Game-theoretic Learning Anti-jamming Approaches in Wireless Networks

Luliang Jia; Nan Qi; Feihuang Chu; Shengliang Fang; Ximing Wang; Shuli Ma; Shuo Feng

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arXiv · 2207.00159

Game-theoretic Learning Anti-jamming Approaches in Wireless Networks

Luliang Jia·Nan Qi·Feihuang Chu·Shengliang Fang·Ximing Wang·Shuli Ma·Shuo Feng

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Abstract

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.

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Luliang Jia, Nan Qi, Feihuang Chu, Shengliang Fang, Ximing Wang, Shuli Ma, Shuo Feng. 2022-06-20. Game-theoretic Learning Anti-jamming Approaches in Wireless Networks. https://arxiv.org/abs/2207.00159

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