Modeling Short-Term Effects of Rain on Satellite Link using Machine Learning

The received signal at the ground station for a satellite link is affected by the stochastic nature of atmospheric channel. Adverse weather events such as rain not only attenuates the signal but also increases noise, scintillation fading and multipath effects that cause rapid variations in the received signal at the ground station. In order to analyze the stochastic effects of weather phenomenon on the link performance on short-term basis, both the slowly changing signal attenuation and the rapid variations caused by channel at the receiver has to be studied. In this work, we first analyze the short-term effects of rain on the statistical and spectral properties of fast varying signal component affecting the link performance. Following this, we model such parameters using several features extracted from the slowly varying signal component with support vector machine (SVM). We show an interesting result that the statistical and spectral properties of fast varying signal under rainy channel conditions can be predicted with very high accuracy using SVM. The prediction of such parameters will lead to receiver design adaptive to varying channel dynamics affecting the link performance under rainy conditions.