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

• URL: http://arxiv.org/abs/2401.12627v1
• Date: Tue, 23 Jan 2024 10:26:15 GMT
• Title: Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs
• Authors: Luca Schmid, Tomer Raviv, Nir Shlezinger, Laurent Schmalen
• Abstract summary: We investigate the application of the framework for blind joint channel estimation and symbol detection on time-variant inter-symbol interference channels. We address the issue by efficiently approximating the posterior propagations using the belief parameter (BP) algorithm on a suitable factor graph. In addition, we propose a data-driven version of our algorithm that introduces momentum in BP updates and learns a suitable EM update schedule. 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.
• Score: 31.8050631698684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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