Publication:
Deep Neural Network-Based QoT Estimation for SMF and FMF Links

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.contributor.authorAmirabadi, Mohammad Ali
dc.contributor.authorKahaei, Mohammad Hossein
dc.contributor.authorNezamalhosseini, S. Alireza
dc.contributor.authorArpanaei, Farhad
dc.contributor.authorCarena, Andrea
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2024-02-09T13:35:00Z
dc.date.available2024-02-09T13:35:00Z
dc.date.issued2023-03-15
dc.description.abstractQuality of transmission (QoT) estimation tools for fiber links are the enabler for the deployment of reconfigurable optical networks. To dynamically set up lightpaths based on traffic request, a centralized controller must base decisions on reliable performance predictions. QoT estimation methods can be categorised in three classes: exact analytical models which provide accurate results with heavy computations, approximate formulas that require less computations but deliver a reduced accuracy, and machine learning (ML)-based methods which potentially have high accuracy with low complexity. To operate an optical network in real-time, beside accurate QoT estimation, the speed in delivering results is a strict requirement. Based on this, only the last two categories are candidates for this application. In this paper, we present a deep neural network (DNN) structure for QoT estimation considering both regular single-mode fiber (SMF) and future few-mode fiber (FMF) proposed to increase the overall network capacity. We comprehensively explore ML-based regression methods for estimating generalized signal-to-noise ratio (GSNR) in partial-load SMF and FMF links. Synthetic datasets have been generated using the enhanced Gaussian noise (EGN) model. Results indicate that the proposed DNN-based regressor can provide better accuracy along with less computation complexity, compared with other state-of-the-art ML methods as well as closed-form-EGN and closed-form-GN modelsen
dc.description.sponsorshipThis work was supported by the Italian Ministry for University and Research (PRIN 2017, project FIRST). Computational resources were provided by HPC@POLITO (http://www.hpc.polito.it). Farhad Arpanaei acknowledges support from the CONEX-Plus programme funded by Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 801538es
dc.identifier.bibliographicCitationAmirabadi, M. A., Kahaei, M. H., Nezamalhosseini, S. A., Arpanaei, F., & Carena, A. (2023). Deep Neural Network-Based QoT Estimation for SMF and FMF Links. Journal Of Lightwave Technology, 41(6), 1684-1695es
dc.identifier.doihttps://doi.org/10.1109/JLT.2022.3225827
dc.identifier.issn0733-8724
dc.identifier.publicationfirstpage1684es
dc.identifier.publicationissue6es
dc.identifier.publicationlastpage1695es
dc.identifier.publicationtitleJOURNAL OF LIGHTWAVE TECHNOLOGYes
dc.identifier.publicationvolume41es
dc.identifier.urihttps://hdl.handle.net/10016/40014
dc.identifier.uxxiAR/0000033916
dc.language.isoenges
dc.publisherIEEEes
dc.relation.projectIDinfo:eu-repo/granAgreement/EC/801538
dc.rights© IEEE
dc.rights.accessRightsopen access
dc.subject.otherDeep neural networken
dc.subject.otherFew-mode fiberen
dc.subject.otherQuality of transmission estimationen
dc.subject.otherTransmission estimationen
dc.subject.otherRegressionen
dc.subject.otherSingle-mode fiberen
dc.titleDeep Neural Network-Based QoT Estimation for SMF and FMF Linksen
dc.typeresearch article
dc.type.hasVersionAM
dspace.entity.typePublication
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