Inverse Reinforcement Learning to Enhance Physical Layer Security in 6G RIS-Assisted Connected Cars

This research paper introduces a groundbreaking approach to address the escalating security concerns in the era of 6G communication networks, particularly in the context of Reconfigurable Intelligent Surfaces (RIS)-assisted connected cars. With the proliferation of connected vehicles, ensuring the confidentiality of wireless communications has become paramount. In response, this study harnesses the potential of Inverse Reinforcement Learning (IRL) to fortify the physical layer security of such networks. By integrating IRL into the RIS-assisted vehicular communication framework, a novel strategy emerges. Firstly, the paper formulates the eavesdropper’s potential actions as a learning problem, allowing the system to discern and adapt to potential threat scenarios. Leveraging this acquired knowledge, the RIS optimizes the configuration of its reflective elements dynamically, thwarting the eavesdropper’s attempts and bolstering communication security. Secondly, the extensive derivations of performance metrics - signal-to-interference noise ratio and bit error rate have been carried out to show the importance of IRL approach. Through comprehensive simulations, the efficacy of the proposed IRL-infused mechanism is validated, demonstrating significant advancements in communication privacy compared to conventional methods. This research bridges the domains of 6G networks, vehicular technology, and machine learning, presenting a promising avenue to reinforce the safeguarding of connected cars against emerging security challenges. As the 6G landscape unfolds, the fusion of innovative techniques like IRL holds immense potential to reshape security paradigms in vehicular communications, amplifying the resilience of next-generation transportation systems.