T. An, I. Song, J. Oh, H.G. Kang, and S.R. Park (Korea)
multiple input multiple output system, tree search, metric first search, Schnorr-Euchner enumeration, near ML
In this paper, a near maximum likelihood (ML) scheme is proposed for the decoding of multiple input multiple output systems. By employing Schnorr-Euchner enumer ation as well as the metric-first search and comparing the branch length with a threshold, the proposed scheme pro vides a higher efficiency than other conventional near ML decoding schemes. Simulation results show that the pro posed scheme provides lower computational complexity than other near ML decoders while maintaining the bit er ror rate very close to the ML performance.
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