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September 10, 2025Open Access

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

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Authors

LSLeyla SadighiTrinity College Dublin CNCarlos NatalinoChalmers University of Technology SKStefan KarlssonFiber Optic Valley

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Implication

This research compares polarizations' impacts on anomaly detection in optical networks, suggesting machine learning techniques maintain accuracy across signal modalities.

Key Points

Modulated signals exhibit smoother polarization trajectories, enhancing anomaly detection accuracy in optical networks.
Accuracy metrics for distinguishing physical events range from 97.12% to 98.47%, demonstrating the effectiveness of machine learning techniques.
The study utilized a 77 km fiber link, providing real-world context for testing both modulated and unmodulated signals.
Four datasets were designed to systematically analyze the effects of signal modulation on State of Polarization dynamics.

Cite This Study

Sadighi et al. (2025) studied this question.

synapsesocial.com/papers/68c1aacc54b1d3bfb60e35f8 https://doi.org/10.36227/techrxiv.175416028.82071655/v1

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Implication

This research compares polarizations' impacts on anomaly detection in optical networks, suggesting machine learning techniques maintain accuracy across signal modalities.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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