Machine Learning for QoT Estimation of Unseen Optical Network States
arXiv:1812.07254 · doi:10.1364/OFC.2019.Tu2E.2
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.
accepted for publication in the Optical Networking and Communication Conference & Exhibition (OFC), 2019