Generative Adversarial Learning for Intelligent Trust Management in 6G Wireless Networks
Liu Yang, Yun Li, Simon X. Yang, Yinzhi Lu, Tan Guo, Keping Yu
Abstract
The emerging sixth 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. AI-based trust management is a promising paradigm to provide trusted and reliable services. In this article, a generative-adversarial-learning-en-abled 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 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.