论文标题
无线网络中的游戏理论学习抗界方法
Game-theoretic Learning Anti-jamming Approaches in Wireless Networks
论文作者
论文摘要
在本文中，从游戏理论学习的角度研究了反杀伤沟通问题。通过探索和分析智能的反犯罪交流，我们介绍了干扰者的特征以及智能反判断方法的要求。这种方法是自感应，自我决定，自我协调，自我评估和学习能力所必需的。然后，提出了一种游戏理论学习反判断（GTLAJ）范式，并引入了其GTLAJ的框架和挑战。此外，通过三种情况，即Stackelberg Anti-Jamming Game，Markov Anti-Jamming Game和基于HyperGraph的反犯罪游戏，讨论了不同的反杀伤游戏模型和应用程序，并提供了一些未来的方向。
In this article, the anti-jamming communication problem is investigated from a game-theoretic learning perspective. By exploring and analyzing intelligent anti-jamming communication, we present the characteristics of jammers and the requirements of an intelligent anti-jamming approach. Such approach is required of self-sensing, self-decision making, self-coordination, self-evaluation, and learning ability. Then, a game-theoretic learning anti-jamming (GTLAJ) paradigm is proposed, and its framework and challenges of GTLAJ are introduced. Moreover, through three cases, i.e., Stackelberg anti-jamming game, Markov anti-jamming game and hypergraph-based anti-jamming game, different anti-jamming game models and applications are discussed, and some future directions are presented.
