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Knowledge management in optical networks: architecture, methods, and use cases [Invited]

Author
Ruiz, M.; Tabatabaeimehr, F.; Velasco, L.
Type of activity
Journal article
Journal
Journal of optical communications and networking
Date of publication
2020-01-01
Volume
12
Number
1
First page
A70
Last page
A81
DOI
10.1364/JOCN.12.000A70
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/2117/175329 Open in new window
URL
https://www.osapublishing.org/jocn/abstract.cfm?uri=jocn-12-1-A70 Open in new window
Abstract
Autonomous network operation realized by means of control loops, where prediction from machine learning (ML) models is used as input to proactively reconfigure individual optical devices or the whole optical network, has been recently proposed to minimize human intervention. A general issue in this approach is the limited accuracy of ML models due to the lack of real data for training the models. Although the training dataset can be complemented with data from lab experiments and simulation, it ...
Citation
Ruiz, M.; Tabatabaeimehr, F.; Velasco, L. Knowledge management in optical networks: architecture, methods, and use cases [Invited]. "Journal of optical communications and networking", 1 Gener 2020, vol. 12, núm. 1, p. A70-A81.
Keywords
Bit error rate, Network topology, Neural networks, Optical network architecture, Optical networks, Optical receivers
Group of research
GCO - Optical Communications Group

Participants