Intelligent Localization-Based DDPG for UAV-Borne RIS in Vehicular Communication

We develop a framework to simultaneously optimize the artificial noise (AN) covariance of eavesdroppers, the high-dimensional continuous phase shifts of Unmanned Aerial Vehicles (UAV)-borne reconfigurable intelligent surfaces (RISs), and beamforming of base stations (BSs) in vehicular networks. This optimization aims to maximize the worst-case secrecy rate for vehicular users (VUs) amidst eavesdroppers considering inaccurate channel state information (CSI) and VUs' quality-of-service (QoS) requirements. We propose a predictive localization-based actor-critic deep reinforcement learning (DRL) solution. We employ a deep neural network (DNN) to predict the future spatial location of VUs and eavesdroppers, since these are apriory unknown. Then, the predicted positions are fed to a Deep Deterministic Policy Gradient (DDPG) algorithm, which determines the AN matrix and RIS beamforming vector. The DDPG integrated with DNN-based predictive localization offers a significant advantage over the standard DDPG, as additional states are incorporated into the system, thereby enhancing the model's capability to coordinate DDPG based on predicted location and to capture more complex and abstract features of the highly dynamic vehicular network. Simulations demonstrate the proposed DDPG with predictive localization ensures secure and reliable communications, surpassing state-f-the-art baselines.