Enhancing Sidelink 5G V2V Communication: A Distributed Probabilistic Congestion Control for Dynamic Resource Allocation

Annu, Rajalakshmi Pachamuthu

Abstract: In Vehicle-to Vehicle (V2V) networks, where there is a pressing need for high-speed and reliable communication, the optimal allocation of resources assumes pivotal significance. In response, we introduce an innovative approach known as the Distributed Probabilistic Congestion Control (DPCC) for the dynamic allocation of resources in sidelink 5G V2V communication. Harnessing the power of a Markov chain model to capture the dynamics of V2V communication and accounting for the Poisson arrivals of V2X messages, our methodology dynamically assigns resources based on prevailing congestion levels. Through comprehensive simulations, we rigorously evaluate the performance of DPCC in comparison to the conventional Sensing-based Semi-persistent Scheduling (SB-SPS) resource allocation technique, analyzing key metrics such as throughput, packet delivery ratio, average delay, resource utilization, and fairness among vehicles. The results indicate that our algorithm surpasses the traditional approach across various metrics, including Packet Delivery Rate (PDR), Average Delay, Fairness, and Resource Utilization.