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Demodulation scheme for constant-weight codes using convolutional neural network in holographic data storage

  • Special Section: Regular Paper
  • International Symposium on Imaging, Sensing, and Optical Memory (ISOM ’20), Takamatsu, Japan
  • Published:
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Abstract

In practical applications of holographic data storage (HDS), which offers both high capacity and high transfer rates, reduction of the error rate is a major issue. In this study, we have applied a convolutional neural network (CNN)-based demodulation scheme for constant-weight codes to HDS and have demonstrated that the error rate can be reduced by approximately one order of magnitude when compared with that of the conventional ranking method. We have also evaluated the HDS error characteristics, which were not clarified fully in previous studies. The results of this evaluation showed that symbols with certain specific shapes are prone to errors. This study is expected to provide useful knowledge for use in practical applications of HDS.

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Authors and Affiliations

  1. Department of Electric and Electronic Engineering, Kindai University, 3-4-1 Kowakae, Higashiosaka City, Osaka, 577-8502, Japan

    Shinya Kurokawa & Shuhei Yoshida

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  1. Shinya Kurokawa
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  2. Shuhei Yoshida
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Correspondence to Shuhei Yoshida.

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Kurokawa, S., Yoshida, S. Demodulation scheme for constant-weight codes using convolutional neural network in holographic data storage. Opt Rev 29, 375–381 (2022). https://doi.org/10.1007/s10043-022-00744-1

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  • DOI: https://doi.org/10.1007/s10043-022-00744-1

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