Deep Neural Network-Based Detection and Partial Response Equalization for Multilayer Magnetic Recording

Ahmed Aboutaleb, Amirhossein Sayyafan, Krishnamoorthy Sivakumar, Benjamin Belzer, Simon Greaves, Kheong Sann Chan, Roger Wood

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


To increase the storage capacity limit of magnetic recording channels, recent studies proposed multilayer magnetic recording (MLMR): the vertical stacking of magnetic media layers. MLMR readback waveforms consist of the superposition of signals from each layer recovered by a read head placed above the upper layer. This article considers the problem of equalization and detection for MLMR comprising two layers. To this end, we use MLMR waveforms generated using a grain switching probability (GSP) model that is trained on realistic micromagnetic simulations. We propose three systems for equalization and detection. The first is a convolutional neural network (CNN) equalizer followed by an MLMR Viterbi algorithm (VA) for detection. We show that this system outperforms the traditional 2-D linear minimum mean squared error (2-D-LMMSE) equalizer. The second system uses CNNs for equalization and separation of signals from each layer, which is followed by a regular VA. The third system contains CNNs trained to directly provide soft bit estimates. By interfacing the CNN detector with a channel decoder, we show that an areal density gain of 16.2% can be achieved by a two-layer MLMR system over a one-layer system.

Original languageEnglish
Article number9261378
JournalIEEE Transactions on Magnetics
Issue number3
Publication statusPublished - 2021 Mar


  • Convolutional neural network (NN) (CNN)
  • detection
  • dual-layer recording
  • multilayer magnetic recording (MLMR)
  • partial response equalization
  • two-dimensional magnetic recording (TDMR)
  • Viterbi algorithm (VA)


Dive into the research topics of 'Deep Neural Network-Based Detection and Partial Response Equalization for Multilayer Magnetic Recording'. Together they form a unique fingerprint.

Cite this