Generalized Belief Propagation Detector for TDMR Microcell Model

Abstract

Signal processing in TDMR encounters several challenges such as read channel modeling and detection in the presence of severe two-dimensional intersymbol interference (2-D ISI). The contribution of this paper is twofold. 1) In this paper, we introduce a novel 2-D read channel model which we call the 2-D Microcell model. In this model, we use generalized 2-D microtracks called microcells to captures the properties of irregular grain boundaries of the medium in a relatively simple yet accurate manner. The data dependent noise (DDN) distributions are analytically derived for this model. The derivation of the DDN distributions makes the 2-D Microcell suitable for detector design purposes. 2) We propose a new framework for designing truly two-dimensional detectors for the Microcell model based on near-optimal generalized belief propagation (GBP). The GBP algorithm is purposefully applied for detection in this model in order to handle the data dependent media noise which is caused by irregular bit transitions in both dimensions. Results are provided to show that the incorporation of the DDN distributions into the GBP detection helps improving the detection performance.