Adaptive Semi-Persistent Scheduling for Enhanced On-road Safety in Decentralized V2X Networks

TL;DR

This paper introduces SPS++, an adaptive semi-persistent scheduling method for decentralized V2X networks that dynamically adjusts resource reservation intervals to improve safety message dissemination under varying traffic conditions.

Contribution

The paper proposes SPS++, an extension of sensing-based semi-persistent scheduling that adaptively adjusts RRI based on channel conditions, enhancing safety message delivery in V2X networks.

Findings

SPS++ improves safety performance by at least 50% over traditional SPS.

Adaptive RRI reduces resource under- and over-utilization.

Enhanced BSM dissemination leads to lower collision risks.

Abstract

Decentralized vehicle-to-everything (V2X) networks (i.e., Mode-4 C-V2X and Mode 2a NR-V2X), rely on periodic Basic Safety Messages (BSMs) to disseminate time-sensitive information (e.g., vehicle position) and has the potential to improve on-road safety. For BSM scheduling, decentralized V2X networks utilize sensing-based semi-persistent scheduling (SPS), where vehicles sense radio resources and select suitable resources for BSM transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). In this paper, we show that such a BSM scheduling (with a fixed RRI) suffers from severe under- and over- utilization of radio resources under varying vehicle traffic scenarios; which severely compromises timely dissemination of BSMs, which in turn leads to increased collision risks. To address this, we extend SPS to accommodate an adaptive RRI, termed as SPS++. Specifically, SPS++ allows each vehicle -- (i) to dynamically adjust RRI based on the channel resource availability (by accounting for various vehicle traffic scenarios), and then, (ii) select suitable transmission opportunities for timely BSM transmissions at the chosen RRI. Our experiments based on Mode-4 C-V2X standard implemented using the ns-3 simulator show that SPS++ outperforms SPS by at least $50\%$ in terms of improved on-road safety performance, in all considered simulation scenarios.