Machine learning-based in-band OSNR estimation from optical spectra

© 2019 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 se...

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Autores: Locatelli, Fabiano|||0000-0002-2971-1303, Christodoulopoulos, Konstantinos, Svaluto Moreolo, Michela, Fàbrega, Josep Maria, Spadaro, Salvatore|||0000-0002-4100-1726
Tipo de recurso: artículo
Fecha de publicación:2019
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:2117/186304
Acceso en línea:https://hdl.handle.net/2117/186304
https://dx.doi.org/10.1109/LPT.2019.2950058
Access Level:acceso abierto
Palabra clave:Machine learning
Optical performance monitoring
Optical signal to noise ratio
Optical spectrum
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
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spelling Machine learning-based in-band OSNR estimation from optical spectraLocatelli, Fabiano|||0000-0002-2971-1303Christodoulopoulos, KonstantinosSvaluto Moreolo, MichelaFàbrega, Josep MariaSpadaro, Salvatore|||0000-0002-4100-1726Machine learningMachine learningOptical performance monitoringOptical signal to noise ratioOptical spectrumAprenentatge automàticÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic© 2019 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.Measuring the optical signal to noise ratio (OSNR) at certain network points is essential for failure handling, for single connection but also global network optimization. Estimating OSNR is inherently difficult in dense wavelength routed networks, where connections accumulate noise over different paths and tight filters do not allow the observation of the noise level at signal sides. We propose an in-band OSNR estimation process, which relies on a machine learning (ML) method, in particular on Gaussian process (GP) or support vector machine (SVM) regression. We acquired high-resolution optical spectra, through an experimental setup, using a Brillouin optical spectrum analyzer (BOSA), on which we applied our method and obtained excellent estimation accuracy. We also verified the accuracy of this approach for various resolution scenarios. To further validate it, we generated spectral data for different configurations and resolutions through simulations. This second validation confirmed the estimation quality of the proposed approach.The authors would like to thank Aragon Photonics Labs for providing the BOSA used for the experiments. This work was partially funded by the ONFIRE project supported by EU Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765275Peer Reviewed20192019-12-1520202020-05-05journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/186304https://dx.doi.org/10.1109/LPT.2019.2950058reponame: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:2117/1863042026-05-27T15:37:01Z
dc.title.none.fl_str_mv Machine learning-based in-band OSNR estimation from optical spectra
title Machine learning-based in-band OSNR estimation from optical spectra
spellingShingle Machine learning-based in-band OSNR estimation from optical spectra
Locatelli, Fabiano|||0000-0002-2971-1303
Machine learning
Machine learning
Optical performance monitoring
Optical signal to noise ratio
Optical spectrum
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
title_short Machine learning-based in-band OSNR estimation from optical spectra
title_full Machine learning-based in-band OSNR estimation from optical spectra
title_fullStr Machine learning-based in-band OSNR estimation from optical spectra
title_full_unstemmed Machine learning-based in-band OSNR estimation from optical spectra
title_sort Machine learning-based in-band OSNR estimation from optical spectra
dc.creator.none.fl_str_mv Locatelli, Fabiano|||0000-0002-2971-1303
Christodoulopoulos, Konstantinos
Svaluto Moreolo, Michela
Fàbrega, Josep Maria
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, Josep Maria
Spadaro, Salvatore|||0000-0002-4100-1726
author_role author
author2 Christodoulopoulos, Konstantinos
Svaluto Moreolo, Michela
Fàbrega, Josep Maria
Spadaro, Salvatore|||0000-0002-4100-1726
author2_role author
author
author
author
dc.subject.none.fl_str_mv Machine learning
Machine learning
Optical performance monitoring
Optical signal to noise ratio
Optical spectrum
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
topic Machine learning
Machine learning
Optical performance monitoring
Optical signal to noise ratio
Optical spectrum
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
description © 2019 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.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-12-15
2020
2020-05-05
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/2117/186304
https://dx.doi.org/10.1109/LPT.2019.2950058
url https://hdl.handle.net/2117/186304
https://dx.doi.org/10.1109/LPT.2019.2950058
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.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
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score 15,300719

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