Intelligent anti-jamming methods for wireless networks

In wireless networks, interference is always present and traditional anti-jamming methods are no longer able to cope with the more intelligent and dynamic interference. Therefore, there is an urgent need to improve anti-jamming technology in order to combat intelligent interference and ensure communication transmissions against the increasing number of intelligent and malicious jams. In this paper, we focus on the task of intelligent anti-jamming of wireless networks systems. Specifically, we first model the wireless networks system under interferer attacks, the signal to interference plus noise ratio and the reward function by using deep reinforcement learning, and then design a double deep Q network to simulate the confrontation between the symbiotic radio network and the interferer. Furthermore, the Q network is constructed using a Swin Transformer Block. Simulation results show that our approach outperforms Q-networks using other machine learning.