Dynamic Coordination in UAV Swarm Assisted MEC via Decentralized Deep Reinforcement Learning

Abstract

Driven by the exploding computing service demands from various intelligent mobile applications, an increasing amount of research efforts have been devoted to mobile edge computing (MEC). Meanwhile, unmanned aerial vehicles (UAVs) have found a great success in assisting existing wireless systems due to their flexibility and low cost. The advancements in these two closely related fields foster the development of the recently advocated UAV-assisted MEC paradigm, which is expected to bring unprecedented performance gain to the existing ground-based MEC systems. Nonetheless, existing works on UAV-assisted MEC mainly focus on the single-UAV scenarios and often assume static system states. In this paper, UAV swarm assisted MEC is considered where multiple collaborative UAVs are employed to help the terrestrial edge server to provide better edge computing services. However, the complexity of using existing methods to find the best dynamic coordination strategy in UAV swarm assisted MEC becomes intractable when the number of UAVs increases. To resolve this challenge, a novel decentralized deep reinforcement learning algorithm is proposed in this work, which can reduce the complexity by orders of magnitude. In addition, simulations are conducted to show that by using the proposed algorithm, the UAV swarm can efficiently learn a good dynamic coordination strategy and thus achieve a significantly better performance than the baseline scheme.