Wireless Beam Management Using AI/ML Signal and Interference Inputs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current AI/ML model-based beam (pair) prediction in wireless communication systems fails to reflect interference between beams, which affects the performance of beam management systems.
Innovation Solution
A wireless communication method that inputs measured power information of reference signals and interference noise into a first network model to predict identifier information of spatial filters and/or Layer1 Signal to Interference plus Noise Ratios (L1-SINRs), reflecting interference between beams and improving beam management performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI/ML model-based beam (pair) prediction is used, then beam management system performance is improved, but interference between beams (beam pairs) cannot be reflected
Solution Approach 1:
The measurement process is segmented into two distinct parts: reference signal measurement and interference noise measurement. The reference signal measurement set is used to measure reference signal power, while the interference signal measurement set is used to measure interference noise power. This segmentation allows the system to separately capture and process different components of the wireless channel, enabling the AI/ML model to receive comprehensive input that includes both desired signal information and interference information, thereby resolving the contradiction between improving beam management performance and reflecting interference between beams.
2Area of stationary object
If multiple beams (beam pairs) are used to cover different terminals, then system coverage is improved, but interference between beams increases
Solution Approach 1:
The system implements a feedback mechanism where the terminal device measures interference noise power from multiple beams and feeds this information back to the network device. The network device then uses this interference information as input to the AI/ML model for beam prediction. This feedback loop enables the system to dynamically adjust beam selection and configuration based on actual interference conditions, allowing multiple beams to be used for expanded coverage while the interference between them is continuously monitored and managed through the predictive model.
Data Source
AI summary
A wireless communication method and a device are disclosed. A first communication device may input into a first network model power information of a reference signal portion and power information of an interference noise portion that are obtained by means of measurement, so as to perform prediction to obtain identification information of K spatial filters and/or L1-SINRs corresponding to the K spatial filters. That is, interference between beams (beam pairs) can be reflected in beam (pair) prediction based on an AI/ML model, thereby improving the performance of a beam management system.