Jamming Mitigation in JRC Systems via Deep Reinforcement Learning and Backscatter-supported Intelligent Deception Strategy

In this paper, we develop a framework to optimize the trade-off between radar sensing and data transmission in Joint Radar-Communication (JRC) systems under smart and reactive jamming attacks. First, we propose a novel JRC design architecture that uses backscatter technology and deception strategy to leverage jamming attacks for a JRC system. The deception strategy is used to predict the jammer's action and adopt appropriate counterattack instantaneously, while backscatter technology is used to transmit data on the jamming signals. To deal with the jamming strategy uncertainty (e.g., jamming capability), we then develop a deep reinforcement learning algorithm to quickly find the optimal defense policy for the JRC system. Our in-depth investigation reveals that the proposed design not only significantly undermines the jamming attacks, but also utilises jamming signals to improve the system performances. Compared with conventional anti-jamming methods, our proposed design significantly improves data throughput while maintaining a satisfactory radar sensing performance in dynamic environments. Moreover, the proposed deep reinforcement learning based solution can converge four times faster than a conventional deep Q- Learning based solution.