GenAI-Based Models for NGSO Satellites Interference Detection - Almoatssimbillah Saifaldawla; Flor Ortiz; Eva Lagunas; Abuzar B. M. Adam; Symeon Chatzinotas (IEEE Transactions on Machine Learning in Communications and Networking)
Recent advancements in satellite communications have highlighted the challenge of interference detection, especially with the new generation of non-geostationary orbit satellites (NGSOs) that share the same frequency bands as legacy geostationary orbit satellites (GSOs). Despite existing radio regulations during the filing stage, this heightened congestion in the spectrum is likely to lead to instances of interference during real-time operations. This paper addresses the NGSO-to-GSO interference problem by proposing advanced artificial intelligence (AI) models to detect interference events. In particular, we focus on the downlink interference case, where signals from low-Earth orbit satellites (LEOs) potentially impact the signals received at the GSO ground stations (GGSs). In addition to the widely used autoencoder-based models (AEs), we design, develop, and train two generative AI-based models (GenAI), which are a variational autoencoder (VAE) and a transformer-based interference detector (TrID). These models generate samples of the expected GSO signal, whose error with respect to the input signal is used to flag interference. Actual satellite positions, trajectories, and realistic system parameters are used to emulate the interference scenarios and validate the proposed models. Numerical evaluation reveals that the models exhibit higher accuracy for detecting interference in the time-domain signal representations compared to the frequency-domain representations. Furthermore, the results demonstrate that TrID significantly outperforms the other models as well as the traditional energy detector (ED) approach, showing an increase of up to 31.23% in interference detection accuracy, offering an innovative and efficient solution to a pressing challenge in satellite communications.