Reduced cluster search ML decoding for QO-STBC M-QAM MIMO systems

Abstract

Since the maximum likelihood (ML) decoding for quasi-orthogonal space-time block codes (QO-STBC) with inner and outer codes multipleinputmultiple- output (MIMO) antenna systems results too complex when the modulation order index and the number of receive antennas increases, an efficient reduced complexity ML-based decoding scheme is proposed in this contribution, aiming to reach promising QO-STBC coded MIMO system throughput × complexity trade-off. Under high-order modulation indexes (16 ≤ M)-QAM, 4 × nR antennas, with nR ≥ 1, this work proposes a reduced cluster search ML decoder (RCS-ML) and compares the performancecomplexity with the ML decoding approach. Numerical results have indicated no degradation in performance and an increasing reduction in the complexity of RCS-ML decoder when the modulation order increases, been 12.5% of ML decoding complexity for 16-QAM, and <1% for 256-QAM.