Signal processing for two dimensional magnetic recording using Voronoi model averaged statistics

This paper considers a signal processing system for two-dimensional magnetic recording (TDMR) employing a random Voronoi grain model. The channel model also includes two-dimensional intersymbol interference (2D-ISI) and additive white Gaussian noise. The system uses a 2D-ISI BCJR detector and irregular repeat-accumulate (IRA) code decoder in a turbo-equalization approach. In order to transfer soft information to the IRA decoder a mapping function based on the 2D-ISI detector soft information statistics is used. Simulations employing the perturbed-bit-centers Voronoi grain model proposed in a previous paper by Hwang et al.

show that the proposed system achieves a 6.5% increase in user bits/grain (U/G) and an 11.7 dB SNR gain compared to Hwang et al. Simulation results also indicate that our random Voronoi model is harder to equalize than the Hwang Voronoi model. For the random Voronoi model considered in this paper, the proposed system achieves a density of 0.4422 U/G, corresponding to an areal density of about 8.8 Terabits/in2 on typical magnetic hard disks; this is nearly an order of magnitude better than the best commercially available systems.