Recursive MIMO Equalizer Channel Estimation With Distributed QR
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Solution Overview
Problem
Current MIMO wireless communication systems face challenges in quickly estimating channel conditions due to high computational density and hardware burden, particularly when processing matrix HestP−1, which is necessary for channel equalization.
Innovation Solution
A receiver module employs a recursive channel estimation method that begins estimating matrix H as soon as the first training field is received, using a matrix P with a condition number of 1, and processes training symbols in accordance with this matrix prior to transmission, reducing computational complexity from O(n3) to O(n2) by performing distributed QR across LTFs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If orthogonal-triangular decomposition (QR) is applied to the full matrix HestP−1, then accurate channel estimation is achieved, but computational complexity increases to O(n³) and processing latency increases
Solution Approach 1:
The patent segments the channel estimation process into two parts: first estimating Hest using training symbols, then computing H = HestP−1 separately. This segmentation allows the complex QR decomposition to be applied only to Hest rather than the full HestP−1 matrix, reducing computational complexity while maintaining estimation accuracy
Solution Approach 2:
The patent performs preliminary computation by pre-calculating the inverse of matrix P (P−1) and storing it. This preliminary action allows the final channel matrix H to be computed simply as H = HestP−1 without performing complex decomposition operations at runtime, significantly reducing processing latency
2Measurement precision
If orthogonal-triangular decomposition (QR) is applied to the full matrix HestP−1, then accurate channel estimation is achieved, but processing latency increases
Solution Approach 1:
The patent segments the channel estimation process into two parts: first estimating Hest using training symbols, then computing H = HestP−1 separately. This segmentation allows the complex QR decomposition to be applied only to Hest rather than the full HestP−1 matrix, reducing computational complexity while maintaining estimation accuracy
Solution Approach 2:
The patent performs preliminary computation by pre-calculating the inverse of matrix P (P−1) and storing it. This preliminary action allows the final channel matrix H to be computed simply as H = HestP−1 without performing complex decomposition operations at runtime, significantly reducing processing latency
3Reliability
If matrix P is used to ensure orthogonality of training symbols, then reliable channel estimation is achieved, but the equivalent matrix RP−1 becomes a full matrix making inversion difficult
Solution Approach 1:
The patent performs preliminary computation by pre-calculating the inverse of matrix P (P−1) and storing it. This preliminary action allows the final channel matrix H to be computed simply as H = HestP−1 without performing complex decomposition operations at runtime, significantly reducing processing latency
Solution Approach 2:
The patent extracts and separates the matrix P operation from the channel estimation process. By computing H = HestP−1 separately after estimating Hest, the patent avoids the complexity of inverting the full equivalent matrix RP−1, taking out the difficult inversion operation and replacing it with a simpler computation
Data Source
AI summary
A receiver module includes an input that receives a data message from a wireless communication channel. The data message has a plurality of training fields and data. A channel estimator module recursively estimates a matrix H that represents the channel based on the plurality of training fields. The recursive estimation is performed as the plurality of training fields are being received. An equalizer module applies coefficients to the data based on the matrix H.