Jia, Luliang;
Qi, Nan;
Su, Zhe;
Chu, Feihuang;
Fang, Shengliang;
Wong, Kai-Kit;
Chae, Chan-Byoung;
(2024)
Game Theory and Reinforcement Learning for Anti-jamming Defense in Wireless Communications: Current Research, Challenges, and Solutions.
IEEE Communications Surveys & Tutorials
10.1109/COMST.2024.3482973.
(In press).
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Abstract
Due to the inherently open and shared nature of the wireless channels, wireless communication networks are vulnerable to jamming attacks, and effective anti-jamming measures are of utmost importance to realize reliable communications. Game theory and reinforcement learning (RL) are powerful mathematical tools in anti-jamming field. This article investigates the anti-jamming problem from the perspective of game theory and RL. First, different anti-jamming domains and anti-jamming strategies are discussed, and technological challenges are globally analyzed from different perspectives. Second, an in-depth systematic and comprehensive survey of each kind of anti-jamming solutions (i.e., game theory and RL) is presented. To be specific, some game models are discussed for game theory based solutions, including Bayesian anti-jamming game, Stackelberg anti-jamming game, stochastic anti-jamming game, zero-sum anti-jamming game, graphical/hypergraphical anti-jamming game, etc. For RL-based anti-jamming solutions, different kinds of RL are given, including Q-learning, multi-armed bandit, deep RL and transfer RL. Third, the strengths and limitations are analyzed for each type of anti-jamming solutions. Finally, we discuss the deep integration of the game theory and RL in solving anti-jamming problems, and a few future research directions are illustrated.
| Type: | Article |
|---|---|
| Title: | Game Theory and Reinforcement Learning for Anti-jamming Defense in Wireless Communications: Current Research, Challenges, and Solutions |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/COMST.2024.3482973 |
| Publisher version: | https://doi.org/10.1109/COMST.2024.3482973 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Wireless security; anti-jamming communication; game theory; reinforcement learning; incomplete information; jamming attacks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10200345 |
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