Analysis and prediction of path loss in UAVBS air-to-ground communication using neural networks

Abstract

Unmanned aerial vehicles bases stations (UAVBS) have many applications in telecommunications. Enables integration into systems in order to provide network signals for users on the ground. The electromagnetic signal from the UAV is characterized by air-to-ground propagation. At different altitudes, the signal suffers losses along the way, thus facing several problems related to transmissions, such as attenuation, fading, and distortion. This paper studies UAV air-to-ground path loss at different altitudes of the UAV. To this, implement a field measurement campaign, which collects and analyzes the signal strength in wireless networks. Finally, it proposes the use of recurrent neural networks to predict the propagation loss in the network. The results were found to show good accuracy in the chosen scenario.