Game-Theoretic Learning Anti-Jamming Approaches in Wireless Networks

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 an 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 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.