Show simple item record

dc.contributor.authorMahajan, Ankush
dc.contributor.authorChristodoulopoulos, Konstantinos
dc.contributor.authorMartinez, Ricardo
dc.contributor.authorSpadaro, Salvatore
dc.contributor.authorMuñoz, Raul
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2020-06-29T16:02:33Z
dc.date.issued2020
dc.identifier.citationMahajan, A. [et al.]. Improving QoT estimation accuracy with DGE monitoring using machine learning. A: International Conference on Optical Network Design and Modeling. "2020 International Conference on Optical Network Design and Modeling (ONDM) took place 18-21 May 2020 in Castelldefels, Spain". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 1-6.
dc.identifier.isbn978-3-903176-22-5
dc.identifier.urihttp://hdl.handle.net/2117/191922
dc.description.abstractIn optical transport networks, Dynamic Gain Equalizers (DGE) are typically used at each link. A DGE selectively attenuates the channels to compensate the cumulative Erbium Doped Fiber Amplifier (EDFA) gain ripple effect on the multi-span link, resulting in almost flat output power at the end of the link. We leverage monitored per link DGE attenuation profiles and coherent receivers Signal to Noise Ratio (SNR) information, and propose a machine learning (ML) based scheme to estimate the EDFA gain ripple penalties for new connections. Using that in realistic simulation scenarios we observed a design margin reduction from ~1dB to ~0.3dBs.
dc.description.sponsorshipThis work is a part of Future Optical Networks for Innovation, Research andExperimentation, ONFIRE project supported by European Union’s fundedHorizon 2020 research and innovation programme under the MarieSkłodowska-Curie grant agreement No. 765275.
dc.format.extent6 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.otherOptical network
dc.subject.otherQoT estimation
dc.subject.otherMonitoring
dc.subject.otherMachine learning
dc.subject.otherMargins
dc.titleImproving QoT estimation accuracy with DGE monitoring using machine learning
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. GCO - Grup de Comunicacions Òptiques
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac28601617
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/765275/EU/Future Optical Networks for Innovation, Research and Experimentation/ONFIRE
dc.date.lift10000-01-01
local.citation.authorMahajan, A.; Christodoulopoulos, K.; Martinez, R.; Spadaro, S.; Muñoz, R.
local.citation.contributorInternational Conference on Optical Network Design and Modeling
local.citation.publicationName2020 International Conference on Optical Network Design and Modeling (ONDM) took place 18-21 May 2020 in Castelldefels, Spain
local.citation.startingPage1
local.citation.endingPage6


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record