Deep Learning For Experimental Hybrid Terrestrial and Satellite Interference Management
This addresses interference intrusion that reduces network efficiency in wireless standards, though it appears incremental as it applies existing deep learning methods to a specific domain.
The paper tackles interference management in wireless communications by proposing a deep learning system that detects and classifies interference across multiple radio standards, even at high signal-to-interference ratios, using real signals from terrestrial and satellite networks.
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.