Decoding of MIMO Systems with Hypothesis Testing Technique

M.A. Jeong, D. Kim, J. Oh, H. Yuxi, H.-K. Min, and I. Song (Korea)


MIMO, tree search, metric-first search, near ML


In this paper, we propose a near maximum likelihood (ML) decoding scheme for multiple input multiple out put (MIMO) systems. Based on the multiple hypothesis testing problem, the proposed decoding scheme provides a higher efficiency than other conventional near ML decod ing schemes by using some characteristics of the channel matrix. Numerical results show that, despite the proposed scheme has a lower computational complexity than other near ML decoders, the performance difference between the ML and proposed scheme is negligibly small.

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