Maximum Likelihood Decoding via Segmented QR Decomposition
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Solution Overview
Problem
Conventional maximum likelihood decoding (MLD) methods in MIMO systems require excessive calculations, leading to increased processing time and a high possibility of missing the optimum solution due to premature narrowing down of candidates in the QRMLD process.
Innovation Solution
The method involves arranging channel impulse responses in multiple orders, generating channel matrices, applying QR decomposition to obtain triangular matrices, and selecting combination candidates with the shortest integrated Euclidean distance to reduce calculation and improve estimation quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the QRM-MLD process narrows down candidates to one in advance, then the amount of calculation is reduced, but the possibility of missing the optimum solution increases
Solution Approach 1:
The patent divides the channel matrix into multiple sub-matrices and processes them separately through QR decomposition. By segmenting the decoding process into parallel sub-processes, the system maintains calculation efficiency while preserving more candidate solutions through integrated processing, thus resolving the contradiction between reducing calculation and maintaining solution accuracy.
Solution Approach 2:
The patent performs QR decomposition on multiple channel matrices with different element orders rather than a single matrix. This partial repetition of the decoding process with varied configurations allows the system to explore more solution spaces without fully exhaustively searching all possibilities, balancing calculation reduction with solution reliability.
2Measurement precision
If multiple channel matrices with different element orders are used, then the quality of estimation solution is improved, but the amount of calculation increases
Solution Approach 1:
The patent segments the channel matrix into multiple sub-matrices and processes them through parallel QR decomposition operations. This segmentation allows the system to improve estimation quality by examining different matrix configurations while managing calculation complexity through structured parallel processing rather than sequential exhaustive analysis.
Solution Approach 2:
The patent varies the element orders of channel matrices as a parameter change strategy. By systematically changing the ordering parameters of matrix elements and processing multiple configurations, the system improves estimation quality by capturing different signal interference patterns while maintaining manageable complexity through the structured approach.
3Reliability
If exhaustive MLD process is performed for all symbol candidates, then the optimum solution is guaranteed, but the processing time becomes excessively long
Solution Approach 1:
The patent segments the exhaustive MLD process into multiple parallel QR decomposition operations on divided channel matrices. This segmentation reduces processing time by enabling parallel computation while maintaining solution optimality through the integrated processing of all segments, avoiding the need for sequential exhaustive search.
Solution Approach 2:
The patent performs preliminary QR decomposition on multiple channel matrix configurations before the final candidate selection. This preliminary processing organizes and pre-computes transformation matrices, reducing the computational burden during the final decoding stage and thereby reducing overall processing time while preserving solution optimality.
Data Source
AI summary
Provided is a maximum likelihood decoding method that includes the steps of; firstly arranging channel impulse responses corresponding to the received signals in a plurality of different orders; secondly specifying the same number of parts as the plurality of different orders in which the channel impulse responses are arranged, so that the received signals are placed in each of the parts; thirdly generating channel matrices each having the channel impulse responses as matrix elements, by using the channel impulse responses arranged in the plurality of different orders, obtaining triangular matrices by applying QR decomposition to the generated channel matrices, and determining at least one combination candidate for each of the parts of the plurality of transmission signals by using the obtained triangular matrices; and fourthly selecting the combination candidates so that a Euclidean distance between the combination candidates determined in the third step is shortest.