Efficient Detection of Rare Beacon Events in GEO Satellite Communication Systems using Deep Learning

Geosynchronous satellite (GEO) communications are highly susceptible to interference from environments such as rain, clouds, and hail. This exhibits a technical challenge to accurately detect the status of the weak Q-band (39.4 GHz) satellite beacon signals, which is critical for ensuring reliable satellite pointing using the ground station antenna. This paper exploits recent advances in deep learning to cope with this challenge. The proposed approach based on deep neural networks (DNN) and filtering (Filter-DNN) classifies rare events such as cloud, mist, rain as Non-line of sight (NLOS) and ordinary clear skies as Line of sight (LOS). Beacon data from a GEO satellite (Alphasat) ground station under two different attenuation conditions is used for validation. The experimental results show that our method can detect the rare event with an accuracy score of 92% by using only 2000 data sample points, while conventional approaches such as MUSIC require about 100k sample points to maintain detection. These results indicate that the proposed technique could be a promising tool for achieving satisfactory results in space exploration and gain insights into GEO satellite communication problems.