Energy-efficient task offloading and trajectory planning in UAV-enabled mobile edge computing networks

In order to meet the double-sided challenges brought by the shortage of computation resources and energy of users, we investigate in this paper the optimization of energy efficiency (EE) in an unmanned aerial vehicle (UAV)-assisted wireless network, where UAV is functioned as a flying energy station and edge server to provide charging and computing services for ground users. We aim to maximize the average EE of the mobile edge computing network by the joint design of user transmit power, user computing frequency, UAV transmit power, bandwidth allocation, and UAV trajectory planning under strict energy and power constraints. In order to solve such challenging problem, we first elaborately construct a Markov decision process to model task offloading and resource allocation by learning from past experiences. Then, an average EE maximization method relying on deep reinforcement learning (DRL) is designed to efficiently adjust task offloading policy, where the policy of agent can be gradually improved by interacting with the environment and collecting the experience for learning. Finally, the EE-maximization proximal policy optimization (EE-PPO) algorithm is proposed to train the DRL agent and thereby solve this optimization problem. Numerical results are given to indicate that the proposed EE-PPO method has the properties of both fast convergence and well performance.