Iterative Equalization with Decision Feedback based on Expectation Propagation

TL;DR

This paper presents an iterative decision feedback equalizer using expectation propagation that outperforms existing methods at high spectral efficiency, offering improved convergence and reduced complexity for single carrier modulations.

Contribution

It introduces a novel iterative DFE-IC scheme based on expectation propagation, significantly enhancing performance over traditional turbo linear equalizers and variants.

Findings

Outperforms turbo LE-IC at high spectral efficiency

Achieves higher data rates with better convergence

Reduces computational complexity with efficient matrix inversion

Abstract

This paper investigates the design and analysis of minimum mean square error (MMSE) turbo decision feedback equalization (DFE), with expectation propagation (EP), for single carrier modulations. Classical non iterative DFE structures have substantial advantages at high data rates, even compared to turbo linear equalizers - interference cancellers (LE-IC), hence making turbo DFE-IC schemes an attractive solution. In this paper, we derive an iterative DFE-IC, capitalizing on the use of soft feedback based on expectation propagation, along with the use of prior information for improved filtering and interference cancellation. This DFE-IC significantly outperforms exact turbo LE-IC, especially at high spectral efficiency, and also exhibits various advantages and performance improvements over existing variants of DFE-IC. The proposed scheme can also be self-iterated, as done in the recent trend on EP-based equalizers, and it is shown to be an attractive alternative to linear self-iterated receivers. For time-varying (TV) filter equalizers, an efficient matrix inversion scheme is also proposed, considerably reducing the computational complexity relative to existing methods. Using finite-length and asymptotic analysis on a severely selective channel, the proposed DFE-IC is shown to achieve higher rates than known alternatives, with better waterfall thresholds and faster convergence, while keeping a similar computational complexity.