AoI Optimization in 5G NR-V2X Sidelink through Propagation-Aware Dynamic Congestion Control

Maintaining low Age of Information (AoI) is essential for connected and autonomous vehicles, yet the SemiPersistent Scheduling (SPS) procedure standardized for 5G NRV2X sidelink employs a fixed sensing window that cannot adapt to changes in traffic load or vehicle density. This limitation increases resource-selection conflicts and degrades the timeliness required for cooperative perception and cooperative driving. This paper introduces a lightweight, fully distributed congestioncontrol mechanism that incorporates real-time congestion information into the SPS sensing process. By deriving statistics from neighboring transmission periodicity, the method dynamically adjusts the sensing-window span to reduce collisions and prevent excessive channel occupation. The approach is evaluated in a system-level framework combining microscopic mobility with a detailed wireless channel and SPS access model. Across diverse densities, mobility conditions, and communication ranges, the adaptive sensing-window mechanism consistently improves both mean and tail AoI and enhances the stability of the Resource Reservation Interval. It achieves substantial reductions in information staleness, including significant gains for shortrange, safety-critical exchanges. Because it solely refines the sensing-window computation without modifying SPS signaling, the method offers a practical and readily deployable enhancement to NR-V2X Mode 2 congestion control