Quantum Federated Reinforcement-Learning-Based Joint Mode Selection and Resource Allocation for STAR-RIS-Aided VRCS

The vehicle-road cooperation system (VRCS) facilitates vehicle-to-vehicle (V2V) communication for future vehicle usage in sixth generation (6G) networks. The implementation of the 6G network has made it possible for V2V communication to enhance network density, optimize transmission mode selection, and offer connectivity between vehicles while guaranteeing Quality of Service (QoS). However, there are inherent challenges, such as limited bandwidth, diverse QoS requirements, interference, and power constraints, associated with resource allocation and mode selection in V2V and vehicle-to-everything (V2X) communication. In this article, we jointly optimized the mode selection and resource allocation problems in VRCS by using simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS). The proposed model utilizes quantum federated reinforcement-learning (QFRL)-based augmented intelligence algorithms within the STAR-RIS VRCS framework. The proposed QFRL algorithm is a promising solution for advanced decision making, automation to improve traffic flow, reduces traffic congestion, and improve safety in the STAR-RIS assisted VRCS. Additionally, by leveraging the unique processing advantage of quantum computing will make the VRCS more capable of handling the enormous amount of real-time data that IoT devices send, which is necessary for the intelligent services it offers. The proposed model QFRL-based STAR-RIS assisted VRCS approach maximizes vehicle-to-infrastructure (V2I) user capacity while meeting the reliability requirement of V2V pairs. Finally, the simulation results prove the superiority of the QFRL algorithm against baseline schemes like quantum federated learning (QFL), federated reinforcement learning (FRL), and federated learning (FL) algorithms for V2V pairs. Furthermore, the performance evaluation findings indicate that the proposed STAR-RIS assisted QFRL algorithm performs 20.5%, 32.2%, and 46.7% better than QFL, FRL, and FL.