Archivio Istituzionale della RicercaWe propose a machine learning approach leveraging state-of-polarization dynamics to detect overlapping fiber anoma- lies. Simulated disturbances and XGBoost classification achieve near-perfect accuracy under noise, enabling precise identification of concurrent events and enhancing both fault detection and physical layer security in optical communication networks
Intelligent Detection of Overlapping Fiber Anomalies in Optical Networks Using Machine Learning / Malik, Gulmina; Dipto, Imran Chowdhury; Masood, Muhammad Umar; Cheruvakkadu Mohamed, Mashboob; Straullu, Stefano; Kishore Bhyri, Sai; Maria Galimberti, Gabriele; Pedro, João; Napoli, Antonio; Wakim, Walid; Curri, Vittorio. - (2025). (Intervento presentato al convegno 2025 IEEE Photonics Society Summer Topicals tenutosi a Berlin (Ger) nel 21-23 Luglio 2025).
Intelligent Detection of Overlapping Fiber Anomalies in Optical Networks Using Machine Learning
Gulmina Malik;Imran Chowdhury Dipto;Muhammad Umar Masood;Mashboob Cheruvakkadu Mohamed;Stefano Straullu;Vittorio Curri
2025
Abstract
We propose a machine learning approach leveraging state-of-polarization dynamics to detect overlapping fiber anoma- lies. Simulated disturbances and XGBoost classification achieve near-perfect accuracy under noise, enabling precise identification of concurrent events and enhancing both fault detection and physical layer security in optical communication networks
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https://hdl.handle.net/11583/3002698