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...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2024-09-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024161/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539183714238464 |
|---|---|
| author | LI Gang WU Qi WANG Xiang LUO Hao LI Lianghong JING Xiaorong CHEN Qianbin |
| author_facet | LI Gang WU Qi WANG Xiang LUO Hao LI Lianghong JING Xiaorong CHEN Qianbin |
| author_sort | LI Gang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3c5bae5d30ef4310b82b6fdf527526c8 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-3c5bae5d30ef4310b82b6fdf527526c82025-01-14T07:25:03ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-09-014511512873359109Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropyLI GangWU QiWANG XiangLUO HaoLI LianghongJING XiaorongCHEN QianbinFor 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024161/anti-jammingdeep reinforcement learningsample information entropyintelligent jamming |
| spellingShingle | LI Gang WU Qi WANG Xiang LUO Hao LI Lianghong JING Xiaorong CHEN Qianbin Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy Tongxin xuebao anti-jamming deep reinforcement learning sample information entropy intelligent jamming |
| title | Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy |
| title_full | Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy |
| title_fullStr | Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy |
| title_full_unstemmed | Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy |
| title_short | Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy |
| title_sort | deep reinforcement learning empowered anti jamming strategy aided by sample information entropy |
| topic | anti-jamming deep reinforcement learning sample information entropy intelligent jamming |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024161/ |
| work_keys_str_mv | AT ligang deepreinforcementlearningempoweredantijammingstrategyaidedbysampleinformationentropy AT wuqi deepreinforcementlearningempoweredantijammingstrategyaidedbysampleinformationentropy AT wangxiang deepreinforcementlearningempoweredantijammingstrategyaidedbysampleinformationentropy AT luohao deepreinforcementlearningempoweredantijammingstrategyaidedbysampleinformationentropy AT lilianghong deepreinforcementlearningempoweredantijammingstrategyaidedbysampleinformationentropy AT jingxiaorong deepreinforcementlearningempoweredantijammingstrategyaidedbysampleinformationentropy AT chenqianbin deepreinforcementlearningempoweredantijammingstrategyaidedbysampleinformationentropy |