Computation Efficiency Maximization in Multi-UAV-Enabled Mobile Edge Computing Systems Based on 3D Deployment Optimization

Unmanned aerial vehicles (UAVs) have been widely devoted to mobile edge computing (MEC) systems that have limited resources to provide high-quality computing and communication services for Internet of Things (IoT) terminals. Energy-efficient computation and resource allocation are key issues for the sustainable operation of the above-mentioned systems. The 3D deployment optimization of multi-UAVs is also crucial to maximizing the system's computation efficiency. In this paper, we discuss a multi-UAV-enabled MEC system. To maximize the computation efficiency of the terminal system, we consider jointly optimizing the terminal's CPU frequency, transmission power, offloading correlation decision, and the 3D position and beamwidth of the UAV. Since the original problem is a mixed-integer nonlinear programming (MINLP) problem with a fractional structure, which is difficult to solve directly. Based on Dinkelbach's method, convex optimization theory, and greedy strategy, we simplify the mathematical model and propose a four-stage alternating iterative computation efficiency maximization algorithm(FICEM) to solve the problem. The simulation results indicate that the algorithm converges fast in various network scenarios and that its computation efficiency is better than that of the baseline algorithm. In addition, the simulation results also manifest the effect of different network parameters on the computation efficiency of the terminal system.