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

An, Tae-Hun (한국과학기술원 전기및전자공학과)
Kang, Hyun-Gu (한국과학기술원 전기및전자공학과)
Oh, Jong-Ho (한국과학기술원 전기및전자공학과)
Song, Iick-Ho (한국과학기술원 전기및전자공학과)
Yoon, Seok-Ho (성균관대학교 정보통신공학부)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.34, no.8C, 2009 , pp. 830-839 More about this Journal
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 close to the ML performance.
Keywords
multiple input multiple; output; near maximum likelihood decoder; metric-first search; branch length threshold; Schnorr-Euchner enumeration;
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Times Cited By KSCI : 2  (Citation Analysis)
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