Game-theoretic Learning Anti-jamming Approaches in Wireless Networks

TL;DR

This paper explores a game-theoretic learning framework for anti-jamming in wireless networks, emphasizing intelligent, self-adaptive strategies to enhance communication resilience against jammers.

Contribution

It introduces the GTLAJ paradigm, detailing its framework, challenges, and applying it to three distinct anti-jamming game models with future research directions.

Findings

Proposed a comprehensive GTLAJ framework for anti-jamming.

Analyzed three game models: Stackelberg, Markov, and hypergraph-based.

Identified key challenges and future directions in game-theoretic anti-jamming.

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.