  By fully exploiting the mobility of unmanned aerial vehicles (UAVs),
UAV-based aerial base stations (BSs) can move closer to ground users to achieve
better communication conditions. In this paper, we consider a scenario where an
aerial BS is dispatched for covering a maximum number of ground users before
exhausting its on-board energy resources. The resulting trajectory optimization
problem is a mixed integer non-linear problem (MINLP) which is non-convex and
is challenging to solve. As such, we propose an iterative algorithm which
decomposes the problem into two sub-problems by applying both successive convex
optimization and block coordinate descent techniques to solve it. To be
specific, the trajectory of the aerial BS and the user scheduling and
association are alternately optimized within each iteration. In addition, to
achieve better coverage performance and speed up convergence, the problem of
designing the initial trajectory of the UAV is also considered. Finally, to
address the unavailability of accurate user location information (ULI) in
practice, two different robust techniques are proposed to compensate the
performance loss in the existence of inaccurate ULI. Simulation results show
both energy and coverage performance gains for the proposed schemes compared to
the benchmark techniques, with an up to 50% increase in coverage probability
and an up to 20% reduction in energy.
