Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs

TL;DR

This paper presents a data-driven factor graph approach for blind joint channel estimation and symbol detection in time-variant channels, combining EM and belief propagation to improve performance and reduce complexity.

Contribution

It introduces a novel data-driven algorithm that integrates momentum and learning strategies into BP and EM, enhancing detection accuracy with minimal offline training.

Findings

Outperforms coherent BP detection at high SNR

Reduces detection complexity to a single BP iteration per EM step

Achieves better performance with few offline training samples

Abstract

We investigate the application of the factor graph framework for blind joint channel estimation and symbol detection on time-variant linear inter-symbol interference channels. In particular, we consider the expectation maximization (EM) algorithm for maximum likelihood estimation, which typically suffers from high complexity as it requires the computation of the symbol-wise posterior distributions in every iteration. We address this issue by efficiently approximating the posteriors using the belief propagation (BP) algorithm on a suitable factor graph. By interweaving the iterations of BP and EM, the detection complexity can be further reduced to a single BP iteration per EM step. In addition, we propose a data-driven version of our algorithm that introduces momentum in the BP updates and learns a suitable EM parameter update schedule, thereby significantly improving the performance-complexity tradeoff with a few offline training samples. Our numerical experiments demonstrate the excellent performance of the proposed blind detector and show that it even outperforms coherent BP detection in high signal-to-noise scenarios.