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dc.contributor.authorLocatelli, Fabiano
dc.contributor.authorChristodoulopoulos, Konstantinos
dc.contributor.authorSvaluto Moreolo, Michela
dc.contributor.authorFàbrega Sánchez, Josep Maria
dc.contributor.authorNadal Reixats, Laia
dc.contributor.authorSpadaro, Salvatore
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.identifier.citationLocatelli, F. [et al.]. Spectral processing techniques for efficient monitoring in optical networks. "Journal of optical communications and networking", 1 Juliol 2021, vol. 13, núm. 7, p. 158-168.
dc.description.abstractHaving ubiquitous optical monitors in dense wavelength-division multiplexing (DWDM) or flex-grid networks allows the estimation in real time of crucial parameters. Such monitoring would be even more important in disaggregated optical networks, to inspect performance issues related to inter-vendor interoperability. Several important parameters can be retrieved using optical spectrum analyzers (OSAs). However, omnipresent OSAs represent an infeasible solution. Nevertheless, the advent of new, relatively cheap, compact and medium-resolution optical channel monitors (OCMs) enable a more intensive deployment of these devices. In this paper, we identify two main scenarios for the placement of such monitors: at the ingress and at the egress of the optical nodes. In the ingress scenario, we can directly estimate the parameters related to the signals, but not those related to the filters. On the contrary, in the egress scenario, the filter-related parameters can be easily detected, but not those related to amplified spontaneous emission. Therefore, we present two methods that, leveraging a curve fitting and a machine learning regression algorithm, allow detection of the missing parameters. We verify the proposed solutions with spectral data acquired in simulation and experimental setups. We obtained good estimation accuracy for both setups and for both studied placement scenarios. It is noteworthy that in the experimental assessment of the ingress scenario, we achieved a maximum absolute error (MAE) lower than 1 GHz in filter bandwidth estimation and a MAE lower than 0.5 GHz in filter frequency shift estimation. In addition, by comparing the relative errors of the considered parameters, we identified the ingress scenario as the more beneficial. In particular, we estimated the filter central frequency shift with 84% and the filter 6 dB bandwidth with 75% higher accuracy, with respect to datasheet/reference values. This translates into a total reduction of the estimated signal-to-noise ratio (SNR) penalty, introduced by a single optical filter, of 0.24 dB.
dc.description.sponsorshipFunding: Horizon 2020 Framework Programme (765275). This work is part of the Future Optical Networks for Innovation, Research and Experimentation (ONFIRE) project (, which is supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Action.
dc.format.extent11 p.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica
dc.subject.lcshOptical fiber communication
dc.subject.lcshMachine learning
dc.subject.otherCurve fitting
dc.subject.otherDense wavelength division multiplexing
dc.subject.otherFiber optic networks
dc.subject.otherFrequency estimation
dc.subject.otherLearning algorithms
dc.subject.otherSpectrum analyzers
dc.subject.otherTuring machines
dc.titleSpectral processing techniques for efficient monitoring in optical networks
dc.subject.lemacComunicació per fibra òptica
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. GCO - Grup de Comunicacions Òptiques
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
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.authorLocatelli, F.; Christodoulopoulos, K.; Svaluto, M.; Fabrega, J.; Nadal, L.; Spadaro, S.
local.citation.publicationNameJournal of optical communications and networking

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