For maximum-likelihood (ML) estimation to jointly track carrier frequency offset (CFO) and channel impulse response (CIR) in orthogonal frequency division multiplexing (OFDM) systems, we present a finite high order approximation method utilizing the MATLAB ‘roots' command on the log-likelihood function derived from the OFDM received signal, coupled with an adaptive iteration algorithm. The tracking performance of this high order approximation algorithm is found to be excellent, and as expected, the algorithm outperforms the other existing first order approximation algorithms.

The probability of making mistakes on the decoded signals at the relay has been used for the maximum-likelihood (ML) decision at the receiver in the decode-and-forward (DF) relay network. It is well known that deriving the probability is relatively easy for the uncoded single-antenna transmission with M-pulse amplitude modulation (PAM). However, in the multiplexing multiple-input multiple-output (MIMO) transmission, the multi-dimensional decision region is getting too complicated to derive the probability. In this paper, a high-performance near-ML decoder is devised by applying a well-known pairwise error probability (PEP) of two paired-signals at the relay in the MIMO DF relay network. It also proves that the near-ML decoder can achieve the maximum diversity of MSMD+MR min (MS,MD), where MS, MR, and MD are the number of antennas at the source, relay, and destination, respectively. The simulation results show that 1) the near-ML decoder achieves the diversity we derived and 2) the bit error probability of the near-ML decoder is almost the same as that of the ML decoder.

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.

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.