A NEAR ML DECODING SCHEME BASED ON THE METRIC-FIRST SEARCH FOR MULTIPLE INPUT MULTIPLE OUTPUT SYSTEMS

In this paper, a near maximum likelihood (ML) scheme is proposed for the decoding of multiple input multiple output systems. By employing Schnorr-Euchner enumeration as well as the metric-first search and comparing the branch length with a threshold, the proposed scheme provides a higher efficiency than other conventional near ML decoding schemes. Simulation results show that the proposed scheme provides lower computational complexity than other near ML decoders while maintaining the bit error rate very close to the ML performance.