. 20. The apparatus of claim 19, wherein said performing prediction from said first information comprises: And determining a prediction time according to the measurement information and the time domain information, wherein the time domain information is the period information and the first time offset.

Description

Prediction processing method and device and readable storage medium Technical Field The present application relates to the field of communications technologies, and in particular, to a prediction processing method, a device, and a readable storage medium. Background Artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) the application of artificial intelligence technology in modern society has become more and more widespread, such as in the medical field, where AI technology can be used for medical image analysis, diagnosis, prediction, etc., in the financial field, where AI technology can be used for risk management, fraud detection, credit assessment, etc., and in the manufacturing industry, AI technology can be used for intelligent manufacturing and intelligent management. In addition, there are many other fields in which AI technology is applied, such as autopilot, smart home, game development, education, etc. In the related art, a terminal may perform measurements on SSBs and CSI-RSs. For SSB measurements, the network device may configure the resource locations of the SSB measurements, such as frequency information, period, and start position within the period, etc. The terminal performs measurements based on these SSB configuration information. For the measurement of the CSI-RS, the network side configures the frequency domain position, the period and the like of the CSI-RS, and the terminal performs the measurement of the CSI-RS according to the configuration information. Because of the traditional measurement, the network side needs to configure corresponding reference signals, such as SSB, CSI-RS and the like, at the corresponding position, the terminal needs to perform measurement at the resource position indicated by the network side, so that higher power consumption is caused, and even part of the technology also needs to stop part of DL receiving/UL transmitting by the terminal to perform corresponding measurement, so that the throughput of the system is reduced and the like. Disclosure of Invention The embodiment of the application provides an information processing method, an information processing device and a readable storage medium, so as to reduce measurement of signal resources by a terminal. In a first aspect, an embodiment of the present application provides a prediction processing method, which is applied to a terminal, including: acquiring prediction information, and executing prediction through an artificial intelligence/machine learning AI/ML model according to the prediction information; wherein the prediction information includes one or more of: Time domain information, frequency domain information, reference signal information, frequency information, and beam information. Optionally, the time domain information includes one or more of: The method comprises the steps of period information, a first time offset, a second time offset, bit mapping information and a measurement reduction rate. Optionally, the prediction information further comprises one or more of a prediction object, an index number of the prediction object and reporting information of a prediction result. Optionally, the prediction information is included in first information, and prediction is performed according to the first information, where the first information further includes: optionally, the performing prediction according to the first information includes: And determining a prediction time according to the measurement information and the time domain information, wherein the time domain information is the period information and the first time offset. Optionally, the performing prediction according to the first information includes: And determining the predicted time according to the measurement information and the second time offset, wherein the second time offset represents the time offset between the first measurement time and the predicted time in the measurement information. Optionally, the performing prediction according to the first information includes: and determining the predicted time according to the second measurement time and the bit mapping information. Optionally, the performing prediction according to the first information includes: and determining the predicted time according to the second measurement time and the measurement reduction rate. Optionally, the performing prediction according to the first information includes: And determining the predicted time according to the second measurement time and the beam information. Optionally, the second measurement time is configured by the network side. Optionally, performing the prediction by the artificial intelligence/machine learning AI/ML model includes: AI prediction is performed by artificial intelligence/machine learning AI/ML model and predictive measurements are obtained. In a second aspect, an embodiment of the present application further provides a prediction processing method, which is applied to a network device, including: Transmitt