Autocorrelation Matrix Precoding for MIMO Quantization Noise
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Solution Overview
Problem
Conventional MIMO systems face quantization errors in MU-MIMO and CoMP transmissions due to limited precoder selection from predefined codebooks, leading to reduced signal-to-noise ratios and increased feedback overhead in LTE networks.
Innovation Solution
The use of an autocorrelation matrix to derive weight vectors for precoding in user equipment, which reduces quantization noise without requiring a larger codebook, allowing for improved linear precoding methods that maximize the transmit signal-to-interference ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a larger codebook is used to reduce quantization error, then precoding accuracy is improved, but feedback overhead and system complexity increase
Solution Approach 1:
The patent introduces an autocorrelation matrix as an intermediary between the channel state and the codebook selection. Instead of directly selecting from a large codebook, the system computes the autocorrelation matrix of the channel and uses its properties (eigenvectors, eigenvalues) to guide precoder selection from the existing codebook, thereby reducing quantization error without expanding the codebook size
Solution Approach 2:
The patent transforms the channel state representation by computing the autocorrelation matrix and its eigendecomposition. By changing the parameter space from raw channel coefficients to autocorrelation eigenvalues and eigenvectors, the system captures essential channel characteristics more efficiently, enabling better precoder selection from the same codebook
2Measurement precision
If feedback bandwidth is increased to provide more precise channel state information, then beamforming performance is improved, but available bandwidth for data traffic is reduced
Solution Approach 1:
The patent extracts the essential characteristics of the channel state by computing the autocorrelation matrix and its dominant eigenvectors. Instead of feeding back the complete channel state information, only the relevant eigenvalues and eigenvectors are used for precoder selection, significantly reducing the feedback overhead while maintaining beamforming performance
Solution Approach 2:
The patent applies partial action by using only the most significant eigenvectors and eigenvalues of the autocorrelation matrix for precoder selection. By focusing on the dominant components of the channel correlation structure, the system achieves effective beamforming with reduced feedback requirements
Data Source
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Figure 2
AI summary
Embodiments of user equipment (UE) and methods for precoding for codebook based beamforming are generally described herein In some embodiments, an autocorrelation matrix is used to derive weight vectors for beamforming feedback resulting in a reduction in quantization noise.