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Spectrum Anomaly Detection for Optical Network Monitoring using Deep Unsupervised Learning
Chalmers University of Technology, Sweden.
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0003-3754-0265
Chalmers University of Technology, Sweden.ORCID iD: 0000-0001-6704-6554
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0001-9839-7488
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2021 (English)In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 25, no 5, p. 1583-1586Article in journal (Refereed) Published
Abstract [en]

Accurate and efficient anomaly detection is a key enabler for the cognitive management of optical networks, but traditional anomaly detection algorithms are computationally complex and do not scale well with the amount of monitoring data. Therefore, we propose an optical spectrum anomaly detection scheme that exploits computer vision and deep unsupervised learning to perform optical network monitoring relying only on constellation diagrams of received signals. The proposed scheme achieves 100% detection accuracy even without prior knowledge of the anomalies. Furthermore, operation with encoded images of constellation diagrams reduces the runtime by up to 200 times. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2021. Vol. 25, no 5, p. 1583-1586
Keywords [en]
Anomaly detection, autoencoder, Constellation diagram, Deep unsupervised learning, Optical fiber networks, optical network monitoring, Optical noise, Optical sensors, Signal to noise ratio, Training, Deep learning, Fiber optic networks, Unsupervised learning, Anomaly-detection algorithms, Cognitive managements, Constellation diagrams, Detection accuracy, Network Monitoring, Optical spectra, Prior knowledge, Received signals
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Engineering and Technology
Identifiers
URN: urn:nbn:se:ri:diva-52552DOI: 10.1109/LCOMM.2021.3055064Scopus ID: 2-s2.0-85100519047OAI: oai:DiVA.org:ri-52552DiVA, id: diva2:1535300
Available from: 2021-03-08 Created: 2021-03-08 Last updated: 2023-06-02Bibliographically approved

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Udalcovs, AleksejsWosinska, LenaOzolins, Oskars

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