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

Abstract

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.