Near ML decoding based on metric-first searching and branch length threshold for multiple input multiple output systems

Abstract

In this thesis, we propose 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 technique provides a higher efficiency than other conventional near ML decoding schemes. From simulation results, it is confirmed that the proposed scheme has lower computational complexity than other near ML decoders while maintaining the bit error rate 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.