Approximate ML Decision Feedback Block Equalizer for Doubly Selective Fading Channels

TL;DR

This paper introduces an approximate ML decision feedback block equalizer for doubly selective fading channels, achieving near-MLSE performance with lower complexity and full multipath diversity.

Contribution

The paper proposes a novel approximate ML decision feedback equalizer that balances performance and complexity for doubly selective channels.

Findings

Achieves near-MLSE performance

Outperforms MMSE-based detectors

Provides full multipath diversity

Abstract

In order to effetively suppress intersymbol interference (ISI) at low complexity, we propose in this paper an approximate maximum likelihood (ML) decision feedback block equalizer (A-ML-DFBE) for doubly selective (frequency-selective, time-selective) fading channels. The proposed equalizer design makes efficient use of the special time-domain representation of the 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 performance close to maximum likelihood sequence estimation (MLSE), and significantly outperforms the minimum mean square error (MMSE) based detectors; 2) It has substantially lower complexity than the conventional equalizers; 3) It easily realizes the 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 behaviour of the A-ML-DFBE, and it 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.