Supply-Power-Constrained Cable Capacity Maximization Using Deep Neural Networks

We experimentally achieve a 19% capacity gain per Watt of electrical supply power in a 12-span link by eliminating gain flattening filters and optimizing launch powers using machine learning by deep neural networks in a massively parallel fiber context.

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Bibliographic Details
Published in:arXiv.org 2019-10
Main Authors: Cho, Junho, Sethumadhavan Chandrasekhar, Sula, Erixhen, Olsson, Samuel, Burrows, Ellsworth, Raybon, Greg, Ryf, Roland, Fontaine, Nicolas, Jean-Christophe Antona, Grubb, Steve, Winzer, Peter, Chraplyvy, Andrew
Format: Article
Language:English
Subjects:
Machine learning
Neural networks
Optimization
Online Access:Get full text
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Description
Summary:We experimentally achieve a 19% capacity gain per Watt of electrical supply power in a 12-span link by eliminating gain flattening filters and optimizing launch powers using machine learning by deep neural networks in a massively parallel fiber context.
ISSN:2331-8422