MIMO Receiver QR Decomposition Reduces Computational Complexity
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Solution Overview
Problem
MIMO receivers face high computational complexity in maximum likelihood detection, especially with increasing values of transmit and receive antennas, leading to prohibitive operational costs and reduced decoding efficiency.
Innovation Solution
The implementation of QR decomposition to reduce the computational complexity of MIMO receivers by transforming the channel matrix into an upper triangular matrix, allowing for reduced operations in distance calculations and improved accuracy through approximations and subset searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maximum likelihood detection is used in MIMO receivers, then detection accuracy is improved, but computational complexity increases prohibitively
Solution Approach 1:
The patent segments the maximum likelihood detection process into two stages: first performing QR decomposition to transform the channel matrix into an upper triangular form, then performing detection on the transformed system. This segmentation reduces the computational complexity from exponential to polynomial while maintaining detection accuracy.
Solution Approach 2:
The patent applies preliminary QR decomposition to the channel matrix before performing maximum likelihood detection. This preliminary transformation simplifies the subsequent detection process by converting the original complex detection problem into a simpler form that can be solved with reduced computational effort.
2Productivity
If the number of transmit and receive antennas is increased, then system capacity and reception quality are improved, but operational costs and processing requirements increase
Solution Approach 1:
The patent changes the parameter representation of the channel matrix by applying QR decomposition, transforming it into an upper triangular matrix. This parameter transformation allows the system to handle increased numbers of antennas with reduced computational burden, enabling higher system capacity without proportionally increasing operational costs.
3Measurement precision
If full maximum likelihood detection is performed, then decoding accuracy is maximized, but processing time and hardware requirements become prohibitive
Solution Approach 1:
The patent segments the detection process into QR decomposition followed by simplified maximum likelihood detection on the transformed system. This segmentation maintains decoding accuracy while dramatically reducing processing time by avoiding the computationally intensive operations required for full maximum likelihood detection on the original channel matrix.
Solution Approach 2:
The patent performs preliminary QR decomposition to transform the channel matrix before detection. This preliminary action simplifies the subsequent detection operations, reducing the number of computations required and thereby decreasing processing time while preserving decoding accuracy.
Data Source
AI summary
A MIMO receiver is provided with a preprocessor for performing QR decomposition of a channel matrix H wherein the factored reduced matrix R is used in place of H and Q*y is used in place of the received vector y in a maximum likelihood detector (“MLD”). The maximum likelihood detector might be a hard-decision MLD or a soft-decision MLD. A savings of computational complexity can be used to provide comparable results more quickly, using less circuitry, and/or requiring less consumed energy, or performance can be improved for a fixed amount of time, circuitry and/or energy. Where the MLD uses approximations, such as finite resolution calculations (fixed point or the like) or L1 Norm approximations, the reduced number of operations resulting from using the reduced matrix results in improved approximations as a result of the finite resolution operations. Other methods of reducing the channel matrix might be used for suitable and/or cumulative advantages.