Titolo della tesi: Age of Information and Persistence Control in 5G NR-V2X Sidelink Communications
As vehicular networks evolve, ensuring ultra-reliable and low-latency communication has become a key priority. 5G NR-V2X sidelink communication addresses this need by enabling direct data exchange between vehicles without cellular infrastructure. However, efficiently allocating resources while maintaining fresh and timely information remains a major challenge, particularly in safety-critical applications.
This research develops an analytical framework to characterize the impact of persistence on Semi-Persistent Scheduling performance in 5G NR-V2X sidelink communication. The models provides a theoretical foundation for understanding the trade-offs between reliability and timeliness in resource allocation. By capturing key system dynamics, the analysis reveals that while persistence enhances resource stability, it can also lead to prolonged collision periods, negatively impacting information freshness. The models are validated through extensive simulations, offering insights for optimizing persistence levels in vehicular networks.
This research also introduces a novel persistence framework that streamlines the traditional Semi-Persistent Scheduling mechanism. By replacing its dual-parameter configuration with a single, more flexible persistence parameter, the framework enhances adaptability and simplifies implementation, broadening its applicability in vehicular networks.
Contrary to prevailing assumptions, results reveal that dynamic scheduling, despite its simplicity, can achieve superior performance in periodic update messaging compared to the more structured semi-persistent scheme. This challenges the necessity of persistent scheduling, particularly in scenarios where minimizing the Age of Information is critical.
Another key contribution is the development of an adaptive congestion control algorithm that dynamically adjusts persistence levels to optimize network reliability and information timeliness. By mitigating excessive resource collisions and transmission delays, this algorithm ensures robust communication even in dense vehicular environments.
The findings of this study provide critical insights for optimizing next-generation vehicular networks, particularly in improving communication efficiency and information freshness. By refining resource allocation mechanisms and introducing an adaptive persistence control strategy, this research contributes to ongoing standardization efforts in 5G NR-V2X. These advancements lay the groundwork for more reliable, scalable, and intelligent vehicular com- munication systems, supporting real-time situational awareness and enhancing the safety of autonomous driving applications.