Equalization with Expectation Propagation at Smoothing Level

TL;DR

This paper introduces a turbo equalizer using expectation propagation at the smoothing level, achieving better performance and lower complexity than Kalman smoothers, especially for high-order modulations and large memory channels.

Contribution

The paper applies expectation propagation at the smoothing stage in turbo equalization, enhancing performance and computational efficiency over existing EP-based methods.

Findings

Outperforms Kalman smoother in simulations

Reduces computational complexity

Speeds up convergence in turbo equalization

Abstract

In this paper we propose a smoothing turbo equalizer based on the expectation propagation (EP) algorithm with quite improved performance compared to the Kalman smoother, at similar complexity. In scenarios where high-order modulations or/and large memory channels are employed, the optimal BCJR algorithm is computationally unfeasible. In this situation, low-cost but suboptimal solutions, such as the linear minimum mean square error (LMMSE), are commonly used. Recently, EP has been proposed as a tool to improve the Kalman smoothing performance. In this paper we review these solutions to apply the EP at the smoothing level, rather than at the forward and backwards stages. Also, we better exploit the information coming from the channel decoder in the turbo equalization schemes. With these improvements we reduce the computational complexity, speed up convergence and outperform previous approaches. We included some simulation results to show the robust behavior of the proposed method regardless of the scenario, and its improvement in terms of performance in comparison with other EP-based solutions in the literature.