Cost optimization of UAV-MEC network calculation offloading: A multi-agent reinforcement learning method

Abstract

In the Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing (MEC) system, most existing studies do not effectively consider the task offloading of user equipment (UEs) and the economic issues related to MEC service providers. Firstly, this paper considers the UE offloading cost and the pricing of MEC server, and establishes the UE, UAV cost and UAV revenue model. For the established non-convex optimization problem, it is further described as a Markov decision process (MDP), and a multi-agent reinforcement deep learning algorithm (MADRL) is proposed to minimize the system energy consumption by jointly optimizing power control, resource allocation and UE association, so as to effectively improve the overall revenue of UAV under the premise of ensuring the system performance. The simulation results show that our scheme can effectively improve the UAV revenue, and is significantly better than the random scheme without energy consumption optimization. In addition, the revenue obtained by our design scheme is 10.7% higher than that of the comparison scheme.