K-best MIMO Decoder Architecture for Complexity Reduction
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Solution Overview
Problem
The complexity of decoding in multiple-input multiple-output (MIMO) systems grows exponentially with the number of transmit transceivers, making it computationally demanding, especially with high-dimensional signal constellations like 16-QAM, which hinders efficient data decoding in wireless communication systems.
Innovation Solution
The implementation of K-best breadth-first decoding methods, which employs a K-best breadth-first search algorithm and a log-likelihood-ratio computing unit to reduce computational complexity by pruning nodes in a node tree based on partial Euclidean distances, thereby efficiently estimating the transmitted constellation point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maximum-likelihood decoding is used to achieve optimal decoding performance, then decoding accuracy is improved, but computational complexity grows exponentially with the number of transmit transceivers
Solution Approach 1:
The patent applies partial action by implementing K-best breadth-first search that explores only the K most promising paths in the constellation space rather than exhaustively searching all possible constellation points. This partial search approach maintains acceptable decoding performance while significantly reducing computational complexity compared to full maximum-likelihood decoding.
Solution Approach 2:
The patent segments the decoding process into multiple stages using breadth-first search, where constellation points are explored level by level based on partial Euclidean distances. This segmentation allows the system to prune unlikely paths early in the search process, reducing the overall computational burden while maintaining decoding accuracy.
2Productivity
If the number of transmit transceivers is increased to improve wireless communication performance, then system capacity is improved, but decoding complexity grows exponentially
Solution Approach 1:
The patent uses K-best breadth-first search to perform partial exploration of the search space rather than complete exhaustive search. By maintaining only the K best paths at each node and pruning the rest, the system can handle higher numbers of transmit transceivers without the exponential complexity increase that would occur with full maximum-likelihood decoding.
Solution Approach 2:
The patent introduces an intermediary search algorithm (K-best breadth-first search) that mediates between the increased system capacity provided by multiple transmit transceivers and the computational resources available. This intermediary approach allows the system to benefit from MIMO capabilities while keeping decoding complexity manageable through intelligent search space pruning.
3Reliability
If exhaustive search of all constellation points is performed to ensure optimal decoding, then decoding reliability is improved, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary action by calculating and storing partial Euclidean distances at each node of the search tree before completing the full constellation point evaluation. This preliminary computation allows the system to rank and prune search paths early, maintaining decoding reliability while significantly reducing the time required to examine all possible constellation points.
Solution Approach 2:
The patent applies partial action by implementing K-best search that maintains only the K most promising paths rather than exhaustively processing all paths. This partial exploration approach ensures sufficient decoding reliability through the K-best paths while dramatically reducing processing time compared to complete exhaustive search.
Data Source
AI summary
In some embodiments, a device includes a multiple-input multiple-output (“MIMO”) decoder module coupled to a first log-likelihood-ratio (“LLR”) computing unit. The decoder module includes at least one processing unit and at least one sorting unit. The decoder module preferably uses a K-best breadth-first search method to decode data from MIMO sources. In some embodiments, a method includes receiving data representing a vector of receive signal samples detected by multiple receive transceivers. The method further includes performing a K-best breadth-first search on the data to obtain an estimated constellation point. The method further includes providing a user data stream based at least in part on the estimated constellation point.