Joint Dual-UAV Trajectory and RIS Design for ARIS-Assisted Aerial Computing in IoT

Abstract

Reconfigurable intelligent surface (RIS), as an emerging technology, has recently been applied to expand the range of mobile-edge computing (MEC) networks and improve wireless environments. However, current terrestrial RIS-assisted MEC networks have some limitations, such as severe signal attenuation and inflexible equipment deployment. To take full advantage of the superiority of the RIS, this article considers an aerial RIS (ARIS)-assisted aerial computing scheme, where the ARIS and the other unmanned aerial vehicle (UAV) equipped with a MEC server are employed to facilitate offloading computing tasks from Internet of Things (IoT) user equipments (UEs) to the access point (AP). With the flexibility of the dual-UAV, we can mitigate the Non-Line-of-Sight (NLoS) air–ground paths caused by obstacles. In the proposed scenario, to improve the system energy efficiency while ensuring the UEs receive high-quality wireless services, we intend to jointly optimize the trajectories of the two UAVs, the phase shift of the ARIS, the computation offloading strategy, and computation resource allocation. The issue is formulated as a mixed nonconvex optimization problem, so it is difficult to solve it in time for adapting different environments by using conventional convex optimization methods. However, we develop a double deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DDQN)-based algorithm to obtain the near-optimal online decision-making solution. Simulation findings indicate that the proposed DDQN-based algorithm can effectively increase the energy efficiency of the proposed dual-UAV cooperative MEC system in comparison to the benchmark schemes.