Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3
This work provides a tool for researchers to test AI algorithms in realistic vehicular wireless network simulations, but it is incremental as it builds on existing ns-3 modules and applies standard AI techniques.
The authors developed an ns-3 simulation framework to implement AI algorithms for optimizing wireless networks, specifically in a V2X context, and demonstrated that it improves network optimization compared to non-AI baselines in a Predictive Quality of Service scenario.
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.