Environment-Adaptive Multiple Access for Distributed V2X Network: A Reinforcement Learning Framework

TL;DR

This paper introduces an environment-adaptive resource allocation method for distributed V2X networks using reinforcement learning, prioritizing vehicles with higher crash risk to improve stability and efficiency in dynamic environments.

Contribution

It presents a novel RL-based framework for autonomous, environment-aware channel access in distributed V2X networks, enhancing stability without central coordination.

Findings

Improves transmission success rate for high-risk vehicles.

Reduces air interface congestion in dynamic scenarios.

Operates effectively without central control.

Abstract

The huge research interest in cellular vehicle-to-everything (C-V2X) communications in recent days is attributed to their ability to schedule multiple access more efficiently as compared to its predecessor technology, i.e., dedicated short-range communications (DSRC). However, one of the foremost issues still remaining is the need for the V2X to operate stably in a highly dynamic environment. This paper proposes a way to exploit the dynamicity. That is, we propose a resource allocation mechanism adaptive to the environment, which can be an efficient solution for air interface congestion that a V2X network often suffers from. Specifically, the proposed mechanism aims at granting a higher chance of transmission to a vehicle with a higher crash risk. As such, the channel access is prioritized to those with urgent needs. The proposed framework is established based on reinforcement learning (RL), which is modeled as a contextual multi-armed bandit (MAB). Importantly, the framework is designed to operate at a vehicle autonomously without any assistance from a central entity, which, henceforth, is expected to make a particular fit to distributed V2X network such as C-V2X mode 4.