TY - JOUR
T1 - A Dual-Tier Policy-Oriented Anti-Jamming Scheme Based on Deep Reinforcement Learning
AU - Chen, Xingyun
AU - Ding, Haichuan
AU - Ma, Ying
AU - Li, Xuanheng
AU - An, Jianping
AU - Fang, Yuguang
N1 - Publisher Copyright:
© 2002-2012 IEEE.
PY - 2026
Y1 - 2026
N2 - With the proliferation of software-defined radio technology, malicious jamming attacks against wireless communications have become more aggressive and flexible, which could easily create a complex and highly dynamic jamming environment by varying both the jamming parameters and the jamming policies. Such a complex jamming environment makes it challenging for most of deep reinforcement learning (DRL) based anti-jamming schemes in rapidly identifying effective strategies. In this paper, we have developed a dual-tier policy-oriented anti-jamming (DPA) scheme based on DRL to facilitate swift adaptation to the complex jamming environment. Unlike existing works, an upper-tier jamming pattern recognition (JPR) network is introduced to extract underlying jamming policy-related information which serves as a guidance for the lower-tier deep recurrent Q-network on anti-jamming decision-making. The output of the JPR network can enable the sharing of experiences among various jamming patterns originated from the same jamming policy and facilitate more efficient and targeted anti-jamming strategic learning. Extensive experimental results demonstrate that the superiority of our DPA scheme over other DRL-based benchmark schemes in terms of both anti-jamming performance and convergence speed.
AB - With the proliferation of software-defined radio technology, malicious jamming attacks against wireless communications have become more aggressive and flexible, which could easily create a complex and highly dynamic jamming environment by varying both the jamming parameters and the jamming policies. Such a complex jamming environment makes it challenging for most of deep reinforcement learning (DRL) based anti-jamming schemes in rapidly identifying effective strategies. In this paper, we have developed a dual-tier policy-oriented anti-jamming (DPA) scheme based on DRL to facilitate swift adaptation to the complex jamming environment. Unlike existing works, an upper-tier jamming pattern recognition (JPR) network is introduced to extract underlying jamming policy-related information which serves as a guidance for the lower-tier deep recurrent Q-network on anti-jamming decision-making. The output of the JPR network can enable the sharing of experiences among various jamming patterns originated from the same jamming policy and facilitate more efficient and targeted anti-jamming strategic learning. Extensive experimental results demonstrate that the superiority of our DPA scheme over other DRL-based benchmark schemes in terms of both anti-jamming performance and convergence speed.
KW - Deep reinforcement learning
KW - anti-jamming communication
KW - cognitive radio network
KW - deep recurrent Q-network
KW - dynamic spectrum access
UR - https://www.scopus.com/pages/publications/105028851168
U2 - 10.1109/TWC.2026.3653807
DO - 10.1109/TWC.2026.3653807
M3 - Article
AN - SCOPUS:105028851168
SN - 1536-1276
VL - 25
SP - 10652
EP - 10668
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
ER -