Angle-of-Arrival Heatmap Storage Optimization
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Solution Overview
Problem
Wireless communication systems face significant storage challenges due to the large amount of data required for Angle-of-Arrival (AoA) heatmaps, especially with multiple antennas, leading to memory constraints in servers and access points.
Innovation Solution
The method involves determining a centroid of the antenna distribution, computing a heatmap for this centroid, and storing only the difference data between the centroid heatmap and each individual antenna's heatmap, while exploiting symmetric antenna configurations to further reduce data storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heatmaps are stored for each antenna individually, then location determination accuracy is improved, but memory usage increases substantially
Solution Approach 1:
The patent combines multiple antenna heatmaps into a single composite heatmap that represents the collective spatial distribution of signal strength across all antennas. This merging approach maintains location determination accuracy by preserving the aggregate spatial information while eliminating the need to store separate heatmap data for each antenna, thereby substantially reducing memory usage.
Solution Approach 2:
The composite heatmap serves multiple functions simultaneously: it provides location determination capability for all antennas collectively, enables signal strength measurement, and supports movement detection. This multi-functional approach replaces the need for multiple individual heatmaps, achieving both accuracy preservation and memory optimization.
2Measurement precision
If the number of antennas is increased, then location determination precision is improved, but data storage requirements increase
Solution Approach 1:
The patent merges the spatial information from multiple antennas into a single composite heatmap structure. This allows the system to leverage the precision benefits of multiple antennas for location determination while storing only one unified heatmap instead of multiple separate heatmaps, thereby linearly reducing storage requirements as antenna count increases.
3Measurement precision
If heatmap resolution is increased, then measurement accuracy is improved, but memory consumption increases
Solution Approach 1:
The composite heatmap consolidates high-resolution spatial data from multiple antennas into a unified structure with optimized memory layout. This merging approach preserves measurement accuracy by maintaining fine-grained spatial resolution while reducing redundant storage of identical or similar data patterns across multiple antenna heatmaps.
Data Source
AI summary
Heatmap data, such as Angle-of-Arrival heatmap data, is generated and stored for a plurality of antennas of wireless communication device. A centroid of the plurality of antennas is determined. A heatmap is computed for the centroid for a measured parameter across a plurality of bins at coordinates within a region of interest. Heatmap data for the centroid is stored. For a given one of the plurality of antennas, a difference is computed between a heatmap for the given antenna and the heatmap for the centroid. The difference data representing the difference is stored for the given antenna.