Applied Sciences (Dec 2020)

CNN Classification Architecture Study for Turbulent Free-Space and Attenuated Underwater Optical OAM Communications

  • Patrick L. Neary,
  • Abbie T. Watnik,
  • Kyle Peter Judd,
  • James R. Lindle,
  • Nicholas S. Flann

DOI
https://doi.org/10.3390/app10248782
Journal volume & issue
Vol. 10, no. 24
p. 8782

Abstract

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Turbulence and attenuation are signal degrading factors that can severely hinder free-space and underwater OAM optical pattern demultiplexing. A variety of state-of-the-art convolutional neural network architectures are explored to identify which, if any, provide optimal performance under these non-ideal environmental conditions. Hyperparameter searches are performed on the architectures to ensure that near-ideal settings are used for training. Architectures are compared in various scenarios and the best performing, with their settings, are provided. We show that from the current state-of-the-art architectures, DenseNet outperforms all others when memory is not a constraint. When memory footprint is a factor, ShuffleNet is shown to performed the best.

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