当前位置: X-MOL 学术IEEE Trans. Commun. › 论文详情
Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks
IEEE Transactions on Communications ( IF 5.690 ) Pub Date : 2019-10-17 , DOI: 10.1109/tcomm.2019.2947918
Ning Gao; Zhijin Qin; Xiaojun Jing; Qiang Ni; Shi Jin

The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack. In this paper, we propose a novel anti-intelligent UAV jamming strategy, in which the ground users can learn the optimal trajectory to elude such jamming. The problem is formulated as a stackelberg dynamic game, where the UAV jammer acts as a leader and the ground users act as followers. First, as the UAV jammer is only aware of the incomplete channel state information (CSI) of the ground users, for the first attempt, we model such leader sub-game as a partially observable Markov decision process (POMDP). Then, we obtain the optimal jamming trajectory via the developed deep recurrent Q-networks (DRQN) in the three-dimension space. Next, for the followers sub-game, we use the Markov decision process (MDP) to model it. Then we obtain the optimal communication trajectory via the developed deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium and derive the closed-form expression for the stackelberg equilibrium in a special case. Moreover, some insightful remarks are obtained and the time complexity of the proposed defense strategy is analyzed. The simulations show that the proposed defense strategy outperforms the benchmark strategies.
更新日期:2020-01-17

 

全部期刊列表>>
化学/材料学中国作者研究精选
Springer Nature 2019高下载量文章和章节
ACS材料视界
南京大学
自然科研论文编辑服务
剑桥大学-
中国科学院大学化学科学学院
南开大学化学院周其林
课题组网站
X-MOL
北京大学分子工程苏南研究院
华东师范大学分子机器及功能材料
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug