Precoding Matrix Index Selection via Eigenvalue Decomposition
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Solution Overview
Problem
Legacy precoding matrix index (PMI) selection methods based on codebook search are impractical due to high complexity, resulting in low performance.
Innovation Solution
A two-stage PMI selection method that determines an ideal precoder through eigenvalue decomposition, selects base beams based on correlation power, and estimates amplitude and cophase coefficients, reducing complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If codebook search methods are used for PMI selection, then selection accuracy can be maintained, but computational complexity increases significantly
Solution Approach 1:
The patent segments the codebook search process into two stages: first performing eigenvalue decomposition to obtain ideal precoders and select base beams, then estimating amplitude and cophase coefficients. This segmentation reduces the search space and computational complexity while maintaining PMI selection accuracy.
Solution Approach 2:
The patent extracts the essential components (eigenvalues, eigenvectors, base beams) from the full codebook search process, eliminating the need to search through the entire large codebook. This extraction approach maintains accuracy by focusing on the most critical parameters while reducing computational complexity.
2Device complexity
If greedy search methods are used to reduce complexity, then computational complexity decreases, but PMI selection performance deteriorates
Solution Approach 1:
The patent performs preliminary eigenvalue decomposition to obtain ideal precoders and identify base beams before the actual PMI selection process. This preliminary action provides a structured foundation that guides subsequent coefficient estimation, ensuring both low complexity and high performance.
Solution Approach 2:
The patent changes the approach from direct codebook index search to estimating continuous parameters (amplitude and cophase coefficients) based on eigenvalue decomposition results. This parameter transformation enables accurate PMI selection with reduced computational complexity by working with optimized mathematical representations rather than exhaustive codebook search.
Data Source
AI summary
A method and system for selecting precoding matrix index are herein disclosed. The method includes determining a precoder and candidate beams, selecting base beams based on a correlation power between the determined precoder and determined candidate beams, and estimating amplitude coefficients and cophase coefficients based on a correlation between the determined precoder and the selected base beams.