A new soft-output detection method for the magnetic recording channel

Abstract − We develop a new method for simple soft-output detection. Like other soft-output detectors, our approach also utilizes the trellis structure. However, in computing the state transition probabilities in the trellis, we model each bit as a linear regression of the channel outputs as well as the previous bits implied in the corresponding branch. The trellis depends not on the channel response but on the regression model parameter that is highly flexible. The resulting soft-output algorithm has a very simple structure, needing only a forward recursion. The amount of memory required is also small. Furthermore, this algorithm can easily handle colored/signaldependent noise. We examine the performance of this algorithm for three ISI channels with different noise characteristics: additive white, colored, and colored and signal-dependent. To verify the quality of the soft decisions, an outer low-density parity check code is used. Results show that the proposed detector allows favorable complexity/performance tradeoffs in correlated/signal-dependent noise environment.