Shaping Connectivity: Channel Modeling and Performance Analysis of Reconfigurable Intelligent Surfaces for IoV Visible Light Communication

This paper presents a promising solution to enhance the reliability and data rate of Internet-of-Vehicles (IoV) visible light communication (VLC) systems using optical reconfigurable intelligent surfaces (RISs). The study focuses on an infrastructure-to-vehicle (I2V) scenario where streetlights act as internet access points. The IoV-VLC system is modeled using a non-sequential ray-tracing approach. A novel closed-form channel model is proposed, incorporating transceiver, RIS, and infrastructure parameters. This model is then validated against broadband ray-tracing simulations under various system configurations, including receiver size, streetlight pole height, spacing between poles, and RIS height. Based on the derived channel model, the error rate and data rate performances of the system are analyzed. Furthermore, it derives the required number of RIS elements per vehicle location to meet specific error rate and data rate targets. The impact of transceiver, RIS, and infrastructure parameters on system performance is thoroughly examined. The obtained results show that the proposed IoV-VLC system with optimal RIS deployment effectively mitigates systems outage and meet reliability and data speed requirements regardless of the vehicles location. Specifically, system performance is found to be highly sensitive to receiver aperture size, inter-pole spacing, and transmit power budget. For instance, with a fixed power budget and target error rate, the number of RIS elements needed increases with spacing distancesfrom 132 at 20 m to 244 at 22 m and to 428 at 24 m. In contrast, pole height has a minimal effect as long as pole spacing is kept constant and adheres to standard regulations.