A Lyapunov-Based Approach to Joint Optimization of Resource Allocation and 3D Trajectory for Solar-Powered UAV MEC Systems

Due to its agility, reusability, and programmability, the unmanned aerial vehicle (UAV) can be utilized as a flying base station in mobile edge computing (MEC) systems, providing cost-effective computation services to distributed ground devices in the absence of terrestrial infrastructure. A defect of traditional UAVs is that they rely heavily on the onboard limited battery for the power supply, severely restricting UAVs’ operating endurance and flying range. To tackle this problem, we consider using a solar-powered UAV as the edge server for sensing data collection and processing. However, owing to the atmospheric absorption, the amount of harvested solar energy increases with the flying altitude, resulting in a non-trivial tradeoff between energy harvesting and communication performance. In addition, the dynamics of the moving clouds also make energy harvesting exhibit stochastic variations in the solar panel’s output, rendering the instability of the energy conversion. In this paper, given the randomness of energy and data arrivals, we propose a Lyapunov-based method to maximize the long-term system throughput, subject to the time average constraints on the solar power supply, the data queue stability, and the energy consumption of the devices. Specifically, without knowing the future system knowledge, we formulate the problem as a multi-stage online stochastic optimization and decompose the original problem into per-slot deterministic optimization problems. In each slot, we iteratively optimize the data sensing rate, the computation offloading, the communication resource allocation, and the 3D trajectory of the UAV. The proposed algorithm can adaptively adjust the UAV’s altitude according to its residual energy, thus striking a balance between energy harvesting and system throughput. Furthermore, it has low complexity which makes it suitable for online implementation. Extensive simulations have demonstrated the effectiveness of the algorithm, in that, it significantly outperforms the benchmark schemes in the system throughput, while satisfying the prescribed time average constraints at the same time.