# Deep Learning For Experimental Hybrid Terrestrial and Satellite Interference Management

## TL;DR

This paper introduces deep learning-based systems for detecting and classifying interference in terrestrial and satellite networks, offering a compact and adaptable solution to improve wireless communication efficiency.

## Contribution

It presents novel deep learning subsystems capable of interference detection and classification across multiple radio standards, addressing limitations of traditional signal processing methods.

## Key findings

- Effective interference detection even at high SIR levels
- Successful classification across various radio standards
- Real signal experiments demonstrate system viability

## Abstract

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.