  Unmanned Aerial Vehicles (UAVs) are emerging as important users of
next-generation cellular networks. By operating in the sky, UAV users
experience very different radio conditions than terrestrial users, due to
factors such as strong Line-of-Sight (LoS) channels (and interference) and Base
Station (BS) antenna misalignment. As a consequence, the UAVs may experience
significant degradation to their received quality of service, particularly when
they are moving and are subject to frequent handovers. The solution is to allow
the UAV to be aware of its surrounding environment, and intelligently connect
into the cellular network taking advantage of this awareness. In this paper we
present REgression and deep Q-learning for Intelligent UAV cellular user to
Base station Association (REQIBA), a solution that allows a UAV flying over an
urban area to intelligently connect to underlying BSs, using information about
the received signal powers, the BS locations, and the surrounding building
topology. We demonstrate how REQIBA can as much as double the total UAV
throughput, when compared to heuristic association schemes similar to those
commonly used by terrestrial users. We also evaluate how environmental factors
such as UAV height, building density, and throughput loss due to handovers
impact the performance of our solution.
