Machine Learning-Based Underwater Communication System Using Light Carrying Orbital Angular Momentum

William A. Jarrett; Svetlana Avramov-Zamurovic; Joel M. Esposito; Charles Nelson

doi:10.1364/CLEO_AT.2022.JW3B.108

Conference on Lasers and Electro-Optics
Technical Digest Series (Optica Publishing Group, 2022),
paper JW3B.108
•https://doi.org/10.1364/CLEO_AT.2022.JW3B.108

Machine Learning-Based Underwater Communication System Using Light Carrying Orbital Angular Momentum

William A. Jarrett, Svetlana Avramov-Zamurovic, Joel M. Esposito, and Charles Nelson

CLEO: Applications and Technology 2022

San Jose, California United States
15–20 May 2022
ISBN: 978-1-957171-05-0

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Joint Poster Session II-B (Virtual) (JW3B)

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W. A. Jarrett, S. Avramov-Zamurovic, J. M. Esposito, and C. Nelson, "Machine Learning-Based Underwater Communication System Using Light Carrying Orbital Angular Momentum," in Conference on Lasers and Electro-Optics, Technical Digest Series (Optica Publishing Group, 2022), paper JW3B.108.

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Abstract

An underwater communication system using light carrying orbital angular momentum is evaluated using a convolutional neural network through simulated and experimental thermally-generated underwater optical turbulence. 100% classification is demonstrated in no or weak turbulence.

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