An O-MAPPO scheme for joint computation offloading and resources allocation in UAV assisted MEC systems

Abstract

The combination of mobile edge computing (MEC) systems with unmanned aerial vehicle (UAV) has gained a high profile in recent years due to its high flexibility and ability to handle intensive tasks. The computation offloading strategy and the resource allocation scheme affect the system performance significantly. Moreover, the system complexity increases with the numbers of users and servers exponentially. It is challenging to consider jointly the computation offloading and the resource allocation in MEC systems that have multiple users and multiple servers. This paper formulates the joint resources management in the UAV assisted MEC network as a partially observable markov decision process and proposes an online multi-agent proximal policy optimization (O-MAPPO) scheme to improve the energy efficiency while guaranteeing the requirements in task, power consumption, computation, and time. Specifically, users and servers are set as agents. All agents cooperatively make decisions of computation offloading and resource allocation to maximize the energy efficiency. Simulation results show that the O-MAPPO scheme significantly outperforms benchmark algorithms in robustness and stability.