A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization and Decoding

TL;DR

This paper introduces an iterative receiver combining belief propagation, mean field, and expectation propagation for joint phase noise estimation, equalization, and decoding, outperforming traditional linearization methods.

Contribution

It proposes a novel approach using second-order Taylor approximation within MF to improve nonlinear phase noise handling in iterative receivers.

Findings

Significant performance improvement over soft-in EKS.

Effective low-complexity implementation.

Enhanced nonlinear phase noise estimation.

Abstract

In this work, with combined belief propagation (BP), mean field (MF) and expectation propagation (EP), an iterative receiver is designed for joint phase noise (PN) estimation, equalization and decoding in a coded communication system. The presence of the PN results in a nonlinear observation model. Conventionally, the nonlinear model is directly linearized by using the first-order Taylor approximation, e.g., in the state-of-the-art soft-input extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to handle the factor due to the nonlinear model, and a second-order Taylor approximation is used to achieve Gaussian approximation to the MF messages, which is crucial to the low-complexity implementation of the receiver with BP and EP. It turns out that our approximation is more effective than the direct linearization in the soft-in EKS with similar complexity, leading to significant performance improvement as demonstrated by simulation results.