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Lattice reduction (LR) has been researched a lot in conjunction with ordered successive interference cancellation (OSIC), contributing large enhancement of the performance with relatively small complexity. However, the performance gap between the LR-aided OSIC (LR-OSIC) and maximum likelihood (ML) still exists. This paper suggests lattice reduction aided all- ordering SIC (LR-AOSIC) as an optimum...
In this paper, we propose an improved MIMO successive interference cancellation (SIC) detector taking maximum likelihood (ML) criterion into account. Applying the ML criterion to multiple candidates obtained from all possible orderings of streams in the SIC, the proposed method can provide improved performance with complexity comparable to conventional ordered SIC for moderate number of spatial streams
This paper proposes a dimension reduction maximum likelihood (ML) soft demodulator for multiple-input multiple-output (MIMO) system. The proposed dimension reduction soft demodulator reduces the dimension of the search space for minimum Euclidean distance calculation by exploiting ML hard detection algorithms and can achieve significantly lower complexity than existing optimal soft demodulators based...
In this letter, we propose a soft-decoding ML MIMO demodulation method that lowers the complexity of the existing ML method. The proposed method finds the nearest point without calculating the Euclidean distance by exploiting the regular structure of bit-to-symbol mapping. It enables significant reduction of the number of distance calculations which is a major complexity burden.
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