期刊
IEEE COMMUNICATIONS LETTERS
卷 22, 期 5, 页码 998-1001出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2018.2815018
关键词
Anti-jamming; deep Q-network; deep reinforcement learning
资金
- Guang Xi Universities Key Laboratory Fund of Embedded Technology and Intelligent System (Guilin University of Technology)
- Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province [BK20160034]
- National Natural Science Foundation of China [61771488, 61671473, 61631020]
- Open Research Foundation of Science and Technology on Communication Networks Laboratory
This letter investigates the problem of anti-jamming communications in a dynamic and intelligent jamming environment through machine learning. Different from existing studies which need to know (estimate) the jamming patterns and parameters, we use the temporal and spectral information, i.e., the spectrum waterfall, directly. First, to cope with the challenge of infinite state of spectrum waterfall, a recursive convolutional neural network is designed. Then, an anti-jamming deep reinforcement learning algorithm is proposed to obtain the optimal anti-jamming strategies. Finally, simulation results validate the proposed approach. The proposed algorithm does not need to model the jamming patterns, and naturally has the ability to explore the unknown environment, which implies that it can be widely used for combating dynamic and intelligent jamming.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据