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


Vol. 33,  No. 12, pp. 1082-1087, Dec.  2008


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  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.

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  Cite this article

[IEEE Style]

S. Park, D. Lee, Y. Byun, "Feedback Semi-Definite Relaxation for near-Maximum Likelihood Detection in MIMO Systems," The Journal of Korean Institute of Communications and Information Sciences, vol. 33, no. 12, pp. 1082-1087, 2008. DOI: .

[ACM Style]

Su-Bin Park, Dong-Jin Lee, and Youn-Shik Byun. 2008. Feedback Semi-Definite Relaxation for near-Maximum Likelihood Detection in MIMO Systems. The Journal of Korean Institute of Communications and Information Sciences, 33, 12, (2008), 1082-1087. DOI: .

[KICS Style]

Su-Bin Park, Dong-Jin Lee, Youn-Shik Byun, "Feedback Semi-Definite Relaxation for near-Maximum Likelihood Detection in MIMO Systems," The Journal of Korean Institute of Communications and Information Sciences, vol. 33, no. 12, pp. 1082-1087, 12. 2008.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880