Challenges when Partially Training a Machine Learning-Based Optical Communication System in Variable Experimental Conditions

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

doi:10.1364/PCAOP.2022.PTh4F.1

Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP)
Technical Digest Series (Optica Publishing Group, 2022),
paper PTh4F.1
•https://doi.org/10.1364/PCAOP.2022.PTh4F.1

Challenges when Partially Training a Machine Learning-Based Optical Communication System in Variable Experimental Conditions

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

Propagation Through and Characterization of Atmospheric and Oceanic Phenomena 2022

Vancouver, British Columbia Canada
11–15 July 2022
ISBN: 978-1-957171-09-8

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AI and Machine Learning in pcAOP (PTh4F)

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W. A. Jarrett, S. Avramov-Zamurovic, J. M. Esposito, and C. Nelson, "Challenges when Partially Training a Machine Learning-Based Optical Communication System in Variable Experimental Conditions," in Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP), Technical Digest Series (Optica Publishing Group, 2022), paper PTh4F.1.

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Abstract

We present challenges when training a machine learning-based underwater wireless optical communication system in selected experimental scenarios. The system is tested under different conditions, that include minor beam misalignment and varying optical turbulence.

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