  Interference Management is a vast topic present in many disciplines. The
majority of wireless standards suffer the drawback of interference intrusion
and the network efficiency drop due to that. Traditionally, interference
management has been addressed by proposing signal processing techniques that
minimize their effects locally. However, the fast evolution of future
communications makes difficult to adapt to new era. In this paper we propose
the use of Deep Learning techniques to present a compact system for
interference management. In particular, we describe two subsystems capable to
detect the presence of interference, even in high Signal to Interference Ratio
(SIR), and interference classification in several radio standards. Finally, we
present results based on real signals captured from terrestrial and satellite
networks and the conclusions unveil the courageous future of AI and wireless
communications.
