Optimization of Iterative Blind Detection based on Expectation Maximization and Belief Propagation

TL;DR

This paper presents a novel iterative blind detection method combining EM and BP algorithms, reducing computational complexity while maintaining high performance in block-fading channels.

Contribution

It introduces a joint EM-BP scheme with a model-based learning approach for parameter updates, improving efficiency and detection accuracy.

Findings

Reduces EM algorithm's computational burden

Learns effective update schedules via model-based learning

Outperforms coherent BP detection in high SNR scenarios

Abstract

We study iterative blind symbol detection for block-fading linear inter-symbol interference channels. Based on the factor graph framework, we design a joint channel estimation and detection scheme that combines the expectation maximization (EM) algorithm and the ubiquitous belief propagation (BP) algorithm. Interweaving the iterations of both schemes significantly reduces the EM algorithm's computational burden while retaining its excellent performance. To this end, we apply simple yet effective model-based learning methods to find a suitable parameter update schedule by introducing momentum in both the EM parameter updates as well as in the BP message passing. Numerical simulations verify that the proposed method can learn efficient schedules that generalize well and even outperform coherent BP detection in high signal-to-noise scenarios.