Convolutional Neural Network-based Media Noise Prediction and Equalization for TDMR Turbo-detection with Write/Read TMR

Abstract

This paper presents a turbo-detection system consisting of a convolutional neural network (CNN) based equalizer, a Bahl-Cocke-Jelinek-Raviv (BCJR) trellis detector, a CNN-based media noise predictor (MNP), and a low-density parity-check (LDPC) channel decoder for two-dimensional magnetic recording (TDMR). The BCJR detector, CNN MNP, and LDPC decoder iteratively exchange soft information to maximize the areal density (AD) subject to a bit error rate (BER) constraint. Simulation results employing a realistic grain switching probabilistic (GSP) media model show that the proposed system is quite robust to track-misregistration (TMR). Compared to a I-D pattern-dependent noise prediction (PDNP) baseline with soft intertrack interference (ITI) subtraction, the system achieves 0.34% AD gain with read-TMR alone and 0.69% with write- and read-TMR together.