LAUSR

In this letter, we investigate an intelligent jamming problem in a wireless confrontation scenario where both the user and the jammer can dynamically adjust spectrum access or the jamming pattern. To realize accurate jamming attacks, we first propose a deep learning-based pattern recognition method to recognize the user’s spectrum access patterns. Then, based on the recognition results, we design targeted jamming methods including conventional jamming patterns and a deep reinforcement learning-based jamming pattern. Moreover, a practical intelligent jamming demonstration system is designed and built based on the software-defined radio (SDR) platform for verification. The simulation and platform verification results demonstrate the effectiveness of the proposed method in the dynamic spectrum confrontation scenario.