TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
A hidden anti-jamming method based on deep reinforcement learning |
| 영문제목(English Title) |
A hidden anti-jamming method based on deep reinforcement learning |
| 저자(Author) |
Yifan Wang
Xin Liu
Mei Wang
Yu Yu
|
| 원문수록처(Citation) |
VOL 15 NO. 09 PP. 3444 ~ 3457 (2021. 09) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
In the field of anti-jamming based on dynamic spectrum, most methods try to improve the ability to avoid jamming and seldom consider whether the jammer would perceive the user's signal. Although these existing methods work in some anti-jamming scenarios, their long-term performance may be depressed when intelligent jammers can learn user's waveform or decision information from user's historical activities. Hence, we proposed a hidden anti-jamming method to address this problem by reducing the jammer's sense probability. In the proposed method, the action correlation between the user and the jammer is used to evaluate the hiding effect of the user's actions. And a deep reinforcement learning framework, including specific action correlation calculation and iteration learning algorithm, is designed to maximize the hiding and communication performance of the user synchronously. The simulation result shows that the algorithm proposed reduces the jammer's sense probability significantly and improves the user's anti-jamming performance slightly compared to the existing algorithms based on jamming avoidance. |
| 키워드(Keyword) |
Environmental Cognition
Anti-Intelligent Jamming
Deep Reinforcement Learning
Hidden Anti-Jamming
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| 파일첨부 |
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