Intelligent Jamming Strategy for Wireless Communications Based on Game Theory

This paper explores the scenario that a jammer attacks an intelligent transmitter which can sense and adapt to the jamming environment. Given the non-cooperative relationship between the transmitter and the jammer, two main challenges are addressed in this paper: how to model their interactions and how to devise a jamming strategy without prior knowledge of the transmitter. A non-zero-sum game is used to model and analyze such non-cooperative interactions. An approximate mixed-strategy Nash equilibrium (NE) under complete information is derived to serve as a benchmark for comparison. According to the non-zero-sum game model, a Deep Q-Network (DQN) approach is proposed to determine jamming strategies by exploiting the detection results of the legitimate signals, such as Acknowledgements (ACKs) feedback and the modulation recognition results obtained by the jammer. Simulation results demonstrate that, without requiring complete information about the transmitter, the proposed DQN approach can achieve a similar utility as the benchmark strategy using complete information. Compared to other learning-based jamming schemes and random jamming strategy, the proposed DQN approach achieves a higher packet error rate for the communication transceiver with reduced jamming power consumption.