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基于深度学习的空间脉冲位置调制多分类检测器

王惠琴 侯文斌 黄瑞 陈丹

王惠琴, 侯文斌, 黄瑞, 陈丹. 基于深度学习的空间脉冲位置调制多分类检测器[J]. , 2023, 16(2): 415-424. doi: 10.37188/CO.2022-0106
引用本文: 王惠琴, 侯文斌, 黄瑞, 陈丹. 基于深度学习的空间脉冲位置调制多分类检测器[J]. , 2023, 16(2): 415-424. doi: 10.37188/CO.2022-0106
WANG Hui-qin, HOU Wen-bin, HUANG Rui, CHEN Dan. Spatial pulse position modulation multi-classification detector based on deep learning[J]. Chinese Optics, 2023, 16(2): 415-424. doi: 10.37188/CO.2022-0106
Citation: WANG Hui-qin, HOU Wen-bin, HUANG Rui, CHEN Dan. Spatial pulse position modulation multi-classification detector based on deep learning[J]. Chinese Optics, 2023, 16(2): 415-424. doi: 10.37188/CO.2022-0106

基于深度学习的空间脉冲位置调制多分类检测器

doi: 10.37188/CO.2022-0106
基金项目: 国家自然科学基金资助项目(No. 61861026,No. 61875080);甘肃省自然科学基金资助项目(No. 20JR5RA472);陕西省科技计划产业研究项目(No. 2020GY-036);西安科技局项目(No. GXYD14.21)
详细信息
    作者简介:

    王惠琴(1971—),女,甘肃渭源人,博士,教授,博士生导师,2006年于西安理工大学获得博士学位,1996 年至今在兰州理工大学计算机与通信学院任教,主要从事无线光通信理论与技术方面的研究。E-mail:15117024169@139.com

  • 中图分类号: TN929.12

Spatial pulse position modulation multi-classification detector based on deep learning

Funds: Supported by National Natural Science Foundation of China (No. 61861026, No. 61875080); Natural Science Foundation of Gansu Province (No. 20JR5RA472); Shaanxi Provincial scientific and technological research projects (No. 2020GY-036); Xi'an Science and Technology Bureau project (No. GXYD14.21)
More Information
  • 摘要:

    为有效避免最大似然(ML)检测复杂的计算过程,根据空间脉冲位置调制(SPPM)信号的特点,将深度神经网络(DNN)与分步检测相结合,提出了一种基于深度学习的SPPM多分类检测器。在该检测器中,利用DNN建立接收信号与PPM符号间的非线性关系,并以此为准则完成在线接收PPM符号的检测,从而有效避免了对PPM符号的穷搜索检测过程。结果表明,采用本文检测器后,SPPM系统在大幅降低检测复杂度的前提下,取得了近似最优的误比特性能,同时还克服了K均值聚类(KMC)分步分类检测所出现的错误平台效应。当PPM阶数为64时,本文方法较ML检测和线性均衡DNN检测器的计算复杂度分别降低了约95.45%、33.54%。

     

  • 图 1  基于深度学习的SPPM多分类检测器

    Figure 1.  Deep learning-based SPPM multi-classification detector

    图 2  不同检测方法时(2,3,4)-SPPM系统的误比特性能

    Figure 2.  Bit error performance of a (2,3,4)-SPPM system with different detection methods

    图 3  不同检测方法时两种SPPM系统的误比特性能

    Figure 3.  Bit error performances of two SPPM systems with different detection methods

    图 4  湍流对系统误比特性能的影响

    Figure 4.  Effect of turbulence on the bit error performance of the system

    图 5  PPM阶数对系统误比特性能的影响

    Figure 5.  Influence of PPM order on the system’s bit error performance

    图 6  算法复杂度与PPM阶数L的关系

    Figure 6.  Relationship between algorithm complexity and PPM order L

    表  1  湍流模型参数

    Table  1.   Turbulence model parameters

    湍流模型G-G信道[11]EW信道(D=25 mm)[20]
    参数${\alpha _{\rm{G}}}$$ {\beta _{\rm{G}}} $$ \sigma _p^2 $$ {\alpha _{\rm{E}}} $$ {\beta _{\rm{E}}} $$ \eta $Rytov方差
    弱湍流11.610.90.23.671.970.730.317
    中等湍流4.01.91.65.370.810.332.202
    强湍流4.21.43.55.500.740.2915.851
    下载: 导出CSV

    表  2  多分类检测器的超参数

    Table  2.   Hyperparameters of the multi-classification detector

    超参数
    各隐藏层神经元数目F1=64,F2=98,F3=48
    Batch1.25×104
    Batch_size24
    轮次Epoch50
    激活函数Relu+Sigmoid
    损失函数Cross Entropy Loss
    优化器SGD
    学习率0.001
    下载: 导出CSV

    表  3  各算法计算复杂度

    Table  3.   Computational complexity of each algorithm

    检测算法计算复杂度/Flops
    ML 检测$ {N_t}L\left( {2{N_t}{N_r}L + 2{N_r}L - 1} \right) $
    KMC分步分类检测[23]$ {N_t}\left( {2{N_t}{N_r}L + {\text{2}}{N_r}L - 1} \right) + L\left( {3{N_r}L - 1} \right) $
    线性均衡DNN检测器[17] $ 2\left( {{{\left( {{N_r}L} \right)}^2} + {N_r}L{F_1} + {F_1}{F_2} + {F_2}{F_3} + {F_3}{{\log }_2}\left( {{N_t}L} \right)} \right) + {\log _2}\left( {{N_t}L} \right) $
    DNN多分类检测器$ 2\left( {{N_r}L{F_1} + {F_1}{F_2} + {F_2}{F_3} + {F_3}L + N_t^2{N_r}L + {N_t}{N_r}L} \right) + 3L - {N_t} - 1 $
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-05-27
  • 修回日期:  2022-06-15
  • 网络出版日期:  2022-10-08

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