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dc.contributor.authorVelasco Esteban, Luis Domingo
dc.contributor.authorShariati, Mohammad Behnam
dc.contributor.authorBoitier, Fabien
dc.contributor.authorLayec, Patricia
dc.contributor.authorRuiz Ramírez, Marc
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2019-06-13T07:25:20Z
dc.date.available2019-06-13T07:25:20Z
dc.date.issued2019-04-05
dc.identifier.citationVelasco, L. [et al.]. Learning life cycle to speed up autonomic optical transmission and networking adoption. "Journal of optical communications and networking", 5 Abril 2019, vol. 11, núm. 5, p. 226-237.
dc.identifier.issn1943-0620
dc.identifier.urihttp://hdl.handle.net/2117/134370
dc.description.abstractAutonomic optical transmission and networking requires machine learning (ML) models to be trained with large datasets. However, the availability of enough real data to produce accurate ML models is rarely ensured since new optical equipment and techniques are continuously being deployed in the network. One option is to generate data from simulations and lab experiments, but such data could not cover the whole features space and would translate into inaccuracies in the ML models. In this paper, we propose an ML-based algorithm life cycle to facilitate ML deployment in real operator networks. The dataset for ML training can be initially populated based on the results from simulations and lab experiments. Once ML models are generated, ML retraining can be performed after inaccuracies are detected to improve their precision. Illustrative numerical results show the benefits of the proposed learning cycle for general use cases. In addition, two specific use cases are proposed and demonstrated that implement different learning strategies: (i) a two-phase strategy performing out-of-field training using data from simulations and lab experiments with generic equipment, followed by an in-field adaptation to support heterogeneous equipment (the accuracy of this strategy is shown for a use case of failure detection and identification), and (ii) in-field retraining, where ML models are retrained after detecting model inaccuracies. Different approaches are analyzed and evaluated for a use case of autonomic transmission, where results show the significant benefits of collective learning.
dc.format.extent12 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
dc.subject.lcshMachine learning
dc.subject.lcshOptical communications
dc.subject.otherAutonomic optical transmission and net-working
dc.subject.otherTraining function placement
dc.titleLearning life cycle to speed up autonomic optical transmission and networking adoption
dc.typeArticle
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacComunicacions òptiques
dc.contributor.groupUniversitat Politècnica de Catalunya. GCO - Grup de Comunicacions Òptiques
dc.identifier.doi10.1364/JOCN.11.000226
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.osapublishing.org/jocn/abstract.cfm?uri=jocn-11-5-226
dc.rights.accessOpen Access
local.identifier.drac25164351
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/TEC2017-90097-R
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/761727/EU/METRO High bandwidth, 5G Application-aware optical network, with edge storage, compUte and low Latency/METRO-HAUL
dc.relation.projectidinfo:eu-repo/grantAgreement/ICREA/V PRI/ICREA ACADEMIA 2015-05
local.citation.authorVelasco, L.; Shariati, M.; Boitier, F.; Layec, P.; Ruiz, M.
local.citation.publicationNameJournal of optical communications and networking
local.citation.volume11
local.citation.number5
local.citation.startingPage226
local.citation.endingPage237


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