Parametric Phase Tracking via Expectation Propagation

TL;DR

This paper introduces simple, efficient algorithms for phase noise mitigation in single-carrier signals using parametric message passing with Tikhonov distributions, significantly improving phase tracking performance without decoder feedback.

Contribution

It develops expectation propagation-based algorithms for one-shot phase tracking, enhancing prior CBC-inspired methods and enabling joint decoding integration.

Findings

Improved phase tracking accuracy before decoder feedback.

Reduced computational complexity via parametric message passing.

Potential for integration into joint decoding schemes.

Abstract

In this work we propose simple algorithms for signal detection in a single-carrier transmission corrupted by a strong phase noise. The proposed phase tracking algorithms are formulated within the framework of a parametric message passing (MP) which reduces the complexity of the Bayesian inference by using distributions from a predefined family; here, of Tikhonov distributions. This stays in line with previous works mainly inspired by the well-known Colavolpe-Barbieri-Caire (CBC) algorithm which gained popularity due to its simplicity and possibility for decoder-aided operation. In our work we mainly focus on practically relevant case of one-shot phase tracking that does not require decoder's feedback. Applying the principles of the expectation propagation (EP), we notably improve the performance of the phase tracking before the decoder's feedback can be even considered. The EP algorithms can be also integrated in the decoding loop in the spirit of joint decoding and phase tracking.