AI Beam Reporting Without Reference Signal Measurement
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Solution Overview
Problem
In wireless communication networks, beam reporting by terminal devices requires measuring reference signals, which consumes excessive uplink and downlink resources due to the need for payload and signal transmission.
Innovation Solution
Implementing artificial intelligence (AI) models at terminal devices to predict beam quality without performing explicit reference signal measurements, allowing for reduced payload in beam reports and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beam measurement and reference signal transmission are performed, then beam reporting accuracy is improved, but uplink and downlink resource consumption increases
Solution Approach 1:
The patent uses AI models to generate virtual reference signals and predict beam quality metrics without actual physical measurement. The terminal device receives AI-generated reference signals from the network device, processes them through an AI model to predict beam quality, and reports only the predicted results. This copying approach replaces physical measurement with virtual simulation, reducing resource consumption while maintaining reporting functionality.
Solution Approach 2:
The patent substitutes the traditional mechanical measurement system with an AI-based prediction system. Instead of physically transmitting reference signals and performing measurements, the system uses machine learning models to predict beam quality based on AI-generated signals. This substitution eliminates the need for actual reference signal transmission and measurement processing, significantly reducing uplink and downlink resource usage.
2Loss of information
If reference signal quality information is included in beam reports, then reporting completeness is improved, but payload size and resource usage increase
Solution Approach 1:
The patent extracts only the essential beam quality prediction results from the AI model processing, omitting the need to report complete reference signal quality information. The terminal device generates compact beam reports containing only the predicted beam quality metrics, excluding detailed measurement data. This extraction approach maintains reporting completeness for decision-making while significantly reducing payload size.
Solution Approach 2:
The patent uses AI-generated virtual reference signals to produce beam quality predictions, replacing the need to report actual measured reference signal quality information. The copied AI prediction results provide sufficient information for beam management decisions without requiring transmission of complete quality metrics, thus reducing payload while maintaining reporting effectiveness.
Data Source
AI summary
Example embodiments of the present disclosure relate to beam reporting based on artificial intelligence prediction. In an example method, the method of communication at the terminal device comprising: receiving a configuration for a first beam report from a network device; generating, the first beam report using an artificial intelligence (AI) model without performing a beam measurement on a first set of reference signals associated with the first beam report; and transmitting the first beam report to the network device. In this way, the payload of the beam report may be reduced, thus save the uplink resource.