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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]. 中国光学(中英文), 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]. 中国光学(中英文), 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
  • [1] LI Y Y, YANG P, DI RENZO M, et al. Precoded optical spatial modulation for indoor visible light communications[J]. IEEE Transactions on Communications, 2021, 69(4): 2518-2531. doi: 10.1109/TCOMM.2020.3041766
    [2] REN Y X, WANG ZH, XIE G D, et al. Atmospheric turbulence mitigation in an OAM-based MIMO free-space optical link using spatial diversity combined with MIMO equalization[J]. Optics Letters, 2016, 41(11): 2406-2409. doi: 10.1364/OL.41.002406
    [3] HAJJARIAN Z, FADLULLAH J, KAVEHRAD M. MIMO free space optical communications in turbid and turbulent atmosphere[J]. Journal of Communications, 2009, 4(8): 524-532.
    [4] ZHONG X, CHEN CH, FU SH, et al. . OFDM-based generalized spatial modulation for optical wireless communication[C]. Proceedings of the IEEE 16th Conference on Industrial Electronics and Applications, IEEE, 2021: 1311-1316.
    [5] ANANDKUMAR D, SANGEETHA R G. A survey on performance enhancement in free space optical communication system through channel models and modulation techniques[J]. Optical and Quantum Electronics, 2021, 53(1): 1-39. doi: 10.1007/s11082-020-02629-6
    [6] YU S Y, GENG CH, ZHONG J, et al. Performance analysis of optical spatial modulation over a correlated Gamma-Gamma turbulence channel[J]. Applied Optics, 2022, 61(8): 2025-2035. doi: 10.1364/AO.447644
    [7] 张悦, 王惠琴, 曹明华, 等. 无线光通信中的增强型光空间调制[J]. 光学学报,2020,40(3):0306001. doi: 10.3788/AOS202040.0306001

    ZHANG Y, WANG H Q, CAO M H, et al. Enhanced optical spatial modulation in wireless optical communication[J]. Acta Optica Sinica, 2020, 40(3): 0306001. (in Chinese) doi: 10.3788/AOS202040.0306001
    [8] INOUE K. Analysis of BER degradation owing to multiple crosstalk channels in optical QPSK/QAM signals[J]. IEICE Transactions on Communications, 2021, E104.B(4): 370-377. doi: 10.1587/transcom.2020EBP3098
    [9] BHOWAL A, KSHETRIMAYUM R S. Advanced optical spatial modulation techniques for FSO communication[J]. IEEE Transactions on Communications, 2021, 69(2): 1163-1174.
    [10] 徐宪莹, 岳殿武. 可见光通信中正交频分复用调制技术[J]. 中国光学,2021,14(3):516-527. doi: 10.37188/CO.2020-0051

    XU X Y, YUE D W. Orthogonal frequency division multiplexing modulation techniques in visible light communication[J]. Chinese Optics, 2021, 14(3): 516-527. (in Chinese) doi: 10.37188/CO.2020-0051
    [11] KUMAR D A, SANGEETHA R G. Power series based gamma-gamma fading MIMO/FSO link analysis with atmospheric turbulence and pointing errors[J]. Optical and Quantum Electronics, 2021, 53(9): 505. doi: 10.1007/s11082-021-03103-7
    [12] 王惠琴, 宋梨花, 曹明华, 等. 湍流信道下光空间调制信号的压缩感知检测[J]. 光学 精密工程,2018,26(11):2669-2674. doi: 10.3788/OPE.20182611.2669

