3979454
doi
10.23919/ONDM48393.2020.9133025
oai:zenodo.org:3979454
user-eu
Christodoulopoulos, Kostas
Nokia Bell Labs
Martínez, Ricardo
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Spadaro, Salvatore
Universitat Politècnica de Catalunya (UPC)
Muñoz, Raül
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Improving QoT Estimation Accuracy with DGE Monitoring using Machine Learning
Mahajan, Ankush
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Optical Network
QoT Estimation
Monitoring
Machine Learning
Margins
<p>In optical transport networks, Dynamic Gain Equalizers (DGE) are typically used at each link. A DGE selectively attenuates the channels to compensate the cumulative Erbium Doped Fiber Amplifier (EDFA) gain ripple effect on the multi-span link, resulting in almost flat output power at the end of the link. We leverage monitored per link DGE attenuation profiles and coherent receivers Signal to Noise Ratio (SNR) information, and propose a machine learning (ML) based scheme to estimate the EDFA gain ripple penalties for new connections. Using that in realistic simulation scenarios we observed a design margin reduction from ~1dB to ~0.3dBs.</p>
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Zenodo
2020-05-18
info:eu-repo/semantics/conferencePaper
3979453
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;
1597193962.790753
1150457
md5:9087b73ef40a4b2436ec5ed6d5ba716b
https://zenodo.org/records/3979454/files/Improving QoT Estimation Accuracy.pdf
public