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Machine Learning for QoT Estimation of Unseen Optical Network States

Networking and Internet Architecture 2020-08-04 v1 Signal Processing
Authors: Tania Panayiotou , Giannis Savva , Behnam Shariati , Ioannis Tomkos , Georgios Ellinas
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Abstract

We apply deep graph convolutional neural networks for Quality-of-Transmission estimation of unseen network states capturing, apart from other important impairments, the inter-core crosstalk that is prominent in optical networks operating with multicore fibers.

Keywords

machine learning quality-of-transmission estimation optical networks unseen network states deep graph convolutional neural networks inter-core crosstalk multicore fibers

Cite

@article{arxiv.1812.07254,
  title  = {Machine Learning for QoT Estimation of Unseen Optical Network States},
  author = {Tania Panayiotou and Giannis Savva and Behnam Shariati and Ioannis Tomkos and Georgios Ellinas},
  journal= {arXiv preprint arXiv:1812.07254},
  year   = {2020}
}

Comments

accepted for publication in the Optical Networking and Communication Conference & Exhibition (OFC), 2019

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