A generalized linear quasi-ML decoder of OSTBCs for wireless communications over time-selective fading channels

We propose a novel generalized linear quasi-maximum-likelihood (quasi-ML) decoder for orthogonal space-time block codes (OSTBCs) for wireless communications over time-selective fading channels. The proposed decoder computes the decision statistics based on the channel-state information and completely removes the intertransmit-antenna interference to provide excellent diversity advantage when the channel varies from symbol to symbol. It is shown that when the channel is quasi-static, the proposed decoder is the optimum ML decoder for OSTBCs. The theoretical bit-error probabilities of the proposed decoder are given and it is shown that the proposed decoder does not exhibit error floors at high signal-to-noise ratios like the decoder proposed in and . Simulation results for various channel-fading rates are presented to verify the theoretical analysis.