Machine Learning-Based in-Band OSNR Estimation from Optical Spectra

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 p...

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Detalles Bibliográficos
Autores: Locatelli, F, Christodoulopoulos, K, Moreolo, MS, Fabrega, JM, Spadaro, S
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Repositorio:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
OAI Identifier:oai:cttc.fundanetsuite.com:p1428
Acceso en línea:https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1428
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077227868&doi=10.1109%2fLPT.2019.2950058&partnerID=40&md5=637e24bda59c06a8a5e9221b6daadd2e
Access Level:acceso abierto
Palabra clave:Learning systems
Machine learning
Spectrum analyzers
Support vector machines
Estimation process
Estimation quality
Optical performance monitoring
Optical signal to noise ratio
Optical spectra
Optical spectrum analyzer
Support vector machine regressions
Wavelength-routed networks
Signal to noise ratio
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spelling Machine Learning-Based in-Band OSNR Estimation from Optical SpectraLocatelli, FChristodoulopoulos, KMoreolo, MSFabrega, JMSpadaro, SLearning systemsMachine learningSpectrum analyzersSupport vector machinesEstimation processEstimation qualityOptical performance monitoringOptical signal to noise ratioOptical spectraOptical spectrum analyzerSupport vector machine regressionsWavelength-routed networksSignal to noise ratioMeasuring 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. © 1989-2012 IEEE.Institute of Electrical and Electronics Engineers Inc.2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1428https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077227868&doi=10.1109%2fLPT.2019.2950058&partnerID=40&md5=637e24bda59c06a8a5e9221b6daadd2eIEEE PHOTONICS TECHNOLOGY LETTERSISSN: 10411135ISSNe: 19410174reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)Inglésinfo:eu-repo/semantics/openAccessoai:cttc.fundanetsuite.com:p14282026-06-17T11:44:47Z
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, F
Learning systems
Machine learning
Spectrum analyzers
Support vector machines
Estimation process
Estimation quality
Optical performance monitoring
Optical signal to noise ratio
Optical spectra
Optical spectrum analyzer
Support vector machine regressions
Wavelength-routed networks
Signal to noise ratio
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, F
Christodoulopoulos, K
Moreolo, MS
Fabrega, JM
Spadaro, S
author Locatelli, F
author_facet Locatelli, F
Christodoulopoulos, K
Moreolo, MS
Fabrega, JM
Spadaro, S
author_role author
author2 Christodoulopoulos, K
Moreolo, MS
Fabrega, JM
Spadaro, S
author2_role author
author
author
author
dc.subject.none.fl_str_mv Learning systems
Machine learning
Spectrum analyzers
Support vector machines
Estimation process
Estimation quality
Optical performance monitoring
Optical signal to noise ratio
Optical spectra
Optical spectrum analyzer
Support vector machine regressions
Wavelength-routed networks
Signal to noise ratio
topic Learning systems
Machine learning
Spectrum analyzers
Support vector machines
Estimation process
Estimation quality
Optical performance monitoring
Optical signal to noise ratio
Optical spectra
Optical spectrum analyzer
Support vector machine regressions
Wavelength-routed networks
Signal to noise ratio
description 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. © 1989-2012 IEEE.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1428
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077227868&doi=10.1109%2fLPT.2019.2950058&partnerID=40&md5=637e24bda59c06a8a5e9221b6daadd2e
url https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1428
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077227868&doi=10.1109%2fLPT.2019.2950058&partnerID=40&md5=637e24bda59c06a8a5e9221b6daadd2e
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv IEEE PHOTONICS TECHNOLOGY LETTERS
ISSN: 10411135
ISSNe: 19410174
reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname_str Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
reponame_str r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
collection r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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