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  • 1.
    Natalino, Carlos
    et al.
    Chalmers University of Technology, Sweden.
    Udalcovs, Aleksejs
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Wosinska, Lena
    Chalmers University of Technology, Sweden.
    Ozolins, Oskars
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Furdek, Marija
    Chalmers University of Technology, Sweden.
    Spectrum Anomaly Detection for Optical Network Monitoring using Deep Unsupervised Learning2021In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 25, no 5, p. 1583-1586Article in journal (Refereed)
    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. 

  • 2.
    Öhlén, Peter
    et al.
    Ericsson Research, Sweden.
    Skubic, Björn
    Ericsson Research, Sweden.
    Rostami, Ahmad
    Ericsson Research, Sweden.
    Fiorani, Matteo
    RISE, Swedish ICT, Acreo, Broadband Technology. KTH Royal Institute of Technology, Sweden.
    Monti, Paolo
    RISE, Swedish ICT, Acreo, Broadband Technology. KTH Royal Institute of Technology, Sweden.
    Ghebretensaé, Zere
    Ericsson Research, Sweden.
    Mårtensson, Jonas
    RISE, Swedish ICT, Acreo.
    Wang, Kun
    RISE, Swedish ICT, Acreo.
    Wosinska, Lena
    RISE, Swedish ICT, Acreo, Broadband Technology. KTH Royal Institute of Technology, Sweden.
    Data Plane and Control Architectures for 5G Transport Networks2016In: Journal of Lightwave Technology, ISSN 0733-8724, E-ISSN 1558-2213, Vol. 34, no 6, p. 1501-1508Article in journal (Refereed)
    Abstract [en]

    Next generation 5G mobile system will support the vision of connecting all devices that benefit from a connection, and support a wide range of services. Consequently, 5G transport networks need to provide the required capacity, latency, and flexibility in order to integrate the different technology domains of radio, transport, and cloud. This paper outlines the main challenges, which the 5G transport networks are facing and discusses in more detail data plane, control architectures, and the tradeoff between different network abstraction models.

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