Stochastic Resource Management and Trajectory Optimization for Cellular-Connected Multi-UAV Mobile Edge Computing Systems

Abstract

This paper studies a mobile edge computing (MEC) framework for cellular-connected multiple unmanned aerial vehicles (UAVs), where the UAVs compute the tasks locally or offload them to ground base stations (GBSs). Considering the time-varying characteristics of the task arriving, we formulate a stochastic problem to minimize the system's average weighted sum energy consumption, by optimizing UAV-GBS association, communication and computation resource allocation, and UAVs' trajectories. We apply Lyapunov approach to convert the stochastic problem into a deterministic problem that is then solved by invoking Lagrange duality method and successive convex approximation technique, based on which an online joint optimization algorithm is proposed. Moreover, we design a velocity-triggered penalty term (VTPT) to reduce the UAVs' energy. Numerical results validate the effectiveness of the designed VTPT, and also demonstrate that our proposed algorithm not only decreases the energy consumption but also maintains the task queue stability.