    WANG H Q, SONG L H, CAO M H, et al. Compressed sensing detection of optical spatial modulation signal in turbulent channel[J]. Optics and Precision Engineering, 2018, 26(11): 2669-2674. (in Chinese) doi: 10.3788/OPE.20182611.2669
    [13] XIE Y H, TEH K C, KOT A C. Deep learning-based joint detection for OFDM-NOMA scheme[J]. IEEE Communications Letters, 2021, 25(8): 1-27. doi: 10.1109/LCOMM.2021.3077878
    [14] BAEK M S, KWAK S W, JUNG J Y. Implementation methodologies of deep learning-based signal detection for conventional MIMO transmitters[J]. IEEE Transactions on Broadcasting, 2019, 65(3): 636-642. doi: 10.1109/TBC.2019.2891051
    [15] SHAMASUNDAR B, CHOCKALINGAM A. A DNN architecture for the detection of generalized spatial modulation signals[J]. IEEE Communications Letters, 2020, 24(12): 2770-2774. doi: 10.1109/LCOMM.2020.3018260
    [16] AMIRABADI M A, KAHAEI M H, NEZAMALHOSSEINI S A. Deep learning based detection technique for FSO communication systems[J]. Physical Communication, 2020, 43: 101229. doi: 10.1016/j.phycom.2020.101229
    [17] WANG T J, YANG F, SONG J. Deep learning-based detection scheme for visible light communication with generalized spatial modulation[J]. Optics Express, 2020, 28(20): 28906-28915. doi: 10.1364/OE.404463
    [18] LUONG T V, KO Y, VIEN N A, et al. Deep learning-based detector for OFDM-IM[J]. IEEE Wireless Communications Letters, 2019, 8(4): 1159-1162. doi: 10.1109/LWC.2019.2909893
    [19] 周畅, 于笑楠, 姜会林, 等. 基于APD自适应增益控制的近地无线激光通信信道大气湍流抑制方法研究[J]. 中国激光,2022,49(4):0406002. doi: 10.3788/CJL202249.0406002

    ZHOU CH, YU X N, JIANG H L, et al. Atmospheric turbulence suppression methods for near the earth wireless laser communication channels based on avalanche photodiode adaptive gain control[J]. Chinese Journal of Lasers, 2022, 49(4): 0406002. (in Chinese) doi: 10.3788/CJL202249.0406002
    [20] BARRIOS R, DIOS F. Exponentiated Weibull model for the irradiance probability density function of a laser beam propagating through atmospheric turbulence[J]. Optics &Laser Technology, 2013, 45(1): 13-20.
    [21] PHAM H T T, DANG N T. Performance improvement of spatial modulation-assisted FSO systems over Gamma-Gamma fading channels with geometric spreading[J]. Photonic Network Communications, 2017, 34(2): 213-220.
    [22] 劳陈哲, 孙建锋, 周煜, 等. 多孔径接收相干合束系统性能研究[J]. 中国激光,2019,46(7):0705003. doi: 10.3788/CJL201946.0705003

    LAO CH ZH, SUN J F, ZHOU Y, et al. Performance of coherent beam combining system with multiple aperture receiver[J]. Chinese Journal of Lasers, 2019, 46(7): 0705003. (in Chinese) doi: 10.3788/CJL201946.0705003
    [23] 王惠琴, 侯文斌, 彭清斌, 等. 基于K均值聚类的SPPM分步分类检测算法[J]. 通信学报,2022,43(1):161-171. doi: 10.11959/j.issn.1000-436x.2022010

    WANG H Q, HOU W B, PENG Q B, et al. Step-by-step classification detection algorithm of SPPM based on K-means clustering[J]. Journal on Communications, 2022, 43(1): 161-171. (in Chinese) doi: 10.11959/j.issn.1000-436x.2022010
    [24] 霍婷婷, 张冬冬, 施祥蕾, 等. 基于碳纳米薄膜/砷化镓范德华异质结的高性能自驱动光电探测器研究[J]. 中国光学,2022,15(2):373-386. doi: 10.37188/CO.2021-0149

    HUO T T, ZHANG D D, SHI X L, et al. High-performance self-powered photodetectors based on the carbon nanomaterial/GaAs vdW heterojunctions[J]. Chinese Optics, 2022, 15(2): 373-386. (in Chinese) doi: 10.37188/CO.2021-0149
    [25] O’SHEA T, HOYDIS J. An introduction to deep learning for the physical layer[J]. IEEE Transactions on Cognitive Communications and Networking, 2017, 3(4): 563-575. doi: 10.1109/TCCN.2017.2758370
    [26] AMIRABADI M A, KAHAEI M H, NEZAMALHOSSENI S A. Low complexity deep learning algorithms for compensating atmospheric turbulence in the free space optical communication system[J]. IET Optoelectronics, 2022, 16(3): 93-105.
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  • 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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