Spectral processing techniques for efficient monitoring in optical networks

Having 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 in...

Descripción completa

Detalles Bibliográficos
Autores: Locatelli, Fabiano|||0000-0002-2971-1303, Christodoulopoulos, Konstantinos, Svaluto Moreolo, Michela, Fàbrega Sánchez, Josep Maria, Nadal Reixats, Laia, Spadaro, Salvatore|||0000-0002-4100-1726
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/348927
Acceso en línea:https://hdl.handle.net/2117/348927
https://dx.doi.org/10.1364/JOCN.418800
Access Level:acceso abierto
Palabra clave:Optical fiber communication
Machine learning
Bandwidth
Curve fitting
Dense wavelength division multiplexing
Fiber optic networks
Frequency estimation
Learning algorithms
Spectrum analyzers
Turing machines
Comunicació per fibra òptica
Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica
id ES_445c9a7ea53a1ca74a1800bfa6d1dd2b
oai_identifier_str oai:upcommons.upc.edu:2117/348927
network_acronym_str ES
network_name_str España
repository_id_str
spelling Spectral processing techniques for efficient monitoring in optical networksLocatelli, Fabiano|||0000-0002-2971-1303Christodoulopoulos, KonstantinosSvaluto Moreolo, MichelaFàbrega Sánchez, Josep MariaNadal Reixats, LaiaSpadaro, Salvatore|||0000-0002-4100-1726Optical fiber communicationMachine learningBandwidthCurve fittingDense wavelength division multiplexingFiber optic networksFrequency estimationLearning algorithmsSpectrum analyzersTuring machinesComunicació per fibra òpticaAprenentatge automàticÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òpticaHaving 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.Funding: Horizon 2020 Framework Programme (765275). This work is part of the Future Optical Networks for Innovation, Research and Experimentation (ONFIRE) project (https://h2020-onfire.eu), which is supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Action.Peer ReviewedInstitute of Electrical and Electronics Engineers (IEEE)20212021-07-0120212021-07-12journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/348927https://dx.doi.org/10.1364/JOCN.418800reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 765275 Future Optical Networks for Innovation, Research and Experimentationopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3489272026-05-27T15:37:01Z
dc.title.none.fl_str_mv Spectral processing techniques for efficient monitoring in optical networks
title Spectral processing techniques for efficient monitoring in optical networks
spellingShingle Spectral processing techniques for efficient monitoring in optical networks
Locatelli, Fabiano|||0000-0002-2971-1303
Optical fiber communication
Machine learning
Bandwidth
Curve fitting
Dense wavelength division multiplexing
Fiber optic networks
Frequency estimation
Learning algorithms
Spectrum analyzers
Turing machines
Comunicació per fibra òptica
Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica
title_short Spectral processing techniques for efficient monitoring in optical networks
title_full Spectral processing techniques for efficient monitoring in optical networks
title_fullStr Spectral processing techniques for efficient monitoring in optical networks
title_full_unstemmed Spectral processing techniques for efficient monitoring in optical networks
title_sort Spectral processing techniques for efficient monitoring in optical networks
dc.creator.none.fl_str_mv Locatelli, Fabiano|||0000-0002-2971-1303
Christodoulopoulos, Konstantinos
Svaluto Moreolo, Michela
Fàbrega Sánchez, Josep Maria
Nadal Reixats, Laia
Spadaro, Salvatore|||0000-0002-4100-1726
author Locatelli, Fabiano|||0000-0002-2971-1303
author_facet Locatelli, Fabiano|||0000-0002-2971-1303
Christodoulopoulos, Konstantinos
Svaluto Moreolo, Michela
Fàbrega Sánchez, Josep Maria
Nadal Reixats, Laia
Spadaro, Salvatore|||0000-0002-4100-1726
author_role author
author2 Christodoulopoulos, Konstantinos
Svaluto Moreolo, Michela
Fàbrega Sánchez, Josep Maria
Nadal Reixats, Laia
Spadaro, Salvatore|||0000-0002-4100-1726
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Optical fiber communication
Machine learning
Bandwidth
Curve fitting
Dense wavelength division multiplexing
Fiber optic networks
Frequency estimation
Learning algorithms
Spectrum analyzers
Turing machines
Comunicació per fibra òptica
Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica
topic Optical fiber communication
Machine learning
Bandwidth
Curve fitting
Dense wavelength division multiplexing
Fiber optic networks
Frequency estimation
Learning algorithms
Spectrum analyzers
Turing machines
Comunicació per fibra òptica
Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica
description Having 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.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-07-01
2021
2021-07-12
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/348927
https://dx.doi.org/10.1364/JOCN.418800
url https://hdl.handle.net/2117/348927
https://dx.doi.org/10.1364/JOCN.418800
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 765275 Future Optical Networks for Innovation, Research and Experimentation
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407087729049600
score 15,301603

Similares en LA Referencia