Non-iterative Optimization of Trajectory and Radio Resource for Aerial Network
This work solves a critical problem for aerial IoT networks by improving network utility through a novel optimization approach, though it is incremental as it builds on existing methods like MDPs.
The paper tackles the joint optimization of trajectory and radio resources in aerial IoT networks to maximize proportional fairness, addressing a flaw in prior coordinate optimization methods that leads to suboptimal solutions. By reformulating the problem as a Markov decision process, they propose a non-iterative framework that significantly outperforms state-of-the-art methods, nearly achieving the global optimum.
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.{https://github.com/hslyu/dbspf}.