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dc.contributor.authorMata, Javier
dc.contributor.authorMiguel Jiménez, Ignacio de 
dc.contributor.authorDurán Barroso, Ramón José 
dc.contributor.authorAguado Manzano, Juan Carlos 
dc.contributor.authorMerayo Álvarez, Noemí 
dc.contributor.authorRuiz Pérez, Lidia 
dc.contributor.authorFernández Reguero, Patricia 
dc.contributor.authorLorenzo Toledo, Rubén Mateo 
dc.contributor.authorAbril Domingo, Evaristo José 
dc.date.accessioned2018-12-19T10:01:20Z
dc.date.available2018-12-19T10:01:20Z
dc.date.issued2017
dc.identifier.citation2017 IEEE International Conference on Big Data (Big Data), 11-14 Dec. 2017, Boston, USA.es
dc.identifier.isbn978-1-5386-2715-0es
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/33552
dc.descriptionProducción Científicaes
dc.description.abstractA novel quality of transmission (QoT) estimator based on support vector machines (SVM) is proposed for classifying optical connections (lightpaths) into high or low quality categories in impairment-aware wavelength-routed optical networks (WRONs). The performance of the SVM-based estimator is evaluated in a long haul communications network and compared to previous semi-analytical and cognitive proposals. Results show that the SVM approach significantly reduces the necessary computing time to estimate the QoT of a given lightpath, critical aspect of design in these networks, and even slightly improves accuracy.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)es
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.subject.classificationRedes de transporte ópticoes
dc.subject.classificationOptical transport networkses
dc.titleA SVM approach for lightpath QoT estimation in optical transport networkses
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.rights.holder© 2018 IEEEes
dc.identifier.doihttps://doi.org/10.1109/BigData.2017.8258545es
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8258545es
dc.title.eventInternational Conference on Big Data (Big Data) (5º. 2017. Boston)es
dc.description.projectMinisterio de Economía, Industria y Competitividad (Projects (TEC2014-53071-C3-2-P and TEC2015-71932-REDT)es
dc.description.projectMinisterio de Educación, Cultura y Deporte (Proyect BES-2015-074514)es


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