Learning in the Sky: TowardsEfficient 3D Placement of UAVs

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations to support cellular networks can deliver a fast and flexible solution for serving high and varying traffic demand. In order to adequately leverage the benefit of UAVs deployment, their efficient placement is of utmost importance, and requires to intelligently adapt to the environment changes. In this paper, we propose novel learning-based mechanisms for the three-dimensional deployment of UAVs assisting terrestrial networks in the downlink for overloaded situations. The problem is modeled as a game among UAVs. To solve the game, we utilize tools from reinforcement learning, and develop low complexity algorithms based on the multi-armed bandit and satisfaction methods to learn UAVs' locations. Simulation results reveal that the proposed satisfaction based UAV placement algorithm can yield significant performance gains up to about 50% and 41% in terms of throughput and the number of outage users, respectively, compared to a learning based benchmark algorithm.