Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy
For the deep reinforcement learning (DRL)-empowered intelligent jamming, an anti-jamming strategy aided by sample information entropy was proposed. Firstly, the anti-jamming strategy network and entropy prediction network were designed based on neural networks. Then, the anti-jamming strategy networ...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2024-09-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024161/ |
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| Summary: | For the deep reinforcement learning (DRL)-empowered intelligent jamming, an anti-jamming strategy aided by sample information entropy was proposed. Firstly, the anti-jamming strategy network and entropy prediction network were designed based on neural networks. Then, the anti-jamming strategy network and entropy prediction network were trained with the samples of the spectrum waterfall, which were formed by performing the short-time Fourier transform to the received signals. The information entropy prediction network was utilized for fine-grained selection of training samples of the anti-jamming strategy network to improve the quality of training samples, thereby enhancing the ultimate online decision-making capability and generalization performance of the anti-jamming strategy. The simulation results indicate that under the extreme condition where the jamming strategy update frequency does not exceed forty times that of the communication anti-jamming strategy and the maximum number of jamming channels is 3, the proposed anti-jamming strategy, aided by sample information entropy, can still achieve a success rate of at least 61%. Moreover, compared to several other anti-jamming strategies, the proposed strategy demonstrates faster convergence. |
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| ISSN: | 1000-436X |