Game-Based Intelligent Jamming Strategy without Prior Information in Wireless Communications

Traditional jamming technologies have become less effective with the development of anti-jamming technologies, especially with the appearance of intelligent transmitters, which can adaptively adjust their transmission strategies. To deal with intelligent transmitters, in this paper, a game-based intelligent jamming scheme is proposed. Considering that the intelligent transmitter has multiple transmission strategy sets whose prior probabilities are unknown to the jammer, we first model the interaction between the transmitter and the jammer as a dynamic game with incomplete information. Then the perfect Bayesian equilibrium is derived based on assumptions of some prior information. For more practical applications when no prior information about the transmitter is available at the jammer, a Q-learning-based method is proposed to find an intelligent jamming strategy by exploiting the sensing results of the wireless communications. The design of the jammer's reward function is guided by the game utility and the reward is calculated based on the Acknowledgement/Negative Acknowledgement feedback of the receiver. Simulation results show that the proposed scheme has only 0.5% loss in jamming utility compared to that of the perfect Bayesian equilibrium strategy. Compared to existing jamming schemes, a higher packet error rate can be achieved by the proposed scheme by consuming less jamming power.