Learning in the Sky: An Efficient 3D Placement of UAVs
This addresses the challenge of dynamic UAV placement for improved network coverage and capacity, representing an incremental advance in learning-based optimization for UAV-assisted communications.
The paper tackles the problem of efficiently placing UAVs as aerial base stations in 3D to assist terrestrial cellular networks, achieving performance gains of up to 52% in throughput and 74% in reduced dropped users compared to a baseline.
Deployment of unmanned aerial vehicles (UAVs) as aerial base stations can deliver a fast and flexible solution for serving varying traffic demand. In order to adequately 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 a learning-based mechanism for the three-dimensional deployment of UAVs assisting terrestrial cellular networks in the downlink. The problem is modeled as a non-cooperative game among UAVs in satisfaction form. To solve the game, we utilize a low complexity algorithm, in which unsatisfied UAVs update their locations based on a learning algorithm. Simulation results reveal that the proposed UAV placement algorithm yields significant performance gains up to about 52% and 74% in terms of throughput and the number of dropped users, respectively, compared to an optimized baseline algorithm.