UABeam: UAV-Based Beamforming System Analysis with In-Field Air-to-Ground Channels

Abstract

Precise air-to-ground propagation modeling is imperative for many unmanned aerial vehicle (UAV) applications such as search and rescue, reconnaissance, and disaster recovery. Furthermore, directionalization via MIMO-based beamforming can boost the transmission range by utilizing Channel State Information (CSI). However, the high mobility and flight conditions of drones can threaten the ability to receive accurate CSI in time to achieve such gains. In this work, we design a UAV-based software defined radio (SDR) platform and perform a measurement study to characterize the air-to-ground channel between the aerial platforms and a terrestrial user in practical scenarios such as hovering, encircling, and linear topologies. Our experiments cover multiple carrier frequencies, including cellular (900~MHz and 1800~MHz) and WiFi (5~GHz) bands. Furthermore, we address three baseline issues for deploying drone-based beamforming systems: channel reciprocity, feedback overhead, and update rate for channel estimation. Numerical results show that explicit CSI feedback can increase throughput by 123.9% over implicit feedback and the optimal update rate are similar across frequencies, underscoring the importance of drone-based beamfoming design. We additionally analyze the reciprocity error and find that the amplitude error remained steady while the phase error depends on mobility. Since our study spans many critical frequency bands, these results serve as a fundamental step towards understanding drone- based beamforming systems.