Intelligent Reflective Surfaces Assisted Vehicular Networks: A Computer Vision-Based Framework

Abstract

This paper addresses the challenges faced by 6G-enabled vehicular networks (V-Nets), including increasing road traffic, ultra-reliable and low latency communication, high data rates, and energy efficiency. The intelligent reflecting surface (IRS) is proposed as a solution to configure the propagation channel in a smart radio environment by adjusting phase shifts. However, designing IRS-assisted V-Nets that achieve ultra-reliability in dynamic and noisy communication is challenging due to the passive nature of the IRS and the limitations of deep reinforcement learning (DRL) methods. To overcome these challenges, this paper presents a computer vision (CV) enabled IRS framework for V-Nets, which combines a convolutional neural network and CV techniques. The framework utilizes real-time visual information to estimate and configure optimal beamforming for IRS-assisted V-Nets. Adapting to real-time network dynamics and intelligently guiding signals, the CV-IRS framework improves prediction accuracy to 95%, an achievable maximum rate of 11.2 bps/Hz with 100 IRS elements, and resource allocation efficiency of 88% with 10 vehicles. The simulation results demonstrate the superiority of the CV-IRS framework over benchmark schemes, making it a promising approach for the efficient configuration of IRS-assisted 6G V-Nets.