Reduced Substreams Maximum Likelihood Decoder for MIMO Systems
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Solution Overview
Problem
MIMO communication systems face computational complexity in maximum likelihood decoding, making real-time symbol estimation challenging, while suboptimal decoders trade complexity for performance, increasing symbol error probability.
Innovation Solution
A greater likelihood decoder is introduced, comprising a suboptimal decoder, weakest substreams decision logic, and subspace search logic, which analyzes received symbol vectors to generate substream indicators, selects the weakest substreams, and derives a reduced substreams maximum likelihood (RSML) decoded symbol vector from a subspace of candidates, reducing computational burden while approximating ML decoding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maximum likelihood decoding is used, then symbol estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the full search space of all possible symbol vectors into multiple subspaces, each corresponding to different transmit antennas. The decoder evaluates candidates within each subspace separately and combines results, rather than searching the entire space at once. This segmentation reduces the computational complexity from exponential in the total number of antennas to a more manageable level while maintaining good symbol estimation accuracy.
Solution Approach 2:
The patent applies local quality by focusing computational resources on evaluating candidates within localized subspaces rather than uniformly across the entire search space. Each subspace evaluation is performed with high quality (comparing all candidates within that subspace), while the overall complexity is controlled by the localized nature of each evaluation. This allows the system to achieve good performance with reduced complexity.
2Device complexity
If suboptimal decoding is used, then computational complexity is reduced, but symbol error probability increases
Solution Approach 1:
The patent incorporates feedback by using the results from subspace evaluations to guide the final symbol decision. The decoder evaluates candidates in each subspace, collects the best candidates from each evaluation, and uses this feedback information to make the final symbol estimation. This feedback mechanism allows the system to achieve better performance than simple suboptimal decoders while maintaining reduced computational complexity.
Solution Approach 2:
The patent applies partial action by evaluating a subset of candidate symbol vectors rather than all possible candidates. Specifically, it evaluates candidates within each antenna subspace and selects the best ones, rather than exhaustively searching the entire space. This partial evaluation approach reduces complexity while maintaining reliability by focusing computational resources on the most promising candidates.
3Reliability
If full maximum likelihood search space is evaluated, then decoding performance is optimized, but processing time increases
Solution Approach 1:
The patent segments the exhaustive search into multiple smaller subspace evaluations that can be performed in parallel or sequence with reduced time each. By dividing the search space into antenna-specific subspaces and evaluating them separately, the system reduces the time required for each evaluation while maintaining overall decoding performance through the combination of subspace results.
Solution Approach 2:
The patent performs preliminary evaluation of candidates within each subspace before making the final symbol decision. By pre-evaluating and ranking candidates within each antenna subspace, the system identifies the most promising candidates early, allowing for faster final decision-making without sacrificing decoding performance.
Data Source
AI summary
A greater likelihood decoder, a method of deriving a reduced substreams maximum likelihood (RSML) decoded symbol vector and a multiple-input, multiple-output (MIMO) receiver incorporating the decoder or the method. In one embodiment, the decoder includes: (1) a suboptimal decoder that analyzes a received symbol vector to generate substream indicators and a decoded symbol vector estimate, (2) weakest substreams decision logic, coupled to the suboptimal decoder, that receives the substream indicators and selects weakest ones thereof and (3) subspace search logic, coupled to the suboptimal decoder and the weakest substreams decision logic, that further selects a reduced substreams maximum likelihood (RSML) decoded symbol vector from a subspace of decoded symbol vector candidates derived from the decoded symbol vector estimate and the weakest ones.