Applications of digital twin for autonomous zero-touch optical networking [Invited]

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 ReviewedPostprint (author's final draft

Dual time and frequency domain optical layer digital twin

We demonstrate a digital twin for failure detection in optical networks. Artificial neural networks-based models for optical constellation analysis enable predicting the transmitted signal in the time domain whereas analytical models are usually used to estimate their spectral evolution.This project has received funding from the European Union Horizon 2020 MSCA-EID MENTOR (G.A. 956713), the H2020 B5G-OPEN (G.A. 101016663), the MICINN IBON (PID2020- 114135RB-I00), and the ICREA Institution.Peer ReviewedPostprint (author's final draft

Degradation detection and severity estimation by exploiting an optical time and frequency digital twin

We exploit the intrinsic advantages of a time and frequency domain digital twin to detect degradations and to estimate their severity. Noticeable performance shown for filter failures confirms the usefulness of this approach.The research leading to these results has received funding from the European Community through the MSCA MENTOR (G.A. 956713) and the H2020 B5G-OPEN (G.A. 101016663) projects, the AEI through the IBON (PID2020-114135RB-I00) project, and by the ICREA institutionPeer ReviewedPostprint (author's final draft