论文标题
车辆无线网络中的人工智能：使用NS-3的案例研究
Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3
论文作者
论文摘要
人工智能（AI）技术已成为一种强大的方法，使无线网络更有效和适应性。在本文中，我们提出了一个NS-3仿真框架，能够实现AI算法以优化无线网络。我们的管道包括：（i）基于几何学的新型移动性通道模型； （ii）基于NS3-MWAVE模块的5G-NR兼容协议堆栈的所有层； （iii）一个新的应用程序来模拟V2X数据传输，（iv）通过AI控制网络的新智能实体。由于其灵活而模块化的设计，研究人员可以使用此工具在现实和受控的环境中实现，训练和评估自己的算法。我们在预测性服务质量（PQOS）方案中测试了框架的行为，其中使用强化学习（RL）实施了AI功能，并证明与不实施AI的基线解决方案相比，它可以促进更好的网络优化。
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.
