A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, ease of availability, and ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail when compared to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios

Airborne Wireless Communication Modeling and Analysis with MATLAB

Over the past decade, there has been a dramatic increase in the use of unmanned aerial vehicles (UAV) for military, commercial, and private applications. Critical to maintaining control and a use for these systems is the development of wireless networking systems [1]. Computer simulation has increasingly become a key player in airborne networking developments though the accuracy and credibility of network simulations has become a topic of increasing scrutiny [2-5]. Much of the inaccuracies seen in simulation are due to inaccurate modeling of the physical layer of the communication system. This research develops a physical layer model that combines antenna modeling using computational electromagnetics and the two-ray propagation model to predict the received signal strength. The antenna is modeled with triangular patches and analyzed by extending the antenna modeling algorithm by Sergey Makarov, which employs Rao-Wilton-Glisson basis functions. The two-ray model consists of a line-of-sight ray and a reflected ray that is modeled as a lossless ground reflection. Comparison with a UAV data collection shows that the developed physical layer model improves over a simpler model that was only dependent on distance. The resulting two-ray model provides a more accurate networking model framework for future wireless network simulations