Deep Neural Network a Posteriori Probability Detector for Two-Dimensional Magnetic Recording

In two-dimensional magnetic recording (TDMR) channels, intersymbol interference (within and between tracks) and pattern-dependent media noise are impediments to reaching higher areal density. We propose a novel deep neural network (DNN)-based a posteriori probability (APP) detection system with para...

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Bibliographic Details
Published in:	IEEE transactions on magnetics Vol. 56; no. 6; pp. 1 - 12
Main Authors:	Shen, Jinlu, Aboutaleb, Ahmed, Sivakumar, Krishnamoorthy, Belzer, Benjamin J., Chan, Kheong Sann, James, Ashish
Format:	Journal Article
Language:	English
Published:	New York IEEE 01-06-2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Artificial neural networks Bit error rate Channel detection Channels Computer architecture Computer simulation convolutional neural network (CNN) Decoding deep learning Density Detectors Equalizers Error analysis grain-flipping-probability (GFP) model Iterative methods long short-term memory (LSTM) Magnetic recording Magnetism Media Neural networks Noise prediction recurrent neural network Statistical analysis Two dimensional displays two-dimensional magnetic recording (TDMR) Viterbi algorithm detectors
Online Access:	Get full text
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