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  1. ieeexplore.ieee.org

    Machine-Learning-Based Soft-Failure Detection and Identification in Optical Networks Abstract: We develop and test several machine-learning methods to perform detection and identification of equipment failures in optical networks. Results, obtained over real BER traces, show above 98% accuracy in most cases with reasonable algorithm complexity. ...
    Author:Shahin Shahkarami, Francesco Musumeci, Filippo Cugini, Massimo TornatorePublished:2018
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      Machine-Learning-Based Soft-Failure Detection and Identification in Optical Networks Abstract: We develop and test several machine-learning methods to perform detection and identification of equipment failures in optical networks. Results, obtained over real BER traces, show above 98% accuracy in most cases with reasonable algorithm complexity. ...

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  3. re.public.polimi.it

    the ML algorithms can be trained. To train the failure detection module (n.b., failure detection is needed to predict if a BER sequence will result into a failure or not) we use different types of ML anomaly-detection classification methods, namely Binary Support Vector Machine (SVM), Random Forest (RF), Multiclass SVM, and neural network
    Author:Shahin Shahkarami, Francesco Musumeci, Filippo Cugini, Massimo TornatorePublished:2018
  4. politesi.polimi.it

    Hence, a solid mechanism for soft failure detection (i.e., recognize anomalies due to failure occurrences), localization (i.e., identify where in the network the failure occurred), and identification (i.e., understand the actual cause of the failure) is crucial, as it may be used by operators to perform traffic re-routing and rapid failure ...
    Author:Shahin Shahkarami, Francesco Musumeci, Filippo Cugini, Massimo TornatorePublished:2018
  5. d197for5662m48.cloudfront.net

    soft failures. We propose a software-defined optical network (SDON) based soft failure detection and identification strategy using a cascaded deep learning model. Time series QoT data of normal and degraded lightpaths obtained through the optical performance monitoring equipment is used to train the proposed cascaded deep learning model.
  6. people.ac.upc.edu

    Failure identification and localization can reduce failure repair times greatly. Failure localization techniques have been proposed mainly for hard failures, while significant work is still required for soft failure detection, identification, and localization. Note that some soft failures could affect signal QoT and eventually evolve to
  7. metro-haul.eu

    Title: "An Introduction to Machine Learning in Optical Transport networks" Abstract: This tutorial provides introductory concepts of Machine Learning (ML) and an overview of its main applications in optical networks. Then our experience in developing some specific applications, as QoT estimation and soft-failure identification, is described ...
  8. ieeexplore.ieee.org

    A convolutional neural network (CNN) based soft failure identifier is proposed. Its superior performance in identifying failure causes including filter shift, filter tightening, ASE noise and nonlinear interference is demonstrated. And its robustness in the scenario with multiple impairment deteriorations is also validated.
  9. ieeexplore.ieee.org

    The rapid progress of 5G, the internet of things (IoT), high-definition online video, and cloud computing have raised high requirements for the capacity of optical networks. To improve capacity, recent works begin to focus on the low-margin optical network. However, reduced margin may lead to soft failures caused by impairments of the physical layer, and if they are not handled in time, link ...
  10. techrxiv.org

    Aug 16, 2024We propose a software-defined optical network (SDON) based soft failure detection and identification strategy using a cascaded deep learning model. Time series QoT data of normal and degraded lightpaths obtained through the optical performance monitoring equipment is used to train the proposed cascaded deep learning model.
  11. networks.cs.ucdavis.edu

    Machine-Learning-Based Soft-Failure Detection and Identification in Optical Networks, OFC, 2018 •Propose a machine learning framework for Pre-FEC BER anomaly detection •Such framework can identify if anomaly was due to narrow filtering or reduced amplification •Sensitivity results on different framework parameters is presented •Good ...

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