Satellite to Ground Station, Attenuation Prediction for 2.4–72 GHz Using LTSM, an Artificial Recurrent Neural Network Technology

Abstract

Satellite communication links suffer from arbitrary weather phenomena such as clouds, rain, snow, fog, and dust. Furthermore, when signals approach the ground station, they have to overcome buildings blocking the direct access to the ground station. This work proposes a model to predict the remaining signal strength for the next timeframe after deducting the attenuation and disruption impact caused during its propagation from the satellite to the ground station. The proposed model can be adjusted to comply with any geographic region and a broad spectrum of frequencies. We employ LTSM, an artificial recurrent neural network technology, providing a time-dependent prediction. We can instantly calibrate the satellite outgoing signal strength to overcome the predicted attenuation, resulting in satellite energy saving using this prediction.