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.
@article{arxiv.2207.00159,
title = {Game-theoretic Learning Anti-jamming Approaches in Wireless Networks},
author = {Luliang Jia and Nan Qi and Feihuang Chu and Shengliang Fang and Ximing Wang and Shuli Ma and Shuo Feng},
journal= {arXiv preprint arXiv:2207.00159},
year = {2022}
}