Design and implementation of reinforcement learning-based intelligent jamming system

Here the intelligent jammer issue is studied. With the rapid development of cognitive radio technology, current cognitive terminals can adaptively or intelligently switch channel by spectrum sensing and decision-making. Most of the traditional jamming methods, such as swept jamming and comb jamming, generally work in a relatively fixed pattern, which are not able to effectively jam the terminals empowered with cognition and spectrum decision-making capability. In view of this problem, the authors propose an intelligent jamming decision-making system based on reinforcement learning. First, in order to jam a pair of transmitter and receiver with adaptive frequency hopping capability, a jammer with spectrum sensing, offline training and learning scheme is proposed. Second, a reinforcement learning-based algorithm for jamming decision-making is proposed and simulated. A special feature of the proposed scheme is that considering the reward is difficult to obtain in the actual communication system, a virtual jamming decision-making method is used to enable the jammer to learn and jam efficiently without the user's prior information. Finally, the proposed jamming model and algorithm are implemented and verified on Universal software radio peripheral testbed.