基于样本信息熵辅助的深度强化学习抗干扰策略

李刚; 吴麒; 王翔; 罗皓; 李良鸿; 景小荣; 陈前斌

doi:10.11959/j.issn.1000-436x.2024161

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基于样本信息熵辅助的深度强化学习抗干扰策略

学术论文 | 更新时间：2024-10-10

- 基于样本信息熵辅助的深度强化学习抗干扰策略
- Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy
- 通信学报 2024年45卷第9期页码：115-128
- 作者机构：
  
  1.中国西南电子技术研究所，四川成都 610036
  2.重庆邮电大学通信与信息工程学院，重庆 400065
- 作者简介：
  
  [ "李刚（1989- ），男，重庆人，中国西南电子技术研究所工程师，主要研究方向为分布式通信系统设计及人工智能等。" ]
  [ "吴麒（1985- ），男，四川眉山人，博士，中国西南电子技术研究所高级工程师，主要研究方向为通信与系统总体设计、智能通信技术等。" ]
  [ "王翔（1988- ），男，湖南常德人，博士，中国西南电子技术研究所工程师，主要研究方向为智能通信与网络技术等。" ]
  [ "罗皓（1997- ），男，四川永川人，中国西南电子技术研究所工程师，主要研究方向为干扰对抗、信号处理等。" ]
  [ "李良鸿（1992- ），男，四川巴中人，重庆邮电大学博士生，主要研究方向为通信抗干扰。" ]
  [ "景小荣（1974- ），男，甘肃平凉人，博士，重庆邮电大学教授、博士生导师，主要研究方向为无线通信及通信对抗等。" ]
  [ "陈前斌（1967- ），男，四川营山人，博士，重庆邮电大学教授、博士生导师，主要研究方向为无线通信、多媒体信息传输与处理。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(U23A20279);中电天奥创新理论技术群基金资助项目(2022-1193-04-04)
- DOI：10.11959/j.issn.1000-436x.2024161
  中图分类号： TN975
- 收稿日期：2024-02-05，
  
  修回日期：2024-08-06，
  
  纸质出版日期：2024-09-25
- 稿件说明：
移动端阅览
李刚,吴麒,王翔等.基于样本信息熵辅助的深度强化学习抗干扰策略[J].通信学报,2024,45(09):115-128.

LI Gang,WU Qi,WANG Xiang,et al.Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy[J].Journal on Communications,2024,45(09):115-128.
李刚,吴麒,王翔等.基于样本信息熵辅助的深度强化学习抗干扰策略[J].通信学报,2024,45(09):115-128. DOI： 10.11959/j.issn.1000-436x.2024161.

LI Gang,WU Qi,WANG Xiang,et al.Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy[J].Journal on Communications,2024,45(09):115-128. DOI： 10.11959/j.issn.1000-436x.2024161.

摘要

针对深度强化学习驱动的智能化干扰，提出了一种基于样本信息熵辅助的通信抗干扰策略。首先，基于神经网络对抗干扰策略网络和熵预测网络进行设计；接着，利用短时傅里叶变换对接收信号处理所形成的频谱瀑布图作为样本，对抗干扰策略网络和信息熵预测网络进行训练；之后，利用信息熵预测网络对抗干扰策略网络的训练样本进行精细化筛选，以提高训练样本的质量，最终提高抗干扰策略的在线决策能力和泛化性能。仿真结果表明，在干扰方干扰策略更新频率不超过通信方40倍且最大干扰通道数为3的极端条件下，基于样本信息熵辅助的通信抗干扰策略仍可取得至少61%的成功率；同时，与其他几种对比抗干扰策略相比，所提通信抗干扰策略具有更快的收敛速度。

Abstract

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.

关键词

Keywords

references

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