Modulated vs. Unmodulated Polarization Signatures for ML-Based Fiber Sensing

Fast and accurate detection of various physical layer threats that target optical networks is key to secure and reliable global communications. Conventional monitoring methods often fail to detect subtle anomalies, which requires advanced sensing techniques. Machine Learning (ML) analysis of the State of Polarization (SOP) of unmodulated signals was shown to successfully detect such disturbances. However, real-world networks typically operate with high-speed modulated signals, which may alter SOP behavior and challenge the applicability of ML techniques developed for unmodulated signals. This paper investigates the implications of signal modulation supporting high-data rates on the interpretability of SOP signatures. We perform the first experimental comparison of anomaly detection approaches based on SOP for modulated and unmodulated optical signals subjected to identical physical perturbations caused by fiber tapping and vibrations. We analyze four representative events under both signal modalities and assess the impact of modulation on SOP dynamics using a 77 km fiber link in a real-world metro network. We design four datasets that isolate, merge, and jointly classify the different signal modalities, and compare the performance of ten best-performing supervised ML techniques in each case. Our findings indicate that modulated signals tend to exhibit smoother SOP trajectories, likely due to the temporal averaging effects introduced by high symbol rates, wherein rapid symbol transitions suppress highfrequency polarization noise. Importantly, this smoothening does not obscure the slower, event-induced polarization drifts observed during physical disturbances, allowing ML models to reliably differentiate between different physical events (e.g., bending, vibrations) and signal modalities (modulated vs. unmodulated), achieving accuracy values between 97.12% and 98.47%.