Age-of-Information Aware Intelligent MAC for Congestion Control in NR-V2X.

The third-generation partnership project (3GPP) introduces the 5G NR-V2X to supplement the C-V2X to support advanced applications. 3GPP defines the semi-persistent scheduling for distributed resource scheduling likewise in C-V2X, however new medium access control (MAC) features are introduced in NR-V2X mode 2. Re-evaluation mechanism is added in semi-persistent scheduling to reduce resource contention. Despite with new MAC feature, NR-V2X mode 2 cannot handle the scheduling of aperiodic packets efficiently. With the increase in vehicular density, channel congestion occurs leading to packet collision. 3GPP defines the channel congestion control mechanism based on two metrics; channel busy ratio (CBR) and channel occupancy ratio (CR). These metrics, however, have considered the system-level requirements but ignore the application-level requirements such as age-of-information (AoI) associated with the message packet. In this paper, we proposed a deep reinforcement learning-based congestion control mechanism to support both system and application requirements. The performance of the proposed scheme is evaluated and compared with the conventional decentralized congestion control mechanism in a simulator designed inline with the 3GPP specifications.