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

Regret based learning for UAV assisted LTE-U/WiFi public safety networks

Abstract Broadband wireless communication is of critical importance during public safety scenarios as it facilitates situational awareness capabilities for first responders and victims. In this paper, the use of LTE-Unlicensed (LTE-U) technology for unmanned aerial base stations (UABSs) is investigated as an effective approach to enhance the achievable broadband throughput during emergency situations by utilizing the unlicensed spectrum. In particular, we develop a game theoretic framework for load balancing between LTE-U UABSs and WiFi access points (APs), based on the users’ link qualities as well as the loads at the UABSs and the ground APs. To solve this game, we propose a regret-based learning (RBL) dynamic duty cycle selection (DDCS) method for configuring the transmission gaps in LTE-U UABSs, to ensure a satisfactory throughput for all users. Simulation results show that the proposed RBL-DDCS yields an improvement of 32% over fixed duty cycle LTE-U transmission, and an improvement of 10% over Q-learning based DDCS