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dc.contributor.authorMahajan, Ankush
dc.contributor.authorChristodoulopoulos, Konstantinos
dc.contributor.authorMartínez, 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.accessioned2020-10-01T17:43:51Z
dc.date.available2020-10-01T17:43:51Z
dc.date.issued2020-05-01
dc.identifier.citationMahajan, A. [et al.]. Modeling EDFA gain ripple and filter penalties with machine learning for accurate QoT estimation. "Journal of lightwave technology", 1 Maig 2020, vol. 38, núm. 9, p. 2616-2629.
dc.identifier.issn0733-8724
dc.identifier.urihttp://hdl.handle.net/2117/329668
dc.description© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractFor reliable and efficient network planning and operation, accurate estimation of Quality of Transmission (QoT) before establishing or reconfiguring the connection is necessary. In optical networks, a design margin is generally included in a QoT estimation tool (Qtool) to account for modeling and parameter inaccuracies, ensuring the acceptable performance. In this work, we use monitoring information from an operating network combined with supervised machine learning (ML) techniques to understand the network conditions. In particular, we model the penalties generated due to i.) Erbium Doped Fiber Amplifier (EDFA) gain ripple effect, and ii.) filter spectral shape uncertainties at Reconfigurable Optical Add and Drop Multiplexer (ROADM) nodes. Enhancing the Qtool with the proposed ML regression models yields estimates for new or reconfigured connections that account for these two effects, resulting in more accurate QoT estimation and a reduced design margin. We initially propose two supervised ML regression models, implemented with Support Vector Machine Regression (SVMR), to estimate the individual penalties of the two effects and then a combined model. On Deutsche Telekom (DT) network topology with 12 nodes and 40 bidirectional links, we achieve a design margin reduction of ~1dB for new connection requests.
dc.description.sponsorshipThis work was supported by the Future Optical Networks for Innovation, Research and Experimentation, ONFIRE project Euro-pean Union’s funded Horizon 2020 research and innovation programme underthe Marie Skłodowska-Curie under Grant 765275.
dc.format.extent14 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.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica::Fibra òptica
dc.subject.lcshMachine learning
dc.subject.lcshOptical fibers
dc.subject.otherErbium doped fiber amplifier (EDFA)
dc.subject.otherFilter cascading
dc.subject.otherMachine learning
dc.subject.otherNetwork margins
dc.subject.otherParameter uncertainties
dc.subject.otherQuality of transmission (QoT)
dc.titleModeling EDFA gain ripple and filter penalties with machine learning for accurate QoT estimation
dc.typeArticle
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacFibres òptiques
dc.contributor.groupUniversitat Politècnica de Catalunya. GCO - Grup de Comunicacions Òptiques
dc.identifier.doi10.1109/JLT.2020.2975081
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9003295
dc.rights.accessOpen Access
local.identifier.drac28989279
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
local.citation.authorMahajan, A.; Christodoulopoulos, K.; Martínez, R.; Spadaro, S.; Muñoz, R.
local.citation.publicationNameJournal of lightwave technology
local.citation.volume38
local.citation.number9
local.citation.startingPage2616
local.citation.endingPage2629


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