Turbo-Detection for Multilayer Magnetic Recording Using Deep Neural Network-Based Equalizer and Media Noise Predictor

Abstract

This article considers deep neural network (DNN)-based turbo-detection for multilayer magnetic recording (MLMR), an emerging hard disk drive (HDD) technology that uses vertically stacked magnetic media layers with readers above the top-most layer. The proposed system uses two layers with two upper layer tracks and one lower layer track. The reader signals are processed by convolutional neural networks (CNNs) to separate the upper and lower layer signals and equalize them to 2-D and 1-D partial response (PR) targets, respectively. The upper and lower layer signals feed 2-D and 1-D Bahl–Cocke–Jelinek–Raviv (BCJR) detectors, respectively. The detectors’ soft outputs feed a multilayer CNN-based media noise predictor whose predicted noise outputs are fed back to the BCJR equalizers to reduce their bit error rates (BERs). The BCJR equalizers also interface with low-density parity-check (LDPC) decoders. Additional BER reductions are achieved by sending soft-information from the upper layer BCJR to the lower layer BCJR. Simulations of this turbo-detection system on a two-layer MLMR signal generated by a grain-switching-probabilistic (GSP) media model show density gains of 11.32% over a comparable system with no lower layer and achieve an overall density of 2.6551 terabits per square inch (Tb/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ).