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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE photonics technology letters 2019-12, Vol.31 (24), p.1929-1932
Main Authors: Locatelli, Fabiano, Christodoulopoulos, Konstantinos, Moreolo, Michela Svaluto, Fabrega, Josep M., Spadaro, Salvatore
Format: Article
Language:English
Subjects:
Adaptive optics
Artificial intelligence
Gaussian process
Machine learning
Noise
Noise levels
Optical amplifiers
Optical attenuators
Optical communication
Optical filters
Optical noise
optical performance monitoring
Optical polarization
optical signal to noise ratio
optical spectrum
Optimization
Regression analysis
Signal processing
Signal to noise ratio
Spectra
Support vector machines
Wavelength routeing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1041-1135
1941-0174
DOI:10.1109/LPT.2019.2950058