Interactive Artificial Intelligence Meets Game Theory in Next-Generation Communication Networks
Jingyu Shen, Chungang Yang, Tong Li, Xinwei Wang, Yanbo Song, Mohsen Guizani
Abstract
Next-generation communication networks can provide high capacity, low latency, and massive connections; however, they introduce novel challenges of management complexity, and traditional mathematical methods cannot well characterize the rational behavior of users. In this article, we pay attention to the methods of artificial intelligence (AI) and game theory. We first review the applications of machine learning (ML) and game theory models in wireless communications and summarize their advantages and disadvantages. After surveying the state of the art, in this article we propose a novel framework combining ML and game theory, which explores and exploits the benefits of the two disciplines. Finally, we apply our novel framework to solve the network selection problem in a 5G ultra-dense and heterogeneous network. Simulation results confirm the advantage of our presented framework on reducing the average delay of users.