Approximate ML Decision-Feedback Block Equalizer for Doubly Selective Fading Channels

To effectively suppress intersymbol interference (ISI) at low complexity, in this paper, we propose an approximate maximum-likelihood decision-feedback block equalizer (A-ML-DFBE) for doubly selective (frequency- and time-selective) fading channels. The proposed equalizer design makes efficient use of the special time-domain representation of multipath channels through a matched filter, a sliding window, a Gaussian approximation, and a decision feedback. The A-ML-DFBE has the following features: 1) It achieves a performance that is close that to that of maximum-likelihood sequence estimation (MLSE) and significantly outperforms minimum mean square error (MMSE)-based detectors. 2) It has substantially lower complexity than conventional equalizers. 3) It easily realizes complexity and performance tradeoff by adjusting the length of the sliding window. 4) It has a simple and fixed-length feedback filter. The symbol error rate (SER) is derived to characterize the behavior of the A-ML-DFBE and can also be used to find the key parameters of the proposed equalizer. In addition, we further prove that the A-ML-DFBE obtains full multipath diversity.