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基于CGAN的避扰通信决策网络离线式训练方法

江民民 李大朋 邱昕 慕福奇 柴旭荣 孙志浩

江民民, 李大朋, 邱昕, 等 . 基于CGAN的避扰通信决策网络离线式训练方法[J]. 北京航空航天大学学报, 2020, 46(7): 1412-1421. doi: 10.13700/j.bh.1001-5965.2019.0448
引用本文: 江民民, 李大朋, 邱昕, 等 . 基于CGAN的避扰通信决策网络离线式训练方法[J]. 北京航空航天大学学报, 2020, 46(7): 1412-1421. doi: 10.13700/j.bh.1001-5965.2019.0448
JIANG Minmin, LI Dapeng, QIU Xin, et al. An offline training method using CGAN for anti-jamming communication decision network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1412-1421. doi: 10.13700/j.bh.1001-5965.2019.0448(in Chinese)
Citation: JIANG Minmin, LI Dapeng, QIU Xin, et al. An offline training method using CGAN for anti-jamming communication decision network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1412-1421. doi: 10.13700/j.bh.1001-5965.2019.0448(in Chinese)

基于CGAN的避扰通信决策网络离线式训练方法

doi: 10.13700/j.bh.1001-5965.2019.0448
详细信息
    作者简介:

    江民民  男, 硕士研究生。主要研究方向:人工智能、认知无线电

    李大朋 男, 博士, 副研究员。主要研究方向:数字信号处理

    邱昕 男, 博士, 研究员。主要研究方向:无线通信系统设计、通信信号处理技术

    慕福奇  男, 研究员, 博士生导师。主要研究方向:无线通信系统与技术、物联网传输与应用

    柴旭荣  男, 硕士, 高级工程师。主要研究方向:无线通信系统与技术、通信信号处理技术

    孙志浩  男, 硕士研究生。主要研究方向:数字信号处理

    通讯作者:

    李大朋, E-mail: insanegtp@sina.cn

  • 中图分类号: TN974;TP181

An offline training method using CGAN for anti-jamming communication decision network

More Information
  • 摘要:

    基于强化学习的避扰通信,由于需要不断地与环境交互从中学习到最优决策,其决策网络的训练时间受环境反馈速率的约束,通常耗时严重。针对这一问题,提出了一种离线式训练方法。构建出一种频谱虚拟环境生成器,可以快速生成大量的逼真合成频谱瀑布图,用于避扰通信决策网络训练。由于所提方法脱离真实环境反馈,形成离线式训练,进而显著提高模型训练效率。实验结果表明:与实时在线训练方法比较,所提离线式训练方法的训练时间可以减少50%以上。

     

  • 图 1  ADRLA训练过程图[3]

    Figure 1.  Training process of ADRLA[3]

    图 2  离线式快速避扰通信模型训练框架

    Figure 2.  An offline fast model training framework for anti-jamming communication

    图 3  产生频谱虚拟环境生成器的细节

    Figure 3.  Details of making spectrum virtual environment generator

    图 4  真实的SW图和相应的标注图

    Figure 4.  Real SW image and corresponding labeled image

    图 5  pix2pix中生成器和判别器功能作用

    Figure 5.  Functions of generator and discriminator in pix2pix

    图 6  增强后的pix2pix生成的合成SW图和原始pix2pix生成的合成SW图对比

    Figure 6.  Comparison between synthesis SW image generated by enhanced pix2pix and synthesis SW image generated by original pix2pix

    图 7  合成SW图和真实SW图

    Figure 7.  Synthesis SW image and real SW image

    图 8  条件图和合成SW图

    Figure 8.  Condition image and synthesis SW image

    图 9  扫频干扰模式的条件图和合成SW图

    Figure 9.  Condition image of sweeping jamming and corresponding synthesis SW image

    图 10  t时刻总体得分变化

    Figure 10.  Total reward variation of time t

    图 11  实验环境

    Figure 11.  Experimental environment

    图 12  在真实环境下的验证

    Figure 12.  Validation in real environment

    图 13  真实环境下本文方法和ADRLA的耗时对比

    Figure 13.  Comparison of time consumption between proposed method and ADRLA for real environment

    表  1  本文方法和ADRLA耗时对比

    Table  1.   Comparison of time consumption between proposed method and ADRLA

    计算机性能 参数 TOFFLINE_ADRLA/min TADRLA/min
    低性能(CPU) Toffline_init =0.5s,
    TCGAN=0.4s,
    Tonline_init=40s,
    Tsample=0.8s,
    TDQN=0.1s,
    k=1000
    8.3 15.7
    中等性能(1块GPU) Toffline_init=0.3s,
    TCGAN=0.2s,
    Tonline_init=40s,
    Tsample=0.8s,
    TDQN=0.05s,
    k=1000
    4.2 14.8
    高性能(4块GPU) Toffline_init=0.1s,
    TCGAN=0.1s,
    Tonline_init=40s,
    Tsample=0.8s,
    TDQN=0.01s,
    k=1000
    1.8 14.2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-08-16
  • 录用日期:  2020-01-18
  • 刊出日期:  2020-07-20

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