通信学报2024，Vol.45Issue(9)：115-128,14.DOI:10.11959/j.issn.1000-436x.2024161
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
摘要
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 in-dicate 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,com-pared to several other anti-jamming strategies,the proposed strategy demonstrates faster convergence.关键词
抗干扰/深度强化学习/样本信息熵/智能干扰Key words
anti-jamming/deep reinforcement learning/sample information entropy/intelligent jamming分类
信息技术与安全科学引用本文复制引用
李刚,吴麒,王翔,罗皓,李良鸿,景小荣,陈前斌..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(9):115-128,14.基金项目
国家自然科学基金资助项目(No.U23A20279) （No.U23A20279）
中电天奥创新理论技术群基金资助项目(No.2022-1193-04-04)The National Natural Science Foundation of China(No.U23A20279),China Electronics Tian'ao Innovation Theory and Technology Group Fund(No.2022-1193-04-04) （No.2022-1193-04-04）
