TY - JOUR
T1 - A perspective on deep neural network-based detection for multilayer magnetic recording
AU - Aboutaleb, Ahmed
AU - Sayyafan, Amirhossein
AU - Sivakumar, Krishnamoorthy
AU - Belzer, Benjamin
AU - Greaves, Simon
AU - Chan, Kheong Sann
AU - Wood, Roger
N1 - Funding Information:
This work was supported by the United States National Science Foundation (NSF) under Grant No. CCF-1817083 and by the Advanced Storage Research Consortium (ASRC).
Publisher Copyright:
© 2021 Author(s).
PY - 2021/7/5
Y1 - 2021/7/5
N2 - This paper describes challenges, solutions, and prospects for data recovery in multilayer magnetic recording (MLMR)—the vertical stacking of magnetic media layers to increase information storage density. To this end, the channel model for MLMR is discussed. Data recovery is described in terms of the readback stage followed by equalization and then detection. We illustrate how deep neural networks (DNNs) can be used to design systems for equalization and detection for MLMR. We show that such DNN-based systems outperform the conventional baseline and provide a good trade-off between complexity and performance. To achieve additional density gains, several prospective methods are discussed. On a physical level, the selective reading of tracks on different layers can be achieved by resonant reading. Resonant reading promises reduced interference from different layers, enabling higher storage densities. Regarding the signal processing, DNNs can be used to estimate the media noise and iteratively exchange soft-bit information with the decoder. Also, to ameliorate partial erasures, an auto-encoder-based system is proposed as a modulation coding scheme.
AB - This paper describes challenges, solutions, and prospects for data recovery in multilayer magnetic recording (MLMR)—the vertical stacking of magnetic media layers to increase information storage density. To this end, the channel model for MLMR is discussed. Data recovery is described in terms of the readback stage followed by equalization and then detection. We illustrate how deep neural networks (DNNs) can be used to design systems for equalization and detection for MLMR. We show that such DNN-based systems outperform the conventional baseline and provide a good trade-off between complexity and performance. To achieve additional density gains, several prospective methods are discussed. On a physical level, the selective reading of tracks on different layers can be achieved by resonant reading. Resonant reading promises reduced interference from different layers, enabling higher storage densities. Regarding the signal processing, DNNs can be used to estimate the media noise and iteratively exchange soft-bit information with the decoder. Also, to ameliorate partial erasures, an auto-encoder-based system is proposed as a modulation coding scheme.
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U2 - 10.1063/5.0051085
DO - 10.1063/5.0051085
M3 - Review article
AN - SCOPUS:85109283827
VL - 119
JO - Applied Physics Letters
JF - Applied Physics Letters
SN - 0003-6951
IS - 1
M1 - 010502
ER -