Pattern-Dependent Noise Predictive Belief Propagation

In this paper, we introduce iterative pattern-dependent noise prediction for belief propagation (BP) channel detectors over intersymbol interference channels with correlated noise. The new scheme, called pattern-dependent noise predictive belief propagation, makes use of factor graphs, and it iteratively whitens the noise samples by modifying the edge probability computation of the BP algorithm. To illustrate the benefits of the proposed scheme, specifically, we consider longitudinal recording channels corrupted by the data-dependent media noise. Simulation results and convergence behavior analysis show that the proposed detector leads to significant performance improvements especially when the media noise level is high