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Machine Learning for QoT Estimation of Unseen Optical Network States

dc.contributor.authorPanayiotou, Taniaen
dc.contributor.authorSavva, Giannisen
dc.contributor.authorShariati, Behnamen
dc.contributor.authorTomkos, Ioannisen
dc.contributor.authorEllinas, Georgiosen
dc.coverage.spatialSan Diegoen
dc.creatorPanayiotou, Taniaen
dc.creatorSavva, Giannisen
dc.creatorShariati, Behnamen
dc.creatorTomkos, Ioannisen
dc.creatorEllinas, Georgiosen
dc.date.accessioned2021-01-26T09:45:42Z
dc.date.available2021-01-26T09:45:42Z
dc.date.issued2019
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/63362
dc.description.abstractWe 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.en
dc.source2019 Optical Fiber Communications Conference and Exhibition (OFC)en
dc.titleMachine Learning for QoT Estimation of Unseen Optical Network Statesen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.description.startingpage1
dc.description.endingpage3
dc.author.facultyΠολυτεχνική Σχολή / Faculty of Engineering
dc.author.departmentΤμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering
dc.type.uhtypeConference Objecten
dc.contributor.orcidEllinas, Georgios [0000-0002-3319-7677]
dc.contributor.orcidSavva, Giannis [0000-0001-5566-1656]
dc.contributor.orcidTomkos, Ioannis [0000-0001-9721-3405]
dc.contributor.orcidPanayiotou, Tania [0000-0002-4698-9892]
dc.gnosis.orcid0000-0002-3319-7677
dc.gnosis.orcid0000-0001-5566-1656
dc.gnosis.orcid0000-0001-9721-3405
dc.gnosis.orcid0000-0002-4698-9892


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