Cognitive RAN-Aware Transport Layer for NR-V2X Communications in 5G and Beyond Networks

The emergence of 5G millimeter wave (mmWave) networks holds tremendous potential for realizing New Radio Vehicle-to-Everything (NR-V2X) applications, which demand high data transfer rates and low latency. While 5G mmWave networks offer a commercially viable platform for the next generation of NR-V2X applications, they encounter various propagation challenges. Unfortunately, the transport layer (L4 layer) between conventional NR-V2X communication endpoint devices remains oblivious to wireless medium issues and congestion scenarios in the Radio Access Network (RAN). This often results in packet loss, transmission delays, and diminished quality of experience. This paper introduces a novel approach to predict congestion or buffer bloat scenarios in the RAN proactively. The proposed solution leverages machine learning (ML) and time series RAN Key Performance Indicators (KPIs) to forecast congestion scenarios for future time steps. Subsequently, this information is relayed to assist Active Queue Management (AQM) in the User Plane Function (UPF), integrating cognitive capabilities rooted in RAN awareness. By predicting congestion events and notifying the sender through Explicit Congestion Notification (ECN), the proposed system optimizes traffic flow, reduces latency, and forestalls impending congestion for NR-V2X applications. Moreover, the proactive prediction from our solution facilitates seamless low-latency applications like augmented reality (AR), virtual reality (VR) and 6 degrees of freedom (DOF) videos in NR-V2X. Moreover, the impending congestion prediction improves the handover experience of vehicular devices from one roadside unit (RSU) to another. Through extensive simulations, this paper demonstrates that the proposed novel ML-based cognitive RAN-aware transport layer significantly enhances the Quality of Experience (QoE), ensuring reliable, efficient, and low-latency connectivity. The proposed approach underscores the potential of a RAN-aware transport layer (L4 layer) in advancing the evolution of NR-V2X applications within the expansive realm of 5G and beyond networks.