Decoding With Expected Length and Threshold Approximated (DELTA): A Near-ML Scheme for Multiple-Input-Multiple-Output Systems

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In this paper, we propose a near maximum likelihood (ML) scheme for the decoding of multiple-input-multiple-output (MIMO) systems. By employing the metric-first search method, Schnorr-Euchner enumeration, and branch-length thresholds in a single frame systematically, the proposed technique provides efficiency that is higher than those of other conventional near-ML decoding schemes. From simulation results, it is confirmed that the proposed scheme has computational complexity lower than those of other near-ML decoders while maintaining the bit error rate (BER) very close to the ML performance. The proposed scheme, in addition, possesses the capability of allowing flexible tradeoffs between the computational complexity and BER performance.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2009-09
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.58, no.7, pp.3234 - 3246

ISSN
0018-9545
DOI
10.1109/TVT.2009.2013867
URI
http://hdl.handle.net/10203/100052
Appears in Collection
EE-Journal Papers(저널논문)
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