We present a low-complexity maximum likelihood (ML) detector for a coded double space-time transmit diversity-orthogonal frequency division multiplexing (DSTTD-OFDM) system. The proposed ML detector exploits properties of two permuted equivalent channel matrices and multiple decision-feedback (DF) detections. This can reduce computational efforts from O(|A|4) to O(2|A|2) with maintaining ML performance, where |A| is the modulation order. Numerical results shows that the proposed ML detector obtains ML performance and requires remarkably lower computational loads compared with the conventional ML detector.

This letter presents a new vertical Bell Labs layered space-time (V-BLAST) transmission scheme for developing low-complexity tree searching in the QRD-M algorithm. In the new V-BLAST system, we assign modulation scheme in ascending order from top to bottom tree branches. The modulation set to be assigned is decided by two criteria: minimum performance loss and maximum complexity reduction. We also propose an open-loop power allocation algorithm to surmount the performance loss. Numerical results show that the proposed V-BLAST transmission approach can significantly reduce the computational loads of the QRD-M algorithm with a slight performance degradation.

This letter deals with computationally efficient maximum-likelihood (ML) detection for the quasi-orthogonal space-time block code (QOSTBC) with four transmit antennas. The proposed ML detector uses a permutation based real-valued equivalent channel matrix representation. As a result, the complexity of ML detection problem is moderated from O(2|A|2) to O(4|A|), where |A| is modulation order. Numerical results show that the proposed ML detector provides ML performance and achieves greatly high computational savings.

This letter introduces an efficient near-maximum likelihood (ML) detector for a coded double space-time transmit diversity-orthogonal frequency division multiplexing (DSTTD-OFDM) system. The proposed near-ML detector constructs a candidate vector set through a relaxed minimization method. It reduces computational loads from O(2|A|2) to O(|A|2), where |A| is the modulation order. Numerical results indicate that the proposed near-ML detector provides both almost ML performance and considerable complexity savings.

This letter proposes two efficient decision-feedback (DF) detection schemes for space-time block code (STBC) over time-selective fading channels. The existing DF detection causes error propagation when the first symbol is not detected correctly. However, the proposed detection schemes provide two candidates according to a channel gain or an average log-likelihood ratio (LLR) based selection rule and choose a better candidate for the first symbol. Simulation results show that the proposed detection schemes reduce error propagation and yield significant signal-to-noise ratio (SNR) gain with moderate complexity, compared to the existing DF detection scheme.