Modeling the Drone-to-Drone Communications Channel for Urban Environments

Preventing mid-air collisions between autonomously flying drones in urban airspace will be a crucial task for the future Urban Air Mobility (UAM). Especially in dense urban scenarios, the direct and fast information exchange between drones based on Drone-to-Drone (D2D) communications is a promising technology for enabling reliable collision avoidance systems. In order to design and validate respective communication systems, accurate knowledge about the specific underlying propagation characteristics is inevitable. Therefore, we performed a wideband channel sounding measurement campaign with two flying drones in different urban scenarios and investigated the underlying channel propagation conditions in previous works. In this work, we present a geometrical-statistical architecture to model the D2D communications channel for urban environments. It considers the identified propagation elements and effects from our measurements and shall serve as a basis to easily incorporate further statistics from our measurements or related other measurement campaigns. We show its feasibility by comparing the preliminary channel model results with a simple parameterization based on a measured scenario. The modeled channel characteristics show a good match with the measurements, but further investigation the underlying statistics in the measurements will refine model in the next.