Multilevel MIMO Detection With Cluster Search and Search-Space Pruning
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Solution Overview
Problem
Current MIMO detection methods face challenges in reducing system complexity while maintaining high performance, especially at low bit error rates and in adapting to increasing data transmission demands, due to issues with system errors and complexity in multilevel QAM constellations.
Innovation Solution
A two-stage search method is employed in the MIMO detection system, utilizing a breadth-first search at the highest level (cluster matching) and a detailed matching at the lowest level, with the generation of symbol vector sets and search radii to reduce the search space, and incorporating a branch and bound method to quickly eliminate non-conforming mean symbol vectors, thereby reducing system complexity and enhancing detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maximum likelihood (ML) algorithm is used for MIMO detection, then detection performance is improved, but system complexity increases significantly
Solution Approach 1:
The patent segments the N-QAM constellation into multiple levels (L levels where L=log4 N), creating a hierarchical structure with symbol vector sets at each level. This segmentation transforms the single complex ML detection problem into multiple simpler detection stages, reducing overall system complexity while maintaining detection performance.
Solution Approach 2:
The patent introduces a new dimensional structure by organizing the constellation space into multilevel clusters with different search radii. Instead of searching the entire N-QAM constellation at once, the algorithm performs sequential searches at different levels with decreasing search radii, effectively adding a hierarchical dimension to the detection process.
2Device complexity
If search range is reduced to decrease complexity, then system complexity is reduced, but detection accuracy deteriorates
Solution Approach 1:
The patent performs preliminary detection at higher levels of the multilevel structure using larger search radii to identify candidate symbol vectors. These preliminary results guide the subsequent searches at lower levels with smaller search radii, ensuring that the reduced search range at each level still captures the correct symbol vectors.
Solution Approach 2:
The detection algorithm uses feedback from higher-level detection results to adjust and constrain the search at lower levels. The candidate symbol vectors identified at level l inform the search radius and candidate selection at level l-1, creating a feedback mechanism that maintains detection accuracy despite reduced search ranges at each individual level.
Data Source
AI summary
A MIMO detection method for a receiver in a MIMO system using N-QAM for modulation, the MIMO detection method including generating a plurality of symbol vector sets and a plurality of search radiuses, selecting a candidate symbol vector set corresponding to a highest level of a multilevel structure of N-QAM constellation, generating a search space corresponding to a lower level of the multilevel structure of N-QAM constellation according to the selected candidate symbol vector set, confirming which level the search space corresponds to, and generating a detection signal according to the search space when the level of the search space is the lowest level of the multilevel structure of the N-QAM constellation.