A generalized simplified ML decoder of orthogonal space-time block code for wireless communications over time-selective fading channels

We propose a novel generalized simplified maximum-likelihood (ML) decoder of orthogonal space-time block code (OSTBC) for wireless communications over time-selective fading channels. The proposed decoder computes the decision statistics based on the channel state information (CSI) and completely removes the inter-transmit-antenna interference (ITAI) and provides diversity advantage when the channel varies from one signaling interval to another. It is shown that when the channel is quasi-static, the proposed decoder becomes the optimum ML decoder for OSTBC. We derive a tight theoretical upper bound for bit error probability of the proposed decoder. We show theoretically that the proposed decoder does not exhibit error floors at high signal-to-noise ratios (SNR). Simulation results for various channel-fading rates are presented to verify our theoretical analysis and demonstrate robust performance of the proposed decoder.