Cellular-Connected Multi-UAV MEC Networks: An Online Stochastic Optimization Approach

In this paper, we consider a mobile edge computing (MEC) network where multiple cellular-connected unmanned aerial vehicles (UAVs) can offload their computation tasks to multiple ground base stations (GBSs). In practice, the UAVs are generally unable to master stochastic information of task arrival and channel changes in advance, which may cause a severe issue in terms of energy consumption. Therefore, we formulate a stochastic optimization problem with the goal of minimizing the average weighted sum energy consumption, by jointly optimizing UAV-GBS associations, communication and computation resource allocation, and three-dimensional (3D) UAV trajectories, during which a velocity-triggered penalty term (VTPT) is designed to suppress a large amount of the energy consumption of the UAVs. To handle the stochastic problem, we propose an online resource allocation and trajectory optimization algorithm with outer and inner structures. The outer structure transforms the original problem to a deterministic one by applying the Lyapunov-based optimization framework. The inner structure solves the obtained deterministic problem via the Lagrange duality method and the successive convex approximation technique, based on the block coordinate descent framework. Numerical results demonstrate that: 1) VTPT dramatically decreases the UAVs’ energy consumption, and 2) the proposed algorithm not only reduces the energy consumption but also ensures the computation queue stability compared with other benchmark schemes.

I. Introduction

The unprecedented expansion of smart devices (e.g., tablets, mobile phones, and wearable devices) and their computation-intensive applications (e.g., online gaming, automatic driving, and three-dimensional (3D) modeling) are expediting the development of mobile edge computing (MEC) technology. MEC is a new network architecture that aims at substantially relieving the network’s computation burden and reducing the service delay, by pushing the computation services down at the edge of networks [1], [2]. Thus far, MEC has been key enabling technology for beyond 5G networks.