Archivio Istituzionale della RicercaThis study introduces an experimentally characterized digital twin framework for training machine learning models to predict EDFA failures. Trained on failure scenarios under fully and partially loaded spectrum conditions, the model achieves 99.6% accuracy
Experimentally Characterized Digital Twin for Machine Learning Based EDFA Failure Prognostics / Mohamed, Mashboob Cheruvakkadu; D'Ingillo, Rocco; Ambrosone, Renato; Masood, Muhammad Umar; Malik, Gulmina; Straullu, Stefano; Nespola, Antonino; Bhyri, Sai Kishore; Galimberti, Gabriele Maria; Pedro, João; Napoli, Antonio; Wakim, Walid; Curri, Vittorio. - (2026). ( Optical Fiber Communication Conference, 2026 Los Angeles (USA) 15-19 March 2026).
Experimentally Characterized Digital Twin for Machine Learning Based EDFA Failure Prognostics
Mohamed, Mashboob Cheruvakkadu;D'Ingillo, Rocco;Ambrosone, Renato;Masood, Muhammad Umar;Malik, Gulmina;Straullu, Stefano;Nespola, Antonino;Curri, Vittorio
2026
Abstract
This study introduces an experimentally characterized digital twin framework for training machine learning models to predict EDFA failures. Trained on failure scenarios under fully and partially loaded spectrum conditions, the model achieves 99.6% accuracy
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Mashboob_ofc26-7.pdf
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Th2A.19.pdf
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https://hdl.handle.net/11583/3009737