面向抗干扰跳频通信的混合改进DQN决策算法
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1.南京邮电大学通信与信息工程学院 南京 210003; 2.南京工程学院计算机工程学院 南京 211167

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TN973.3

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国家自然科学基金(61771256)项目资助


Novel mixed DQN reinforcement learning algorithm for frequency hopping anti-jamming communications
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1.College of Communication and Information Engineering, Nanjing University of Posts and Telecommunication,Nanjing 210003, China; 2.School of Computer Engineering, Nanjing Institute of Engineering,Nanjing 211167, China

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    摘要:

    针对复杂电磁环境下的跳频抗干扰通信决策问题,提出了一种新的混合深度循环Q网络(MixDRQN)决策算法。该深度决策算法有效集成了双深度Q网络(DoubleDQN)和对决深度Q网络(DuelingDQN)两种决策机理的优点,并在信号处理前端引入长短时记忆(LSTM)层,以增强决策网络对输入频谱瀑布信号的时间相关特征提取能力。研究表明,所提出的混合决策算法通过引入DoubleDQN解决了基于ε-greedy算法导致的Q值估计偏高的问题,同时通过DuelingDQN和前端增加的LSTM层,能有效学习输入频谱瀑布信号的时间相关特征。实验结果显示,所提方法在多种干扰信号下的收敛速度及抗干扰性能均显著提升,收敛速度较已有算法提升8倍以上。

    Abstract:

    This paper investigates the problem of anti-jamming communications with intelligent frequency hopping in complex electromagnetic environment. Essentially, this paper proposes a new mixed deep recurrent Q-learning network (MixDRQN) for reinforcement learning (RL) of the optimal antijamming strategy. The proposed deep RL algorithm effectively combines double deep Q-learning network(DoubleDQN) and dueling deep Q-learning network(DuelingDQN), and further introduces long short-term memory (LSTM) layer for preprocessing the time-sensitive inputs. With the use of DoubleDQN, the proposed RL algorithm solves the problem of Q-value over-estimation caused by ε-greedy algorithm. In the mean time, the use of DuelingDQN and the LSTM layer has been proved to be very efficient for learning the time-correlated feature of inputs. Extensive experimental results show that both the convergence speed and anti-jamming performance are significantly improved, and in particular, the convergence speed of the proposed RL algorithm is more than 8 times higher than that of the existing RL algorithms.

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夏重阳,张剑书,吴晓富,靳越.面向抗干扰跳频通信的混合改进DQN决策算法[J].电子测量技术,2023,46(20):50-57

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  • 在线发布日期: 2024-01-23
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