Volume 39, Number 3, June 2021
|Page(s)||641 - 649|
|Published online||09 August 2021|
An intelligent decision-making method for anti-jamming communication based on deep reinforcement learning
In order to solve the problem of intelligent anti-jamming decision-making in battlefield communication, this paper designs an intelligent decision-making method for communication anti-jamming based on deep reinforcement learning. Introducing experience replay and dynamic epsilon mechanism based on PHC under the framework of DQN algorithm, a dynamic epsilon-DQN intelligent decision-making method is proposed. The algorithm can better select the value of epsilon according to the state of the decision network and improve the convergence speed and decision success rate. During the decision-making process, the jamming signals of all communication frequencies are detected, and the results are input into the decision-making algorithm as jamming discriminant information, so that we can effectively avoid being jammed under the condition of no prior jamming information. The experimental results show that the proposed method adapts to various communication models, has a fast decision-making speed, and the average success rate of the convergent algorithm can reach more than 95%, which has a great advantage over the existing decision-making methods.
Key words: anti-jamming communication / intelligent decision-making / deep reinforcement learning
关键字 : 通信抗干扰 / 智能决策 / 深度强化学习
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
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