Deep Deterministic Policy Gradient-Based Rate Maximization for RIS-UAV-Assisted Vehicular Communication Networks

Abstract

Reconfigurable intelligent surface (RIS) is a promising paradigm for implementing intelligent reconfigurable wireless propagation environments in the 6G era. However, most of the existing studies focus on utilizing RIS deployed on buildings to provide services to users or constructing a RIS-assisted system framework for static users, which greatly limited application in real-time changing vehicular communication environments. As a result, combining unmanned aerial vehicles (UAVs) with RIS (RIS-UAV) plays a crucial role in various wireless networks due to their high mobility. To maximize the communication rate between base station (BS) and mobile vehicle, we propose a position prediction strategy for vehicles that facilitates real-time adjustment of UAV trajectories and RIS phase shifts, enhancing communication in dynamic environments. Deep reinforcement learning (DRL) algorithm is utilized to solve the above question, which achieves a good effect on convergence in continuous action space. Simulation results demonstrate that compared with benchmark schemes, the algorithm we suggested has significant performance gains, that is to maximize the communication rate under system constraints and guarantee the reliability of the communication.