论文标题
6G无线网络中智能信任管理的生成对手学习
Generative Adversarial Learning for Intelligent Trust Management in 6G Wireless Networks
论文作者
论文摘要
新兴的六代（6G）是异质无线网络的集成，它们可以在任何地方和任何时间网络中无缝支持。但是，高质量的质量应由6G提供，以满足移动用户的期望。人工智能（AI）被认为是6G中最重要的组成部分之一。然后，基于AI的信任管理是提供可信赖且可靠的服务的有希望的范式。在本文中，为6G无线网络提出了一种生成的对抗性学习信任管理方法。首先对一些基于AI的典型信任管理方案进行了审查，然后引入了潜在的异质和智能6G架构。接下来，开发了AI和信任管理的集成以优化情报和安全性。最后，提出的基于AI的信任管理方法用于确保聚类以实现可靠和实时的通信。仿真结果表明了其在保证网络安全和服务质量方面的出色性能。
Emerging six generation (6G) is the integration of heterogeneous wireless networks, which can seamlessly support anywhere and anytime networking. But high Quality-of-Trust should be offered by 6G to meet mobile user expectations. Artificial intelligence (AI) is considered as one of the most important components in 6G. Then AI-based trust management is a promising paradigm to provide trusted and reliable services. In this article, a generative adversarial learning-enabled trust management method is presented for 6G wireless networks. Some typical AI-based trust management schemes are first reviewed, and then a potential heterogeneous and intelligent 6G architecture is introduced. Next, the integration of AI and trust management is developed to optimize the intelligence and security. Finally, the presented AI-based trust management method is applied to secure clustering to achieve reliable and real-time communications. Simulation results have demonstrated its excellent performance in guaranteeing network security and service quality.
