3979496
doi
10.23919/ONDM48393.2020.9133001
oai:zenodo.org:3979496
user-eu
Christodoulopoulos, Konstantinos
Nokia Bell Labs
Fàbrega, Josep M.
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Svaluto- Moreolo, Michela
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Nadal, Laia
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Spadaro, Salvatore
Universitat Politècnica de Catalunya (UPC)
Experimental Demonstration of a Machine Learning-Based in-band OSNR Estimator from Optical Spectra
Locatelli, Fabiano
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Nokia Bell Labs
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Machine learning
optical performance monitoring
optical spectrum
<p>Channel spectral monitors are becoming a cost effective solution to improve the management, resiliency and efficiency of next generation optical transport networks. We experimentally demonstrate a technique based on machine learning (ML) for the in-band estimation of amplified spontaneous emission (ASE) noise and filter 3-dB bandwidth, using optical spectra acquired after the reconfigurable optical add/drop multiplexers (ROADMs) filters. We assess the performance of the proposed method, considering laser drift and filters bandwidth tightening scenarios, showing quite good estimation accuracy under such conditions.</p>
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Zenodo
2020-05-18
info:eu-repo/semantics/conferencePaper
3979495
user-eu
award_title=Future Optical Networks for Innovation, Research and Experimentation; award_number=765275; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/765275; funder_id=00k4n6c32; funder_name=European Commission;
1597193962.975693
941878
md5:2eceead48e172d47ad174a4df473dbb6
https://zenodo.org/records/3979496/files/Experimental Demonstration of a Machine.pdf
public