Near ML Decoding Based on Metric-First Searching and Branch Length Threshold for Multiple Input Multiple Output Systems 


Vol. 34,  No. 8, pp. 830-839, Aug.  2009


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  Abstract

In this paper, we address a near maximum likelihood (ML) scheme for the decoding of multiple input multiple output systems. Based on the metric-first search method and by employing Schnorr-Euchner enumeration and branch length thresholds, the proposed scheme provides reduced computational complexity. The proposed scheme is shown by simulation to have lower computational complexity than other near ML decoders while maintaining the bit error rate very close to the ML performance.

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  Cite this article

[IEEE Style]

T. An, H. G. Kang, J. Oh, I. Song, S. Yoon, "Near ML Decoding Based on Metric-First Searching and Branch Length Threshold for Multiple Input Multiple Output Systems," The Journal of Korean Institute of Communications and Information Sciences, vol. 34, no. 8, pp. 830-839, 2009. DOI: .

[ACM Style]

Taehun An, Hyun Gu Kang, Jongho Oh, Iickho Song, and Seokho Yoon. 2009. Near ML Decoding Based on Metric-First Searching and Branch Length Threshold for Multiple Input Multiple Output Systems. The Journal of Korean Institute of Communications and Information Sciences, 34, 8, (2009), 830-839. DOI: .

[KICS Style]

Taehun An, Hyun Gu Kang, Jongho Oh, Iickho Song, Seokho Yoon, "Near ML Decoding Based on Metric-First Searching and Branch Length Threshold for Multiple Input Multiple Output Systems," The Journal of Korean Institute of Communications and Information Sciences, vol. 34, no. 8, pp. 830-839, 8. 2009.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880