R&D: Deep Neural Network-Based Detection and Partial Response Equalization for Multilayer Magnetic Recording

IEEE Transactions on Magnetics has published an article written by Ahmed Aboutaleb, Amirhossein Sayyafan, Krishnamoorthy Sivakumar, Benjamin Belzer, School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA, Simon Greaves, Research Institute of Electrical Communication (RIEC), Tohoku University, Sendai, Japan, Kheong Sann Chan, Nanjing Institute of Technology, Nanjing, China, and Roger Wood, School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA.

Abstract: “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.“