Beam Report Compression Using Linear PCA for Lower Overhead
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently compressing beam report overhead while maintaining explainability and reducing computational resources, particularly in the context of auto-encoder-based methods which lack transparency and require extensive computation.
Innovation Solution
Implementing a linear principal component analysis (PCA) filter to compress beam measurement information, allowing for an explainable output and reduced signaling overhead, compatible with various measurement patterns and tolerant of missed measurements, with precomputed singular value decomposition for efficient computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If auto-encoders are used to compress beam report overhead, then compression capability is improved, but computational resources and system complexity increase significantly
Solution Approach 1:
The patent extracts only the most significant components from the beam measurement information using PCA. By identifying and retaining only the top k principal components that capture the majority of variance in the data, the system removes redundant information while preserving essential beam report data, achieving compression without requiring complex auto-encoder architectures
Solution Approach 2:
The patent transforms the beam measurement data by changing its parameter representation through PCA. By rotating the coordinate system to align with eigenvectors of the covariance matrix and projecting data onto new axes, the system reparameterizes the information to reveal underlying patterns and reduce dimensionality, achieving compression through mathematical transformation rather than neural network processing
2Measurement precision
If comprehensive beam measurement information is reported, then measurement precision is improved, but signaling overhead increases
Solution Approach 1:
The patent extracts the essential features from comprehensive beam measurement information by identifying the top k principal components that capture the most significant variance. This extraction process separates critical measurement data from redundant information, enabling precise beam reporting with reduced signaling overhead by transmitting only the extracted essential components
Solution Approach 2:
The patent applies partial action by reporting only the top k principal components rather than all available beam measurement information. This partial reporting strategy captures the most significant measurement patterns while deliberately omitting less important details, achieving an optimal balance between measurement precision and signaling overhead reduction
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a plurality of reference signals, each reference signal of the plurality of reference signals corresponding to a beam of a plurality of beams. The UE may transmit a beam report comprising measurement information associated with the plurality of reference signals, wherein the measurement information is based on an output of a linear principal component analysis (PCA) filter. Numerous other aspects are described.