Confidence-Guided Beam Management for Reliable Beam Selection
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Solution Overview
Problem
Existing beam management systems in communication technologies, particularly in AI-based systems, struggle with selecting reliable target beam pairs due to the lack of confidence indicators for predicted beam resources, leading to potential incorrect predictions and increased measurement overheads.
Innovation Solution
A method and apparatus for beam management that evaluates and selects target beam resources based on confidence levels associated with each beam resource, using AI models to quantify reliability and reduce measurement overheads by scanning only when confidence is low.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI models are used to predict optimal beam pairs, then beam management efficiency is improved, but reliability of beam selection deteriorates due to lack of confidence indicators
Solution Approach 1:
The patent introduces confidence indicators as feedback information from the AI model prediction process. These confidence indicators provide feedback on the reliability of each predicted beam pair, allowing the system to assess prediction quality and make informed decisions about whether to trust the AI predictions or perform additional measurements.
Solution Approach 2:
The patent performs preliminary confidence assessment before final beam selection. By evaluating confidence indicators in advance, the system can determine which predicted beam pairs are reliable enough to use directly and which require further verification through measurement, thus preventing unreliable selections before they occur.
2Reliability
If measurement overheads are increased to verify beam quality, then beam selection reliability is improved, but system complexity and resource consumption worsen
Solution Approach 1:
The patent applies partial verification by performing measurements only on beam pairs with low confidence indicators rather than all predicted beam pairs. This partial action approach maintains reliability for high-confidence selections while reducing measurement overhead for low-confidence cases, avoiding excessive verification of already reliable predictions.
Solution Approach 2:
The patent changes the parameter of verification intensity based on confidence levels. High-confidence beam pairs undergo minimal or no verification, while low-confidence beam pairs undergo more thorough measurement. This parameter-based differentiation optimizes the balance between reliability and measurement overhead.
3Measurement precision
If confidence-based selection is implemented, then beam management accuracy is improved, but information processing requirements worsen
Solution Approach 1:
The patent extracts only the essential confidence indicator information from the AI model output for further processing, rather than handling all raw prediction data. This extraction approach maintains measurement precision by focusing on the critical confidence metric while reducing the quantity of information that needs to be processed and stored.
Data Source
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AI summary
A beam management method and apparatus, a device, a storage medium, and a program product, which relate to the technical field of communications. The method comprises: selecting a target beam resource from among K beam resources according to confidence levels which respectively correspond to the K beam resources, wherein K is an integer greater than or equal to 1 (710). One target beam resource is selected from among the K beam resources by means of the confidence levels which respectively correspond to the K beam resources. On the basis of confidence level assisted beam management, when a target beam resource is selected, the reliability of beam resources can be evaluated on the basis of confidence levels, and a more reliable target beam resource is acquired to carry out a data service, thereby achieving better spatial-domain and time-domain beam management.