  Spectrum monitoring and interference detection are crucial for the satellite
service performance and the revenue of SatCom operators. Interference is one of
the major causes of service degradation and deficient operational efficiency.
Moreover, the satellite spectrum is becoming more crowded, as more satellites
are being launched for different applications. This increases the risk of
interference, which causes anomalies in the received signal, and mandates the
adoption of techniques that can enable the automatic and real-time detection of
such anomalies as a first step towards interference mitigation and suppression.
In this paper, we present a Machine Learning (ML)-based approach able to
guarantee a real-time and automatic detection of both short-term and long-term
interference in the spectrum of the received signal at the base station. The
proposed approach can localize the interference both in time and in frequency
and is universally applicable across a discrete set of different signal
spectra. We present experimental results obtained by applying our method to
real spectrum data from the Swedish Space Corporation. We also compare our
ML-based approach to a model-based approach applied to the same spectrum data
and used as a realistic baseline. Experimental results show that our method is
a more reliable interference detector.
