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Feedback Semi-Definite Relaxation for near-Maximum Likelihood Detection in MIMO Systems  

Park, Su-Bin (시립인천대학교 전자공학과 신호처리연구실)
Lee, Dong-Jin (시립인천대학교 전자공학과 신호처리연구실)
Byun, Youn-Shik (시립인천대학교 전자공학과)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.33, no.12C, 2008 , pp. 1082-1087 More about this Journal
Abstract
Maximum Likelihood (ML) detection is well known to exhibit better bit-error-rate (BER) than many other detectors for multiple-input multiple-output (MIMO) channel. However, ML detection has been shown a difficult problem due to its NP-hard problem. It means that there is no known algorithm which can find the optimal solution in polynomial-time. In this paper, Semi-Definite relaxation (SDR) is iteratively applied to ML detection problem. The probability distribution can be obtained by survival eigenvector out of the dominant eigenvalue term of the optimal solution. The probability distribution which is yielded by SDR is recurred to the received signal. Our approach can reach to nearly ML performance.
Keywords
MIMO; Semi-Definite Relaxation; Maximum Likelihood Detection;
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