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...
| Autores: | , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Recursos: | 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 |
| Acesso em linha: | 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 |
| Palavra-chave: | 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 |
| Resumo: | 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. |
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