AI-Predicted Beam Dwelling Time for Lower-Overhead UE Reporting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing beam reporting mechanisms in cellular networks, such as 5G, result in unnecessary power consumption and increased overhead due to frequent beam measurements and reports, as the selected beam may remain optimal for a certain duration without being updated.
Innovation Solution
Implementing an artificial intelligence model to predict the dwelling time of a beam, allowing the UE to avoid unnecessary beam reporting by determining when the current beam quality is expected to degrade, thereby reducing unnecessary measurements and signaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent beam measurements and reports are performed, then beam quality optimization is improved, but power consumption and processing overhead increase
Solution Approach 1:
The AI model performs preliminary prediction of beam dwelling time before actual beam switching occurs. By predicting how long the current beam will remain optimal, the system can advance beam reporting decisions and avoid unnecessary measurements during periods when the current beam is expected to remain superior, thus reducing power consumption while maintaining optimization reliability.
Solution Approach 2:
The system implements feedback through the AI model that continuously learns from actual beam performance data. The model uses historical beam measurement data and dwelling time information to refine its predictions, creating a closed-loop system that adapts to actual channel conditions and optimizes the balance between measurement frequency and power consumption.
2Reliability
If frequent beam measurements and reports are performed, then beam quality optimization is improved, but processing overhead increases
Solution Approach 1:
The AI model performs preliminary prediction of beam dwelling time before actual beam switching occurs. By predicting how long the current beam will remain optimal, the system can advance beam reporting decisions and avoid unnecessary measurements during periods when the current beam is expected to remain superior, thus reducing processing overhead while maintaining optimization reliability.
Solution Approach 2:
The AI model serves itself by automatically learning from historical data and refining its own predictions without requiring manual intervention or complex external processing systems. This self-service capability reduces the overall processing overhead by eliminating the need for additional analysis, validation, or adjustment mechanisms.
3Reliability
If beam reporting is performed continuously, then beam quality is maintained, but unnecessary measurements and signaling occur
Solution Approach 1:
The AI model performs preliminary prediction of beam dwelling time before actual beam switching occurs. By predicting how long the current beam will remain optimal, the system can advance beam reporting decisions and avoid unnecessary measurements during periods when the current beam is expected to remain superior, thus reducing energy waste while maintaining quality through intelligent timing rather than continuous operation.
Solution Approach 2:
Instead of continuous beam reporting, the system implements periodic action driven by AI predictions. Beam reporting occurs at strategically determined intervals based on predicted dwelling time, transitioning from continuous to periodic operation that maintains beam quality while eliminating unnecessary measurements and signaling during stable periods.
Data Source
AI summary
The present application relates to devices and components including apparatus, systems, and methods to perform beam measurements and beam reporting. In an example, a beam measurement may exist and can be used to generate a predicted dwelling time of a UE in a beam of a base station. The dwelling time represents a time duration during which the beam is expected to have the best beam quality for the UE among the beams of the base station. Given the predicted dwelling time, the base station can send a reference signal to the UE, or the UE can perform a measurement on the reference signal and/or send information about this measurement to the base station. This information can be sent as a beam report.