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Learning from the optical spectrum: failure detection and identification [Invited]

Author
Shariati, M.; Ruiz, M.; Comellas, J.; Velasco, L.
Type of activity
Journal article
Journal
Journal of lightwave technology
Date of publication
2018-07-23
Volume
37
Number
2
First page
433
Last page
440
DOI
https://doi.org/10.1109/JLT.2018.2859199 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/2117/125289 Open in new window
URL
https://ieeexplore.ieee.org/document/8418780 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, we explore several machine learning approaches for soft-failure detection, identification and localization that take advantage of OSAs. In particular, we present three different solutions for the two most common filter-related soft-failures; filter shift and tight filtering which noticeably deform the expected shape of the optical spectru...
Citation
Shariati, M., Ruiz, M., Comellas, J., Velasco, L. Learning from the optical spectrum: failure detection and identification [Invited]. "Journal of lightwave technology", 23 Juliol 2018, vol. 37, núm. 2, p. 433-440.
Keywords
Elastic optical networks, Feature extraction, Filtering algorithms, Monitoring, Optical fiber networks, Optical filters, Optical noise, Optical performance monitoring, Soft-failure detection and identification
Group of research
GCO - Optical Communications Group

Participants

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