Model-based vs. Data-driven Approaches for Predicting Rain-induced Attenuation in Commercial Microwave Links: A Comparative Empirical Study

Abstract

Real-time analysis and forecasting of rain-induced attenuation patterns in terrestrial microwave links has gained increasing attention in the field of communication and meteorology, enabling preparation for upcoming events. This paper presents an empirical study of model-based and data-driven techniques applied to multi-step predictions of rain attenuation in terrestrial microwave links. Data-driven approaches have been adopted in many research fields, including time series forecasting, which allows the modeling of complex data patterns without assuming a particular model representation. However, the superiority of such algorithms over traditional time series model-based methods has yet to be resolved for short-term rain attenuation predictions. We provide a comprehensive evaluation through empirical analysis using real-world measurements by comparing the performances of six main state-of-the-art algorithms involving two dimensions: the available training dataset and forecast horizon. The empirical results demonstrate the superiority of data-driven algorithms over model-based methods with an increasing gap as the forecast horizon grows, reaching over 20% gain in the RMSE. Nevertheless, adopting data-driven algorithms in rainfall prediction requires a sufficient amount of available data and typically requires a significant number of observed rainfall hours, highlighting the challenge when the dataset is limited or unavailable.