A study on modeling of the writing process and two-dimensional neural network equalization for two-dimensional magnetic recording

A simple writing process considering magnetic clusters due to exchange coupling between grains is studied for two-dimensional magnetic recording. The bit error rate (BER) performance of a low-density parity-check coding and iterative decoding system with a two-dimensional neural network equalizer (2D-NNE) that can diminish the influences of jitter-like medium noise and inter-track interference is obtained using a read/write channel model based on the proposed writing process, and it is compared with those for one- and two-dimensional finite impulse response equalizers (FIREs). It is clarified that the BER performance for the 2D-NNE is far superior to those for the FIREs.