  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.
