We introduce a novel weather-adaptive approach for multi-step forecasting of multi-scale SOP changes in aerial fiber links. By harnessing the discrete wavelet transform and incorporating weather data, our approach improves forecasting accuracy by over 65% in RMSE and 63% in MAPE compared to baselines.
@article{arxiv.2409.03663,
title = {Weather-Adaptive Multi-Step Forecasting of State of Polarization Changes in Aerial Fibers Using Wavelet Neural Networks},
author = {Khouloud Abdelli and Matteo Lonardi and Jurgen Gripp and Samuel Olsson Fabien Boitier and Patricia Layec},
journal= {arXiv preprint arXiv:2409.03663},
year = {2024}
}