Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors
Universitat Politècnica de Catalunya. GCO - Grup de Comunicacions Òptiques
2023
Huge efforts have been paid lastly to study the application of Machine Learning techniques to optical transport networks. Applications include Quality of Transmission (QoT) estimation, failure and anomaly detection, and network automation, just to mention a few. In this regard, the development of Optical Layer Digital Twins able to accurately model the optical layer, reproduce scenarios, and generate expected signals are of paramount importance. In this paper, we introduce two applications of Optical Layer Digital Twins namely, misconfiguration detection and QoT estimation. Illustrative results show the accuracy and usefulness of the proposed applications.
The research leading to these results has received funding from the European Community through the MSCA MENTOR (G.A. 956713) and the HORIZON SEASON (G.A. 101096120) projects, the AEI through the IBON (PID2020-114135RB-I00) project, and by the ICREA institution.
Peer Reviewed
Postprint (author's final draft)
Conference report
English
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica; Machine learning; Optical communications; Failure analysis (Engineering); Optical digital twin; Network automation; Aprenentatge automàtic; Comunicacions òptiques; Anàlisi de fallades (Enginyeria)
Institute of Electrical and Electronics Engineers (IEEE)
https://ieeexplore.ieee.org/document/10144875
info:eu-repo/grantAgreement/EC/H2020/956713/EU/Machine LEarning in Optical NeTwORks/MENTOR
info:eu-repo/grantAgreement/EC/HE/101096120/EU/SElf-mAnaged Sustainable high-capacity Optical Networks/SEASON
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114135RB-I00/ES/AI-POWERED INTENT-BASED PACKET AND OPTICAL TRANSPORT NETWORKS AND EDGE AND CLOUD COMPUTING FOR BEYOND 5G/
Open Access
E-prints [73015]