Design Optimisation of Power-Efficient Submarine Line through Machine Learning

Maria Ionescu; Amirhossein Ghazisaeidi; Jérémie Renaudier; Pascal Pecci; Olivier Courtois

doi:10.1364/CLEO_SI.2020.STh4M.5

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2020),
paper STh4M.5
•https://doi.org/10.1364/CLEO_SI.2020.STh4M.5

Design Optimisation of Power-Efficient Submarine Line through Machine Learning

Maria Ionescu, Amirhossein Ghazisaeidi, Jérémie Renaudier, Pascal Pecci, and Olivier Courtois

CLEO: Science and Innovations 2020

Washington, DC United States
10–15 May 2020
ISBN: 978-1-943580-76-7

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Applications of Machine Learning (STh4M)

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M. Ionescu, A. Ghazisaeidi, J. Renaudier, P. Pecci, and O. Courtois, "Design Optimisation of Power-Efficient Submarine Line through Machine Learning," in Conference on Lasers and Electro-Optics, OSA Technical Digest (Optica Publishing Group, 2020), paper STh4M.5.

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Abstract

An optimised subsea system design for energy-efficient SDM operation is demonstrated using machine learning. The removal of gain-flattening filters employed in submarine optical amplifiers can result in capacity gains at no additional overall repeater cost.

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