Proactive Fiber Damage Detection in Real-time Coherent Receiver

Boitier, Fabien; Lemaire, Vincent; Pesic, Jelena; Chavarria, L.; Layec, Patricia; Bigo, S.; Dutisseuil, E.

doi:10.1109/ecoc.2017.8346077

Cited by 36 publications

(20 citation statements)

References 5 publications

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“…Threshold-based approaches for early-failure detection have been proposed to detect fiber deterioration before the occurrence of a break [62] or to identify anomalous BER trends [61], [63], [64]. In the former scenario, the fiber SOP rotation speed in the Stokes coordinates is monitored and compared to a threshold.…”

Section: B Failure Prediction and Early-detectionmentioning

confidence: 99%

A Tutorial on Machine Learning for Failure Management in Optical Networks

Musumeci

Rottondi

Corani

et al. 2019

J. Lightwave Technol.

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Failure management plays a role of capital importance in optical networks to avoid service disruptions and to satisfy customers' service level agreements. Machine Learning (ML) promises to revolutionize the (mostly manual and humandriven) approaches in which failure management in optical networks has been traditionally managed, by introducing automated methods for failure prediction, detection, localization and identification. This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of optical-network failure management. It then introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks. Finally, for a reader interested in more implementative details, we provide a step-by-step description of how to solve a representative example of a practical failure-management task.

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Section: B Failure Prediction and Early-detectionmentioning

confidence: 99%

A Tutorial on Machine Learning for Failure Management in Optical Networks

Musumeci

Rottondi

Corani

et al. 2019

J. Lightwave Technol.

View full text Add to dashboard Cite

show abstract

“…When the severity of the degradation increases, localizing its root cause is of paramount importance for maintenance purposes [6,7]. It is also possible to predict failures and proactively re-route the traffic [8], which allows a high resiliency of the optical network at the just-enough cost. To this end, dedicated optical protection is replaced with just-in-time optical restoration.…”

Section: Operators' Vision In Near-term and Data Availability The Netmentioning

confidence: 99%

“…To cope with complex and time-variable 5G service scenarios, Machine Learning (ML)-based algorithms [2] are being proposed to facilitate network operation and predictive maintenance. ML algorithms, fed with real measurements, are able to accurately estimate the Quality of Transmission (QoT) of new lightpaths, to anticipate capacity exhaustion and degradations, or to predict and localize failures, among others (see, e.g., [3][4][5][6][7][8]).…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Data Analytics for Optical Networking: Benefits, Architectures, and Use Cases

Velasco

Ruiz²,

Cugini

et al. 2019

IEEE Network

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“…An initial training dataset with 10,000 samples from lab experiments ( [18,27]) was used to train ANNs; specifically, ANNs were configured with 90 inputs (i.e., 30 last values of each Stokes parameter) and 45 hidden neurons. Then, operation started and continuously generated synthetic random samples at a rate of 278 μs (3.6 kHz), emulating real events that included some unobserved during lab experiments, causing SOP and BER fluctuation according to [18].…”

Section: In-field Retrainingmentioning

confidence: 99%

Learning Life Cycle to Speed Up Autonomic Optical Transmission and Networking Adoption

Velasco¹,

Shariati²,

Boitier

et al. 2019

J. Opt. Commun. Netw.

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Autonomic optical transmission and networking requires machine learning (ML) models to be trained with large datasets. However, the availability of enough real data to produce accurate ML models is rarely ensured since new optical equipment and techniques are continuously being deployed in the network. One option is to generate data from simulations and lab experiments, but such data could not cover the whole features space and would translate into inaccuracies in the ML models. In this paper, we propose an ML-based algorithm life cycle to facilitate ML deployment in real operator networks. The dataset for ML training can be initially populated based on the results from simulations and lab experiments. Once ML models are generated, ML retraining can be performed after inaccuracies are detected to improve their precision. Illustrative numerical results show the benefits of the proposed learning cycle for general use cases. In addition, two specific use cases are proposed and demonstrated that implement different learning strategies: (i) a two-phase strategy performing out-of-field training using data from simulations and lab experiments with generic equipment, followed by an in-field adaptation to support heterogeneous equipment (the accuracy of this strategy is shown for a use case of failure detection and identification), and (ii) in-field retraining, where ML models are retrained after detecting model inaccuracies. Different approaches are analyzed and evaluated for a use case of autonomic transmission, where results show the significant benefits of collective learning.

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Proactive Fiber Damage Detection in Real-time Coherent Receiver

Cited by 36 publications

References 5 publications

A Tutorial on Machine Learning for Failure Management in Optical Networks

A Tutorial on Machine Learning for Failure Management in Optical Networks

Monitoring and Data Analytics for Optical Networking: Benefits, Architectures, and Use Cases

Learning Life Cycle to Speed Up Autonomic Optical Transmission and Networking Adoption

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