Robust Spectrum Access Scheme Against Diverse Jamming Policies: A Prioritized Fictitious Rival-Play-Based Approach
Hao Han, Yuhua Xu, Wen Li, Ximing Wang, Yifan Xu, Xiaokai Zhang, Yong Gao · IEEE Internet of Things Journal · 2024
With the rapid development of reinforcement learning (RL)-enhanced anti-jamming wireless communication technologies and jamming technologies, intelligent communication confrontation has become an urgent problem to be solved. Most existing work assumed that detailed information of jammer was known in advance, which hardly holds in practice. Besides, some work was sensitive to the changing of jamming policy, leading to limited adaptability and scalability. This article extends the research to scenarios with unknown jammer and diverse jamming policies, including fixed, reactive, and deep RL (DRL)-based proactive jamming policies. The interaction between communication party and jammer is formulated as a partially observable adversarial team stochastic game (POATSG). To cope with unknown and diverse jamming policies, a prioritized fictitious rival play (PFRP)-based robust anti-jamming spectrum access scheme (RASAS) is proposed. First, a fictitious jammer is designed to force the communication party to promote robustness via adversarial training. Then, a synchronized update mechanism is adopted to mitigate the nonstationary issue. Finally, the fictitious agent pool is introduced to create diverse fictitious opponents and avoid overfitting. Simulation results show that the PFRP-based scheme is robust to the jamming policy, switching cycle of the jamming policy, and jamming channel number.