Energy Consumption Modeling and Optimization of UAV-Assisted MEC Networks Using Deep Reinforcement Learning

Abstract

Unmanned aerial vehicle (UAV)-assisted multiaccess edge computing (MEC) technology has garnered significant attention and has been successfully implemented in specific scenarios. The optimization of the network energy consumption in the relevant scenarios is essential for the whole system performance due to the constrained energy capacity of UAVs. However, the dynamic changes in MEC network resources make energy consumption optimization a challenge. In this article, a multi-UAV-multiuser MEC model is established to assess the system energy consumption, and the optimization problem of multi-UAV cooperation strategies is formulated based on the model. Then, a multiagent deep deterministic policy gradient (MADDPG) algorithm based on deep reinforcement learning (DRL) is employed to resolve the above optimization problem. Each UAV is equivalent to a single agent that cooperates with other agents to train actors and critic evaluation networks to accomplish collaborative decision-making. In addition, a prioritized experience replay (PER) scheme is used to improve the convergence of the training process. Simulation results show the impact of changes in different network resources on the network energy consumption by comparing the performance of different algorithms. The findings presented in this article serve as a valuable reference for future work on system performance optimization, specifically in terms of energy efficiency.