Doppler-Aware Sum Rate Maximization for RIS-UAV Assisted Mobile IoV Networks

This paper investigates a novel RIS-assisted downlink mobile Internet of Vehicles (IoV) network to improve the service quality of long-distance communications. The proposed network leverages unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS), which can increase the signal path, thereby recovering the interrupted links and enhancing the signal strength. Additionally, the Doppler shift is considered in channel modeling to characterize the channel properties of dynamic environments. Aiming at the high-speed demand of 6 G networks, this paper introduces non-orthogonal multiple access (NOMA) technology to serve multiple vehicles and formulates a sum rates (SR) maximization problem. Then, an iterative framework is proposed for joint optimization, adopting the designed adaptive firefly algorithm (FA), semidefinite relaxation (SDR) technology, and deep deterministic policy gradient (DDPG) algorithm. Simulation results demonstrate that the proposed scheme can significantly enhance SR performance compared with space division multiple access and orthogonal multiple access schemes.