Despite the remarkable progress of fiber optics in communication, little attention has been devoted to the automatic detection of anomalies in optical spectra, i.e., poorly transmitted channels. This task is typically addressed by ad-hoc heuristics that fall short in spectra presenting heavy distortions caused by optical amplifiers during transmission. We propose a method based on a joint optimization procedure for estimating the major trends that characterize the spectrum, enabling the detection of anomalies even in the presence of few channels and heavy distortions. Our experiments have shown that the proposed method can successfully localize anomalies achieving more than 98% accuracy, outperforming all competitors.

Anomaly Detection in Optical Spectra VIA Joint Optimization / Rizzo, Antonino Maria; Magri, Luca; Invernizzi, Pietro; Sozio, Enrico; Piciaccia, Stefano; Tanzi, Alberto; Binetti, Stefano; Alippi, Cesare; Boracchi, Giacomo. - ELETTRONICO. - (2023), pp. 1-5. (Intervento presentato al convegno 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) tenutosi a Rhodes Island (Greece) nel 04-10 June 2023) [10.1109/ICASSP49357.2023.10096609].

Anomaly Detection in Optical Spectra VIA Joint Optimization

Rizzo, Antonino Maria;Magri, Luca;Alippi, Cesare;
2023

Abstract

Despite the remarkable progress of fiber optics in communication, little attention has been devoted to the automatic detection of anomalies in optical spectra, i.e., poorly transmitted channels. This task is typically addressed by ad-hoc heuristics that fall short in spectra presenting heavy distortions caused by optical amplifiers during transmission. We propose a method based on a joint optimization procedure for estimating the major trends that characterize the spectrum, enabling the detection of anomalies even in the presence of few channels and heavy distortions. Our experiments have shown that the proposed method can successfully localize anomalies achieving more than 98% accuracy, outperforming all competitors.
2023
978-1-7281-6327-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2980792