Interference-Aware AI/ML Beam Prediction in Wireless Communication
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, leading to suboptimal performance in beam management systems.
Innovation Solution
A wireless communication method that inputs measured power information of reference signals and interference noise into an AI/ML model to predict spatial filter identifiers and/or Layer1 Signal to Interference plus Noise Ratios (L1-SINRs), thereby 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 performance is improved, but interference between beams cannot be reflected
Solution Approach 1:
The received signal power is segmented into two distinct components: reference signal power and interference noise power. This segmentation allows the AI/ML model to process interference information separately and accurately, resolving the contradiction by enabling interference reflection while maintaining beam management performance improvement.
Solution Approach 2:
The patent performs preliminary measurement and separation of reference signal power and interference noise power before feeding data into the AI/ML model. This preliminary action ensures that interference information is captured and processed in advance, allowing the model to reflect interference while still achieving performance improvement through predictive capabilities.
2Area of stationary object
If multiple beams (beam pairs) are used to cover different terminals, then coverage is improved, but interference between beams increases
Solution Approach 1:
The patent introduces feedback mechanisms where the AI/ML model predicts L1-SINR values that reflect interference levels. This feedback information is used to adjust beam selection and management decisions, allowing the system to maintain wide coverage while compensating for interference effects through informed decision-making.
Solution Approach 2:
The patent changes the parameter set fed into the AI/ML model to include separately measured reference signal power and interference noise power. This parameter change enables the model to account for interference when making beam management decisions, allowing multiple beams to be used for coverage while managing interference through predictive analysis.
Data Source
Figure 1~3
Figure 4~5
Figure 6~7
AI summary
Provided in the embodiments of the present application are a wireless communication method and a device. 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.