Turbo-detection for Multilayer Magnetic Recording Using Deep Neural Network-based Equalizer and Media Noise Predictor

Amirhossein Sayyafan, Ahmed Aboutaleb, Benjamin J. Belzer, Krishnamoorthy Sivakumar, Simon Greaves, Kheong Sann Chan

Research output: Contribution to journalArticlepeer-review

Abstract

This paper considers deep neural network (DNN) based turbo-detection for multilayer magnetic recording (MLMR), an emerging hard disk drive (HDD) technology that employs vertically stacked magnetic media layers with readers above the top-most layer. The proposed system employs 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 2.6561 Terabits per square inch (Tb/in2).

Original languageEnglish
JournalIEEE Transactions on Magnetics
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • BCJR detector
  • CNN equalizer-separator
  • CNN media noise predictor
  • convolutional neural network (CNN)
  • Convolutional neural networks
  • Deep neural network (DNN)
  • Detectors
  • Equalizers
  • LDPC decoder
  • Magnetic recording
  • Media
  • Multilayer magnetic recording (MLMR)
  • Predictive models
  • Target tracking
  • turbo-detector

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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