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Modeling EDFA gain ripple and filter penalties with machine learning for accurate QoT estimation

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10.1109/JLT.2020.2975081
 
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hdl:2117/329668

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Mahajan, Ankush
Christodoulopoulos, Konstantinos
Martínez, Ricardo
Spadaro, SalvatoreMés informacióMés informacióMés informació
Muñoz, Raul
Document typeArticle
Defense date2020-05-01
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
ProjectONFIRE - Future Optical Networks for Innovation, Research and Experimentation (EC-H2020-765275)
Abstract
For reliable and efficient network planning and operation, accurate estimation of Quality of Transmission (QoT) before establishing or reconfiguring the connection is necessary. In optical networks, a design margin is generally included in a QoT estimation tool (Qtool) to account for modeling and parameter inaccuracies, ensuring the acceptable performance. In this work, we use monitoring information from an operating network combined with supervised machine learning (ML) techniques to understand the network conditions. In particular, we model the penalties generated due to i.) Erbium Doped Fiber Amplifier (EDFA) gain ripple effect, and ii.) filter spectral shape uncertainties at Reconfigurable Optical Add and Drop Multiplexer (ROADM) nodes. Enhancing the Qtool with the proposed ML regression models yields estimates for new or reconfigured connections that account for these two effects, resulting in more accurate QoT estimation and a reduced design margin. We initially propose two supervised ML regression models, implemented with Support Vector Machine Regression (SVMR), to estimate the individual penalties of the two effects and then a combined model. On Deutsche Telekom (DT) network topology with 12 nodes and 40 bidirectional links, we achieve a design margin reduction of ~1dB for new connection requests.
Description
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
CitationMahajan, A. [et al.]. Modeling EDFA gain ripple and filter penalties with machine learning for accurate QoT estimation. "Journal of lightwave technology", 1 Maig 2020, vol. 38, núm. 9, p. 2616-2629. 
URIhttp://hdl.handle.net/2117/329668
DOI10.1109/JLT.2020.2975081
ISSN0733-8724
Publisher versionhttps://ieeexplore.ieee.org/document/9003295
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  • Departament de Teoria del Senyal i Comunicacions - Articles de revista [2.469]
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  • Doctorat en Teoria del Senyal i Comunicacions - Articles de revista [188]
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