Blind Channel Estimation and Joint Symbol Detection with Data-Driven
  Factor Graphs
Luca Schmid Tomer Raviv Nir Shlezinger Laurent Schmalen
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.