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Intelligent jamming decision-making system based on reinforcement learning
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AI
DOI：10.1016/j.compeleceng.2025.110288.png)
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Abstract
En 中文
Cognitive communication countermeasures have increasingly been emphasized as an important research interests in cognitive electronic warfare. However, the low signal-to-noise ratio (SNR) and frequency hopping (FH) in communication countermeasures create significant difficulties for spectrum sensing and jamming decision-making. In this paper, an intelligent jamming decision-making system for FH communication is designed based on an improved deep Q-network (DQN). First, a spectrum sensing method utilizing a bidirectional long short-term memory (Bi-LSTM) network is introduced, which establishes the received signals as a binary hypothesis testing model and employs the Bi-LSTM network for signal classification. Second, the jamming channel selection problem is modeled as a Markov decision process (MDP), and an improved DQN algorithm is applied to facilitate intelligent decision-making for jamming channels. Finally, simulation experiments are conducted to evaluate the performance of the algorithms. The results show that the proposed Bi-LSTM network achieves a detection probability of over 88% even in low-SNR communication countermeasure environments at -12 dB. Furthermore, the improved DQN algorithm achieves a 100% channel jamming rate and the fastest convergence speed among the five compared algorithms, effectively learning the FH sequences and implementing jamming.
Keywords:
Intelligent jamming
Spectrum sensing
Communication countermeasure
Deep reinforcement learning
DQN
Journal
C
IF:
4.9
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6.7K
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