A Q-Learning-Based Method for UAV Communication Resilience Against Random Pulse Jamming

In open wireless communication channels, the combined effects of random pulse jamming and multipath-induced time-varying fading significantly degrade the reliability and efficiency of information transmission. Particularly in highly dynamic scenarios such as unmanned aerial vehicle (UAV) communications, existing Q-learning-based anti-jamming methods often rely on idealized channel assumptions, leading to mismatched “transmit/silence” decisions under fading conditions. To address this issue, this paper proposes a Q-learning and time-varying fading channel-aware anti-jamming method against random pulse jamming. In the proposed framework, a fading channel model is incorporated into Q-learning, where the state space jointly represents timeslot position, jamming history, and channel sensing results. Furthermore, a reward function is designed by jointly considering jamming power and channel quality, enabling dynamic strategy adaptation under rapidly varying channels. A moving average process is applied to smooth simulation fluctuations. The results demonstrate that the proposed method effectively suppresses jamming collisions, enhances the successful transmission rate, and improves communication robustness in fast-fading environments, showing strong potential for deployment in practical open-channel applications.