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Learning from the optical spectrum: soft-failure identification and localization

Author
Velasco, L.; Shariati, M.; Vela, A.; Comellas, J.; Ruiz, M.
Type of activity
Presentation of work at congresses
Name of edition
2018 Optical Fiber Communication Conference and Exposition
Date of publication
2018
Presentation's date
2018-03-11
Book of congress proceedings
2018 Optical Fiber Communications Conference and Exposition (OFC): San Diego, California, USA 11–15 March 2018: proceedings
First page
1
Last page
3
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1364/OFC.2018.W1G.1 Open in new window
Project funding
CogniTive 5G application-aware optical metro netWorks Integrating moNitoring, data analyticS and optimization
METRO High bandwidth, 5G Application-aware optical network, with edge storage, compUte and low Latency
Repository
http://hdl.handle.net/2117/123429 Open in new window
URL
https://www.osapublishing.org/abstract.cfm?URI=OFC-2018-W1G.1 Open in new window
Abstract
The availability of coarse-resolution cost-effective Optical Spectrum Analyzers (OSA) allows its widespread deployment in operators' networks. In this paper, several machine learning approaches for failure identification and localization that take advantage of OSAs are presented
Citation
Velasco, L., Shariati, M., Vela, A., Comellas, J., Ruiz, M. Learning from the optical spectrum: soft-failure identification and localization. A: Optical Fiber Communication Conference and Exposition. "2018 Optical Fiber Communications Conference and Exposition (OFC): San Diego, California, USA 11–15 March 2018: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1-3.
Keywords
Cost effectiveness, Failure identification, Learning systems, Machine learning approaches, Optical fiber communication, Optical fibers, Optical spectra, Optical spectrum analyzer, Resolution costs, Soft failure, Spectrum analyzers
Group of research
GCO - Optical Communications Group

Participants