Deep Neural Network Media Noise Predictor Turbo-detection System for One and Two Dimensional High-Density Magnetic Recording

Abstract

The hard disk drive (HDD) industry is facing a physical limit on the areal density (AD) of one-dimensional magnetic recording (1DMR) on traditional magnetic media. To increase capacity without media redesign, twodimensional magnetic recording (TDMR) has been introduced. The effective channel model has a media noise term which models signal dependent noise due to, e.g., magnetic grains intersected by bit boundaries. Trellis based detection with pattern dependent noise prediction (PDNP) [1] is standard practice in HDDs. The trellis detector sends soft coded bit estimates to a channel decoder, which outputs user information bit estimates. PDNP uses a relatively simple autoregressive noise model and linear prediction; this model is somewhat restrictive and may not accurately represent the media noise, especially at high storage densities. To address this modeling problem, we design and train deep neural network (DNN) based media noise predictors. As DNN [2] models are more general than autoregressive models, they more accurately model media noise compared to PDNP. The proposed turbo detector assumes a channel model for the k th linear equalizer filter output y(k):