Priority-Aware Multi-Agent Deep Reinforcement Learning for Resource Scheduling in C-V2X Mode 4 Communication

The need for vehicular networks with exceptional levels of reliability and negligible delay in communication, especially with the ongoing 5G and the upcoming generation of 6G systems, has given rise to Cellular-Vehicle-to-Anything C-V2X systems. This paper proposes a novel framework of Priority-Aware Multi-Agent Deep Reinforcement Learning (PA-MADRL) to address with issues such as high interference, dynamic allocation of critical signals, resource contention in density traffic, dynamic fading, and safety-critical message ratio. This approach is aimed at the development of an effective and adaptive resource allocation mechanism that will improve the likelihood of transmission of safety critical signals and improve overall network performance in metropolitan areas. PA-MADRL allows autonomous vehicles to efficiently allocate resources despite varied congestion along with interference conditions by combining centralized training for global optimization and decentralized execution for scalable decision-making. PA-MADRL is derived from the concept of optimal global allocation of resources in central training and intelligent instantaneous distribution in decentralized setting. Several tests carried out at different levels of interference and traffic densities demonstrate that PA-MADRL improves throughput by 35%, lowers latency by 40% and increases Packet Delivery Ratio (PDR) by nearly 50%, especially when the traffic density increases. These results show that PA-MADRL has the capability to adaptively work in dense vehicular networks with low communication overhead. Ultimately, the framework enhances safety in autonomous transportation by facilitating the timely delivery of critical messages, thus contributing to sustainable and intelligent transportation systems as part of next-generation smart cities.