Machine Learning-Based in-Band OSNR Estimation from Optical Spectra
Por:
Locatelli, F, Christodoulopoulos, K, Moreolo, MS, Fabrega, JM, Spadaro, S
Publicada:
1 ene 2019
Resumen:
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.
Filiaciones:
Locatelli, F:
Nokia Bell Labs, D-70435 Stuttgart, Germany
Ctr Tecnol Telecomunicac Catalunya, Opt Networks & Syst Dept, Castelldefels 08860, Spain
Christodoulopoulos, K:
Nokia Bell Labs, D-70435 Stuttgart, Germany
Moreolo, MS:
Ctr Tecnol Telecomunicac Catalunya, Opt Networks & Syst Dept, Castelldefels 08860, Spain
Fabrega, JM:
Ctr Tecnol Telecomunicac Catalunya, Opt Networks & Syst Dept, Castelldefels 08860, Spain
Spadaro, S:
Univ Politecn Cataluna, Dept Signal Theory & Commun, ES-08034 Barcelona, Spain
Green Submitted, All Open Access; Green Open Access
|