UAV-Mounted RIS-Aided Mobile Edge Computing System: A DDQN-Based Optimization Approach

• Published in: Drones (Basel) Vol. 8; no. 5; p. 184
• Main Authors: Wu, Min, Zhu, Shibing, Li, Changqing, Zhu, Jiao, Chen, Yudi, Liu, Xiangyu, Liu, Rui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Algorithms; Beamforming; Capital costs; Data processing; double-deep Q-network; Drone aircraft; Edge computing; Energy consumption; Energy efficiency; Internet of Things; Investigations; Methods; Mobile communication systems; Mobile computing; mobile edge computing; Optimization; Reconfigurable intelligent surfaces; Resource allocation; Signal processing; Signal reflection; Signal transmission; Time Division Multiple Access; Unmanned aerial vehicles; Wireless communication systems; China
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones8050184

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are increasingly employed in mobile edge computing (MEC) systems to flexibly modify the signal transmission environment. This is achieved through the active manipulation of the wireless channel facilitated by the mobile deployment of UAVs and the intelligent reflection of signals by RISs. However, these technologies are subject to inherent limitations such as the restricted range of UAVs and limited RIS coverage, which hinder their broader application. The integration of UAVs and RISs into UAV–RIS schemes presents a promising approach to surmounting these limitations by leveraging the strengths of both technologies. Motivated by the above observations, we contemplate a novel UAV–RIS-aided MEC system, wherein UAV–RIS plays a pivotal role in facilitating communication between terrestrial vehicle users and MEC servers. To address this challenging non-convex problem, we propose an energy-constrained approach to maximize the system’s energy efficiency based on a double-deep Q-network (DDQN), which is employed to realize joint control of the UAVs, passive beamforming, and resource allocation for MEC. Numerical results demonstrate that the proposed optimization scheme significantly enhances the system efficiency of the UAV–RIS-aided time division multiple access (TDMA) network.