PR-CARA: Proactive V2X Resource Allocation with Extended 1-Stage SCI and Deep Learning-based Sensing Matrix Estimator

TL;DR

This work tackles the reliability and latency challenges of distributed V2X resource allocation under high congestion. It introduces PR-CARA, which combines an extended 1-stage SCI for proactive resource monitoring with a physics-informed, deep-learning-based proactive RSSI estimator to mitigate hidden/exposed-node interference. By transforming the resource allocation problem into a minimum proactive RSSI (quasi-optimal) decision, PR-CARA achieves lower packet collisions and higher reliability, demonstrated in CACC-based platoons via SUMO/WiLabV2Xsim across periodic and aperiodic service scenarios. The results suggest PR-CARA offers substantial practical benefits for URLLC in autonomous driving and provide a pathway toward integrating physics-based AI with reinforcement learning for further robustness.

Abstract

Distributed resource allocation algorithms differ from centralized methods by relying on locally collected information for resource selection, leading to a low vehicle-to-everything (V2X) communication quality of service (QoS) in high-traffic congestion. To overcome these challenges, this study proposes a proactive received signal strength indicator (RSSI)-based collision avoidance resource allocation (PR-CARA) algorithm. This algorithm features an extended 1-stage SCI system, which is a critical component that enables resource monitoring of adjacent vehicle user equipment (VUE). Monitored resources were then processed through a deep learning-based proactive RSSI estimator. The estimated proactive RSSI helps avoid resource selection, which leads to packet collisions, thereby significantly reducing the occurrence of this issue during resource allocation. The proposed algorithm is tested in a cooperative adaptive cruise control (CACC)-based platoon driving scenario that requires ultra-reliable and low-latency communication (URLLC) performance. Simulation results demonstrate that the proposed deep-learning-based proactive resource allocation algorithm, with the extended 1-stage SCI system, reduces packet collisions and improves the transmission signal-to-interference-plus-noise ratio (SINR), thereby significantly enhancing communication reliability compared to the benchmark resource allocation algorithm.

Figures (16)

• Figure 1: CACC-based driving service scenario
• Figure 2: Dynamics parameter of the CACC system
• Figure 3: A system model for verification of the proposed algorithm: The red road section operates an event-triggered service (aperiodic), and the blue road section operates a CACC-based driving service (periodic). The red and black solid arrows represent the PAM and CAM transmission V2V links, respectively, while the blue dotted arrows indicate the interference that the CAM V2V link causes in the PAM V2V link.
• Figure 4: Wireless network resource pool of CACC-based platoon driving system model
• Figure 5: (a) Configuration of sensing and selection windows for SB-SPS; (b) Average RSSI calculation process for each $CSR$ for SB-SPS