Reduced Cluster Search ML Decoding for QO-STBC Systems

Abstract

Since the maximum likelihood (ML) decoding results too complex when the modulation order and the number of receive antennas increase, an efficient reduced complexity ML-based decoding scheme applied to a multiple-input-multiple-output (MIMO) antenna systems with quasi-orthogonal space-time block code (QO-STBC) is proposed, and named reduced cluster search ML decoding (RCS-ML). Its performance and complexity aspects are compared to the conventional ML decoding approach. High-order modulation indexes and short low density parity check codes (LDPC) are considered. Numerical results have indicated no degradation in the performance and an increasing reduction in the complexity of RCS-ML decoding with respect to the conventional ML when the modulation order increases.