Integrating Reconfigurable Intelligent Surfaces into Next-Generation Mobile Networks: Comparative Simulations based on Simu5G

Abstract

Due to the need for high-performance communication systems capable of supporting a wide range of applications -including industrial automation, smart healthcare, and autonomous driving-, Next-Generation Mobile Networks (NGMNs) are continuing to evolve. Furthermore, to cope with variable traffic situations in urban vehicular environments, autonomous cars require communication, high reliability, doubtless integrity and low latency. Besides, in smart city environments, an AI-powered attack can i) exploit vulnerabilities in connected autonomous vehicles by generating spoofed signals to misdirect navigation and orchestrating jamming attacks to disrupt Vehicle-to-Everything (V2X) communications; In the same way, ii) telemedicine applications and wearable medical devices in the healthcare industry require reliable and secure communication in dynamic, interference-prone indoor and outdoor environments. However, in order to facilitate synchronized Machine-to-Machine (M2M) operations under strict latency and reliability limitations, industrial automation relies on resilient and robust wireless communication. In this context, Reconfigurable Intelligent Surfaces (RISs) have emerged as one of the potential Sixth Generation (6G)-enabling technologies capable of addressing these challenges. By dynamically reconfiguring the wireless propagation environment through programmable surfaces, RISs can improve the system performance in terms of signal reliability, coverage, and energy efficiency. To examine this, this work focuses on comparative simulations evaluating the network-layer performance of RIS-enhanced and non-RIS networks using the network simulation environment Simu5G. Thereby, key RIS features, such as channel optimization and interference suppression, are modelled to assess their impact on critical metrics like Signal-to-Interference-plus-Noise-Ratio (SINR), resilience, and secrecy efficiency against adversarial threats. Furthermore, this work highlights how RISs can mitigate security risks such as eavesdropping, spoofing, and jamming, which are becoming increasingly prevalent in AI-driven attack scenarios. For instance, RISs effectively counters AI-generated spoofed signals in autonomous vehicle networks and suppresses jamming in V2X communication. Comparative results demonstrate the superiority of RIS-enabled network architectures in both performance and security. In addition, the work provides academic and industrial researchers with a robust toolkit for examining the dual function of RIS in improving wireless network performance and security by expanding the Simu5G platform with RIS-capable modules. This contribution is an important step towards enabling real-world deployment of RIS in future networks.