Communication-, Computation-, and Control-Enabled UAV Mobile Communication Networks

Unmanned-aerial-vehicles (UAVs)-enabled mobile-edge computing (MEC) networks have shown a huge advantage in providing on-demand communication and computation service for the ground users. To reap the benefit of these integrated networks, reducing their energy consumption becomes a key issue, since both UAVs and ground users are energy-limited devices. To address this problem, this article attempts to provide a novel method that optimizes communication, computation, and control (3C), i.e., user association, computational task offloading, and UAVs flight control, to reduce the communication and computation energy consumption. Specifically, in order to find out the appropriate deployment positions for UAV MEC servers to provide on-demand communication and computation service, we propose the concept of the virtual force field (VFF) based on the user statistical distribution model and then devise a coordinated flight control algorithm for UAV MEC servers. After that, the user association and computational task offloading are optimized alternately. Utilizing the optimal transport theory (OTT), we derive the boundary formulation of the optimal user association and develop an iterative algorithm to approach the optimal association boundary. Then, given the user association, the optimal computational task offloading scheme is investigated. The convergence of the proposed iterative algorithm and the alternating optimization algorithm is proved. The complexity of the 3C optimization method is also analyzed. Simulation results demonstrate that the proposed designs considerably outperform the similar existing algorithm. Comparisons with the benchmark scheme show that the proposed scheme can reduce about 88% energy consumption and also improve energy efficiency performance greatly under the same simulation setups.

Automated summary

This article proposes an optimization method for unmanned-aerial-vehicles (UAVs)-enabled mobile-edge computing (MEC) networks to reduce communication and computation energy consumption. The method utilizes a virtual force field and coordinated flight control algorithm to deploy MEC servers and optimize user association and computational task offloading. Simulation results demonstrate significant improvement in energy efficiency.