A Deep Reinforcement Learning Based Approach for Optimizing Trajectory and Frequency in Energy Constrained Multi-UAV Assisted MEC System

Abstract

Mobile Edge Computing (MEC) is a technology that shows great promise in enhancing the computational power of smart devices (SDs) in the Internet of Things (IoT). However, the fixed location and limited coverage of MEC servers constrain their performance. To overcome this issue, this paper explores a multiple unmanned aerial vehicle (UAV) assisted MEC system. The proposed system considers a scenario where multiple UAVs work together to provide computing services while dynamically adjusting their frequency based on the task size, under the constraint of limited energy. This paper aims to maximize computation bits, SDs’ fairness, and UAVs’ load balancing in multi-UAV assisted MEC system by jointly optimizing the trajectory and frequency. To address this challenge, we model it as a Partially Observable Markov Decision Process and propose a joint optimization strategy based on multi-agent deep reinforcement learning. The effectiveness of the proposed strategy is evaluated on both synthetic and realistic datasets. The results demonstrate that our strategy outperforms other benchmark strategies.