| Autor: |
LI Gang, WU Qi, WANG Xiang, LUO Hao, LI Lianghong, JING Xiaorong, CHEN Qianbin |
| Jazyk: |
čínština |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Tongxin xuebao, Vol 45, Pp 115-128 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1000-436X |
| DOI: |
10.11959/j.issn.1000-436x.2024161 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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