  We address a joint trajectory planning, user association, resource
allocation, and power control problem to maximize proportional fairness in the
aerial IoT network, considering practical end-to-end quality-of-service (QoS)
and communication schedules. Though the problem is rather ancient, apart from
the fact that the previous approaches have never considered user- and
time-specific QoS, we point out a prevalent mistake in coordinate optimization
approaches adopted by the majority of the literature. Coordinate optimization
approaches, which repetitively optimize radio resources for a fixed trajectory
and vice versa, generally converge to local optima when all variables are
differentiable. However, these methods often stagnate at a non-stationary
point, significantly degrading the network utility in mixed-integer problems
such as joint trajectory and radio resource optimization. We detour this
problem by converting the formulated problem into the Markov decision process
(MDP). Exploiting the beneficial characteristics of the MDP, we design a
non-iterative framework that cooperatively optimizes trajectory and radio
resources without initial trajectory choice. The proposed framework can
incorporate various trajectory planning algorithms such as the genetic
algorithm, tree search, and reinforcement learning. Extensive comparisons with
diverse baselines verify that the proposed framework significantly outperforms
the state-of-the-art method, nearly achieving the global optimum. Our
implementation code is available at https://github.com/hslyu/dbspf.
