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

Applied Sciences

JamesR. Lindle¹  NicholasS. Flann²  PatrickL. Neary²  AbbieT. Watnik³  KylePeter Judd³ 

[1] DCS Corporation, Alexandria, VA 22310, USA;Department of Computer Science, Utah State University, Logan, UT 84322, USA;U.S. Naval Research Laboratory, Washington, DC 20375, USA;

关键词: convolutional neural networks;    orbital angular momentum;    underwater communications;   

DOI  :  10.3390/app10248782

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown