AI Beam Management for Wireless UE Power and Latency
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Solution Overview
Problem
Current wireless communication systems lack efficient methods for beam management, leading to high power consumption, increased latency, and reduced signal quality due to the need for extensive beam sweeping and refinement processes.
Innovation Solution
The implementation of AI-enabled beam management techniques, where user equipment (UE) utilizes AI algorithms to process beam measurements and infer higher strength and quality beams, reducing the need for extensive beam sweeping and refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive beam sweeping and refinement processes are performed to achieve high directional precision and signal quality, then beam measurement accuracy is improved, but power consumption increases and latency increases
Solution Approach 1:
The system performs preliminary beam sweeping to identify candidate beams, then uses AI algorithms to predict and infer optimal beams without exhaustive measurement. This preliminary action reduces the need for extensive subsequent beam refinement, thereby lowering power consumption while maintaining measurement accuracy.
Solution Approach 2:
AI algorithms are introduced as an intermediary between beam sweeping and beam selection. The AI model processes beam measurement data and predicts optimal beams, acting as a mediator that reduces the computational burden and power consumption associated with traditional exhaustive beam refinement processes.
2Measurement precision
If extensive beam sweeping and refinement processes are performed to achieve high directional precision and signal quality, then beam measurement accuracy is improved, but latency increases
Solution Approach 1:
The system performs preliminary beam sweeping to identify candidate beams, then uses AI algorithms to predict and infer optimal beams without exhaustive measurement. This preliminary action reduces the time required for subsequent beam refinement, thereby reducing latency while maintaining measurement accuracy.
Solution Approach 2:
Traditional mechanical beam sweeping and refinement processes are replaced with AI-based prediction algorithms. This substitution eliminates the time-consuming iterative measurement and adjustment processes, significantly reducing latency while maintaining or improving beam selection accuracy.
3Use of energy by moving object
If AI algorithms are used to infer beams from measured beams, then power consumption is reduced and latency is reduced, but beam measurement accuracy may be compromised
Solution Approach 1:
The system performs partial beam sweeping to gather sufficient training data for AI algorithms, then uses these algorithms to infer optimal beams. This partial action approach consumes less power and time while the AI model compensates for the reduced measurement coverage, maintaining accuracy through intelligent prediction rather than exhaustive measurement.
Solution Approach 2:
The AI algorithms create virtual copies or predictions of beam characteristics based on measured data patterns. Instead of performing exhaustive physical measurements, the system uses AI-generated virtual beam profiles to identify optimal beams, reducing power consumption while maintaining measurement accuracy through pattern recognition and prediction.
Data Source
AI summary
Various aspects of the present disclosure relate to artificial intelligence (AI) enabled beam management. A base station may notify a user equipment (UE) of an availability of AI-enabled beam management and provide configuration parameters for an AI algorithm. The UE may utilize the configuration parameters to configure an AI algorithm. The UE may process a first set of beams utilizing the configured AI algorithm to identify a second set of beams. The UE may notify the base station (and/or other network entity) of the second set of beams, and one or more beams of the second set of beams can be utilized for wireless communication between the UE and the base station and/or other network entity or UE.