Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3

TL;DR

This paper introduces an ns-3 simulation framework integrating AI algorithms for optimizing vehicular wireless networks, demonstrating improved performance through reinforcement learning in a realistic simulation environment.

Contribution

It presents a modular ns-3 framework with new models and AI control for V2X networks, enabling research and evaluation of AI-based network optimization techniques.

Findings

AI-enhanced network control improves QoS

Reinforcement learning outperforms baseline methods

Framework supports realistic V2X simulations

Abstract

Artificial intelligence (AI) techniques have emerged as a powerful approach to make wireless networks more efficient and adaptable. In this paper we present an ns-3 simulation framework, able to implement AI algorithms for the optimization of wireless networks. Our pipeline consists of: (i) a new geometry-based mobility-dependent channel model for V2X; (ii) all the layers of a 5G-NR-compliant protocol stack, based on the ns3-mmwave module; (iii) a new application to simulate V2X data transmission, and (iv) a new intelligent entity for the control of the network via AI. Thanks to its flexible and modular design, researchers can use this tool to implement, train, and evaluate their own algorithms in a realistic and controlled environment. We test the behavior of our framework in a Predictive Quality of Service (PQoS) scenario, where AI functionalities are implemented using Reinforcement Learning (RL), and demonstrate that it promotes better network optimization compared to baseline solutions that do not implement AI.