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Applying cognitive dynamic learning strategies for margins reduction in operational optical networks
dc.contributor.author | Notivol Calleja, Luis David |
dc.contributor.author | Spadaro, Salvatore |
dc.contributor.author | Perelló Muntan, Jordi |
dc.contributor.author | Junyent Giralt, Gabriel |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.date.accessioned | 2020-10-14T16:31:33Z |
dc.date.available | 2022-07-29T00:27:12Z |
dc.date.issued | 2020-09-01 |
dc.identifier.citation | Notivol, L. [et al.]. Applying cognitive dynamic learning strategies for margins reduction in operational optical networks. "Optical switching and networking", 1 Setembre 2020, vol. 38, p. 100585:1-10585:11. |
dc.identifier.issn | 1573-4277 |
dc.identifier.uri | http://hdl.handle.net/2117/330265 |
dc.description | © <2020>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.description.abstract | Today's optical transport networks are complex already and the support of the new arising services will further increase such complexity. Traditional deterministic network procedures will need to be revisited, especially their operations. Network Operators will require more dynamic approaches to get the best out of their infrastructure. In this context, cognition and machine learning techniques can provide innovative management solutions for traditional telecom operators. In this paper, we explore a dynamic cognitive approach to improve the adaption of Network Operators' operational processes to the new digital age. We propose a dynamic strategy considering the Case-Base Reasoning (CBR) technique for helping to reduce overall costs by optimizing operation margins. In this way, highly competitive exploitation methods to support new services can be deployed. The proposed dynamic algorithms can achieve higher transmitted power efficiency, up to 20% versus previously proposed static solutions, prolonging the transceivers' lifetime and thus addressing telco operator costs reduction. |
dc.description.sponsorship | This work has been supported by the Spanish Government through project ALLIANCE (TEC2017-90034-C2-2-R) with FEDER contribution. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica::Fibra òptica |
dc.subject.lcsh | Optical fibers |
dc.subject.other | Optical networks |
dc.subject.other | Machine learning |
dc.subject.other | Optical network optimization |
dc.title | Applying cognitive dynamic learning strategies for margins reduction in operational optical networks |
dc.type | Article |
dc.subject.lemac | Fibres òptiques |
dc.contributor.group | Universitat Politècnica de Catalunya. GCO - Grup de Comunicacions Òptiques |
dc.contributor.group | Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla |
dc.identifier.doi | 10.1016/j.osn.2020.100585 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/abs/pii/S1573427719301651 |
dc.rights.access | Open Access |
local.identifier.drac | 29313263 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2017-90034-C2-2-R/ES/DISEÑANDO UNA INFRAESTRUCTURA DE RED 5G DEFINIDA MEDIANTE CONOCIMIENTO HACIA LA PROXIMA SOCIEDAD DIGITAL-B/ |
local.citation.author | Notivol, L.; Spadaro, S.; Perello, J.; Junyent, G. |
local.citation.publicationName | Optical switching and networking |
local.citation.volume | 38 |
local.citation.startingPage | 100585:1 |
local.citation.endingPage | 10585:11 |
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