Bidirectional Perception Signaling for Adaptive Beam Weight Learning
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Solution Overview
Problem
Existing wireless communication systems, particularly in the context of 5G, face challenges in optimizing beam management and positioning accuracy due to the complexity of multipath channels and varying environmental conditions, leading to suboptimal performance in adaptive beam weight learning and sensing operations.
Innovation Solution
Implementing perception-based adaptive beam weight learning and sensing operations, where user equipment (UE) and base stations utilize perception-based approaches to determine spatial regions of interest and adjust beam weights dynamically, enhancing communication performance through improved signal processing and beamforming techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If perception-based adaptive beam weight learning is implemented, then communication performance and positioning accuracy are improved, but system complexity increases
Solution Approach 1:
The system divides the complex beam management task into separate perception-based learning components at the UE and execution components at the base station. The UE performs perception-based approaches to determine spatial regions of interest and generates indications, while the base station receives and executes the beam weight adjustments, splitting the computational complexity across multiple devices.
Solution Approach 2:
The UE performs perception-based analysis and determines spatial regions of interest before transmitting beam weight indications to the base station. This preliminary action allows the base station to execute pre-optimized beam configurations, improving communication performance while reducing real-time computational complexity at the base station.
2Measurement precision
If perception-based sensing operations are performed in spatial regions of interest, then positioning accuracy is improved, but signal processing complexity increases
Solution Approach 1:
The system applies perception-based sensing operations specifically in identified spatial regions of interest rather than uniformly across all directions. The UE determines which spatial regions require attention and directs sensing resources accordingly, improving positioning accuracy while reducing overall signal processing complexity by focusing computational efforts locally.
3Productivity
If adaptive beam weight learning is used, then beam management performance is improved, but computational requirements increase
Solution Approach 1:
Instead of having the base station perform all adaptive beam weight learning computations, the system inverts the approach by having the UE perform perception-based learning and generate beam weight indications. This shifts computational requirements to the UE, which may have comparable processing capabilities, while the base station executes the learned beam configurations with reduced real-time computational burden.
Data Source
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AI summary
Disclosed are techniques for wireless communication. In an aspect, a user equipment (UE) determines that a perception-based approach to adaptive beam weight learning would provide improved performance of wireless communication with a base station over a wireless communication medium relative to non-perception-based approaches to adaptive beam weight learning, and transmits, to a network node, an indication for the base station to perform perception-based adaptive beam weight learning procedures for the wireless communication with the base station over the wireless communication medium.