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Learning from the optical spectrum: failure detection and identification [Invited]
dc.contributor.author | Shariati, Mohammad Behnam |
dc.contributor.author | Ruiz Ramírez, Marc |
dc.contributor.author | Comellas Colomé, Jaume |
dc.contributor.author | Velasco Esteban, Luis Domingo |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2018-11-30T14:56:38Z |
dc.date.available | 2018-11-30T14:56:38Z |
dc.date.issued | 2018-07-23 |
dc.identifier.citation | Shariati, M., Ruiz, M., Comellas, J., Velasco, L. Learning from the optical spectrum: failure detection and identification [Invited]. "Journal of lightwave technology", 23 Juliol 2018, vol. 37, núm. 2, p. 433-440. |
dc.identifier.issn | 0733-8724 |
dc.identifier.uri | http://hdl.handle.net/2117/125289 |
dc.description | © 2018 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.abstract | The availability of coarse-resolution cost-effective Optical Spectrum Analyzers (OSA) allows its widespread deployment in operators’ networks. In this paper, we explore several machine learning approaches for soft-failure detection, identification and localization that take advantage of OSAs. In particular, we present three different solutions for the two most common filter-related soft-failures; filter shift and tight filtering which noticeably deform the expected shape of the optical spectrum. However, filter cascading is a key challenge as it affects the shape of the optical spectrum similarly to tight filtering; the approaches are specifically designed to avoid the misclassification of properly operating signals when normal filter cascading effects are present. The proposed solutions are: $i) multi-classifier$ approach, which uses features extracted directly from the optical spectrum, $ii) single-classifier$ approach, which uses pre-processed features to compensate for filter cascading, and $iii) residual-based$ approach, which uses a residual signal computed from subtracting the acquired single by OSAs from an expected signal synthetically generated. Extensive numerical results are ultimately presented to compare the performance of the proposed approaches in terms of accuracy and robustness. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica |
dc.subject.lcsh | Optical communications |
dc.subject.other | Optical filters |
dc.subject.other | Feature extraction |
dc.subject.other | Monitoring |
dc.subject.other | Filtering algorithms |
dc.subject.other | Optical fiber networks |
dc.subject.other | Optical noise |
dc.subject.other | Soft-failure detection and identification |
dc.subject.other | Optical performance monitoring |
dc.subject.other | Elastic optical networks |
dc.title | Learning from the optical spectrum: failure detection and identification [Invited] |
dc.type | Article |
dc.subject.lemac | Comunicacions òptiques |
dc.contributor.group | Universitat Politècnica de Catalunya. GCO - Grup de Comunicacions Òptiques |
dc.identifier.doi | 10.1109/JLT.2018.2859199 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8418780 |
dc.rights.access | Open Access |
local.identifier.drac | 23537761 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info: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.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2017-90097-R/ES/COGNITIVE 5G APPLICATION-AWARE OPTICAL METRO NETWORKS INTEGRATING MONITORING, DATA ANALYTICS AND OPTIMIZATION/ |
local.citation.author | Shariati, M.; Ruiz, M.; Comellas, J.; Velasco, L. |
local.citation.publicationName | Journal of lightwave technology |
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