Title:
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Experimental demonstration of machine-learning-aided QoT estimation in multi-domain elastic optical networks with alien wavelengths
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Author:
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Proietti, Roberto; Chen, Xiaoliang; Zhang, Kaiqi; Liu, Gengchen; Shamsabardeh, Mohammadsadegh; Castro, Alberto; Velasco Esteban, Luis Domingo; Zhu, Zuqing; Yoo, S.J. Ben
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Other authors:
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors; Universitat Politècnica de Catalunya. GCO - Grup de Comunicacions Òptiques |
Abstract:
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In multi-domain elastic optical networks with alien wavelengths, each domain needs to consider intradomain and interdomain alien traffic to estimate and guarantee the required quality of transmission (QoT) for each lightpath and perform provisioning operations. This paper experimentally demonstrates an alien wavelength performance monitoring technique and machine-learning-aided QoT estimation for lightpath provisioning of intradomain/interdomain traffic. Testbed experiments demonstrate modulation format recognition, QoT monitoring, and cognitive routing for a 160 Gbaud alien multi-wavelength lightpath. By using experimental training datasets from the testbed and an artificial neural network, we demonstrated an accurate optical-signal-to-noise ratio prediction with an accuracy of ~95% when using 1200 data points. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica -Machine learning -Routers (Computer networks) -Telecommunication -- Traffic -- Management -Optical communications -Alien wavelength -Multi-domain elastic optical networks -Aprenentatge automàtic -Encaminadors (Xarxes d'ordinadors) -Telecomunicació -- Tràfic -- Gestió -Comunicacions òptiques |
Rights:
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Document type:
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Article - Submitted version Article |
Published by:
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Institute of Electrical and Electronics Engineers (IEEE)
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