Joint Optimization of UAV 3-D Deployment, Task Scheduling, Offloading Ratio, and Resource Allocation in UAV-Enabled MEC Networks

Abstract

Uncrewed aerial vehicle (UAV)-enabled mobile edge computing (MEC) has emerged as a promising paradigm for future wireless communication systems by providing flexible and efficient computation offloading services to resource-limited ground user devices (UDs). However, the nonuniform user distribution and mobility, heterogeneous computation demands, and scarce UAV resources pose significant challenges in simultaneously achieving high system performance and long-term service fairness. To address these challenges, this article proposes a fairness-driven joint optimization framework that integrates UAV 3-D deployment, task scheduling, offloading ratio, and resource allocation. First, the original mixed-integer-nonlinear-programming (MINLP) problem is decomposed into two tractable subproblems, namely, the UAV deployment subproblem and the task scheduling subproblem. Based on this decomposition, a fairness-driven UAV deployment optimization and task scheduling (FUDOTS) algorithm is developed to maximize fairness-based system throughput. Extensive simulation results demonstrate that the effectiveness of the proposed FUDOTS algorithm, which consistently outperforms other existing benchmark schemes in terms of Jain’s fairness index (JFI) and fairness-based system throughput.