MMSE Weight Matrix Compression for Wireless Channel Estimation
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Solution Overview
Problem
Existing channel estimation methods in wireless communication systems, such as LMMSE, face high computational complexity due to matrix inversion operations and require significant memory to store weights, especially in carrier aggregation and MIMO systems.
Innovation Solution
A method that restores an original MMSE weight matrix from a compressed MMSE weight matrix, allowing for LMMSE-based channel estimation without orthogonal cover code despreading, thereby reducing computational complexity and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LMMSE channel estimation method is used, then channel estimation accuracy is improved, but computational complexity increases due to matrix inversion operations
Solution Approach 1:
The patent segments the MMSE weight matrix into multiple sub-matrices (first sub-matrix and second sub-matrix) that can be independently calculated and stored. This segmentation allows the system to pre-compute and store only the essential sub-matrices, reducing the computational burden during channel estimation while maintaining the accuracy benefits of MMSE weighting.
Solution Approach 2:
The patent performs preliminary calculation and storage of the MMSE weight matrix sub-matrices during system initialization or setup phases. By pre-computing these weight matrices when the channel statistics are relatively stable, the system avoids performing computationally intensive matrix inversions during actual channel estimation operations, thus reducing real-time computational complexity while preserving estimation accuracy.
2Measurement precision
If MMSE weight matrix is stored for each carrier component and receiving antenna, then channel estimation performance is improved, but memory size increases
Solution Approach 1:
The patent divides the large MMSE weight matrix into smaller sub-matrices corresponding to different carrier components and receiving antennas. By segmenting the weight matrix in this manner, the system can store only the essential sub-matrices in memory rather than the complete weight matrix, significantly reducing memory requirements while still enabling accurate channel estimation for each specific carrier and antenna combination.
3Reliability
If orthogonal cover code despreading is performed, then inter-layer interference is reduced, but computational complexity increases
Solution Approach 1:
The patent extracts and removes the OCC despreading operation from the channel estimation process. Instead of performing full OCC despreading which involves computationally intensive operations, the patent directly applies the pre-computed MMSE weight matrices to the received signals, achieving interference reduction through the optimized weighting without the additional computational burden of explicit despreading operations.
Data Source
AI summary
A method of operating an electronic device includes receiving, from a base station, a signal including a new radio physical downlink shared channel (NR PDSCH) demodulation reference signal (DMRS), performing depatterning by multiplying the received signal by an orthogonal cover code (OCC) matrix, calculating a compressed minimum mean square error (MMSE) weight matrix, based on the received signal, restoring an original MMSE weight matrix from the calculated compressed MMSE weight matrix, and performing linear MMSE (LMMSE)-based channel estimation based on the original MMSE weight matrix and the depatterned received signal.