Nonuniform Data-Dependent Noise Prediction in Magnetic Recording

In this paper, we report a method for nonuniform data-dependent noise prediction (DDNP), in which the noise predictive filters do not have an identical order (number of taps). To accommodate this new architecture, we implemented a partial decision feedback strategy for branch metric computations of the higher order predictive filters. Theoretical analysis and computer simulations show that our proposed DDNP can achieve a better tradeoff between detection performance and computational complexity compared with the previously reported DDNP