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

Autor:	Schmid, Luca, Raviv, Tomer, Shlezinger, Nir, Schmalen, Laurent
Rok vydání:	2024
Předmět:	Computer Science - Information Theory Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Submitted to IEEE for peer review
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.12627 Zobrazit plný text záznamu View this record from Arxiv