Iterative detection for partial response magnetic recording channels-a graphical view

In this paper, we compare detection algorithms for additive white Gaussian noise (AWGN) channels and correlated/signal-dependent noise channels for a serially concatenated recording system from the viewpoint of probability propagation. It has been proven that the classical turbo decoding algorithm is an instance of Pearl´s belief propagation algorithm, and the decoding algorithm for serially concatenated convolutional codes also can be derived from a belief propagation viewpoint. This result is also valid for correlated/signal-dependent noise channels, such as magnetic recording channels. Using the relationship of probability dependency, Bayesian networks and their corresponding probability propagation schemes for a rate-16/17 serially concatenated recording system are obtained for both AWGN and correlated/signal-dependent noise channels. Noise predictive turbo systems (NPTS) are also discussed from a graphical perspective