高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于深度学习的水声信道联合多分支合并与均衡算法

刘志勇 金子皓 杨洪娟 刘彪 唐新丰 李博

刘志勇, 金子皓, 杨洪娟, 刘彪, 唐新丰, 李博. 基于深度学习的水声信道联合多分支合并与均衡算法[J]. 电子与信息学报, 2024, 46(5): 2004-2010. doi: 10.11999/JEIT231196
引用本文: 刘志勇, 金子皓, 杨洪娟, 刘彪, 唐新丰, 李博. 基于深度学习的水声信道联合多分支合并与均衡算法[J]. 电子与信息学报, 2024, 46(5): 2004-2010. doi: 10.11999/JEIT231196
LIU Zhiyong, JIN Zihao, YANG Hongjuan, LIU Biao, TANG Xinfeng, LI Bo. Deep Learning-based Joint Multi-branch Merging and Equalization Algorithm for Underwater Acoustic Channel[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2004-2010. doi: 10.11999/JEIT231196
Citation: LIU Zhiyong, JIN Zihao, YANG Hongjuan, LIU Biao, TANG Xinfeng, LI Bo. Deep Learning-based Joint Multi-branch Merging and Equalization Algorithm for Underwater Acoustic Channel[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2004-2010. doi: 10.11999/JEIT231196

基于深度学习的水声信道联合多分支合并与均衡算法

doi: 10.11999/JEIT231196
基金项目: 战略火箭创新基金(ZH2022007),国家自然科学基金(61871148),山东省重大科技创新工程(2020CXGC010705, 2021ZLGX05, 2022ZLGX04)
详细信息
    作者简介:

    刘志勇:男,副教授,研究方向为无线通信、水声通信

    金子皓:男,硕士生,研究方向为水声通信、人工智能

    杨洪娟:女,副教授,研究方向为无线通信、水声通信

    刘彪:男,工程师,研究方向为水声通信、无线通信

    唐新丰:男,工程师,研究方向为无线测控通信、数据链

    李博:男,副教授,研究方向为无线通信、水声通信

    通讯作者:

    杨洪娟 hjyang@hit.edu.cn

  • 中图分类号: TN929.3

Deep Learning-based Joint Multi-branch Merging and Equalization Algorithm for Underwater Acoustic Channel

Funds: The Strategic Rocket Innovation Foundation (ZH2022007), The National Natural Science Foundation of China (61871148), The Major Scientific and Technological Innovation Project of Shandong Province of China (2020CXGC010705, 2021ZLGX05, 2022ZLGX04)
  • 摘要: 为了更好地解决水声信道中的衰落及严重码间干扰问题,该文提出一种基于深度学习的联合多分支合并与均衡算法。该算法借助深度学习网络的非线性拟合能力,联合实现了多分支合并和均衡。在算法实现中,合并与均衡并非相互独立,而是基于深度学习网络的总输出计算出总误差,以总误差对网络参数实现联合调整,数据集则基于统计水声信道模型进行构建。仿真结果表明,相较于已有算法,所提算法能获得更快的收敛速度和更好的误码率性能,使得其能更好地适应水声信道。
  • 图  1  水声SIMO通信系统示意图

    图  2  JMME-DL算法结构示意图

    图  3  SIGMOID激活函数示意图

    图  4  发射机与各个水听器间的信道冲激响应

    图  5  网络层数对算法性能的影响对比图

    图  6  水声信道下算法误码率性能比较

    图  7  水声信道下算法收敛曲线

    1  JMME-DL算法

     输入:训练集:$ \mathcal{D} = \left\{ {\left( {{r_i}(n),s(n)} \right)} \right\}_{i = 1}^N $中$ K $组数据;验证集:
      $ V $;学习率:$ \alpha $;正则化系数:$ \lambda $;迭代次数:$ M $
     初始化:$ {\boldsymbol{\theta}} ,{\boldsymbol{b}} $
     repeat
      for i = 1 2 ··· M do
       (1) 从训练集$ \mathcal{D} $中选取$ K $组数据样本
       (2) 前馈计算,直到最后一层并计算总输出
       (3) 反向传播计算每一层的误差
        // 计算每一层参数的导数
        $ \dfrac{{\partial L}}{{\partial {{\boldsymbol{\theta}} }_{^{(i)}}^{^{(l)}}}} = {({{\boldsymbol{o}}}_i^{(l - 1)})^{\text{T}}}{{\boldsymbol{\delta}} }_i^l{\text{ }}(l = 1, \cdots ,L - 1) $
        $ \dfrac{{\partial L}}{{\partial {{\boldsymbol{b}}}_{^{(i)}}^{^{(l)}}}} = {\text{sum}}\{ {{\boldsymbol{\delta}} }_i^l{\text{\} }}(l = 1, \cdots ,L - 1) $
        // 更新参数
        $ {{\boldsymbol{\theta}} }_{(i)}^{^{(l)}} = {{\boldsymbol{\theta }}}_{^{(i)}}^{^{(l)}} - \alpha {({{\boldsymbol{o}}}_i^{(l - 1)})^{\text{T}}}{{\boldsymbol{\delta}} }_i^l{\text{ }}(l = 1, \cdots ,L - 1) $
        $ {{\boldsymbol{b}}}_{^{(i)}}^{^{(l)}} = {{\boldsymbol{b}}}_{^{(i)}}^{^{(l)}} - \alpha {{\boldsymbol{\delta}} }_i^l{\text{ }}(l = 1, \cdots ,L - 1) $
     until训练的模型在验证集$ V $的错误率不再下降;
     输出:$ {\boldsymbol{\theta}} ,{\boldsymbol{b}} $
    下载: 导出CSV

    表  1  水声信道仿真主要参数

    仿真参数数值
    海水深度(m)300
    发射机深度(m)100
    水听器1深度(m)
    水听器2深度(m)
    120
    125
    水听器3深度(m)130
    发射机与水听器水平距离(m)3000
    水下传播系数1.6
    水下声速(m/s)1500
    载波频率(kHz)10
    带宽(kHz)5
    下载: 导出CSV
  • [1] 邵宗战. 现代水声通信技术发展探讨[J]. 科技创新与应用, 2022, 12(20): 152–155. doi: 10.19981/j.CN23-1581/G3.2022.20.036.

    SHAO Zongzhan. Discussion on the development of modern hydroacoustic communication technology[J]. Technology Innovation and Application, 2022, 12(20): 152–155. doi: 10.19981/j.CN23-1581/G3.2022.20.036.
    [2] XIE Lin, ZHAO Haili, TIAN Chengjun, et al. Comparison of several new improved variable-step LMS algorithms[C]. 2022 7th International Conference on Automation, Control and Robotics Engineering (CACRE), Xi’an, China, 2022: 229–233. doi: 10.1109/CACRE54574.2022.9834206.
    [3] GUO Xiaochen, ZHANG Youwen, and ZHENG Wei. Variable forgetting factor RLS algorithm for mobile single carrier SIMO underwater acoustic communication[C]. Proceedings of SPIE 12615, International Conference on Signal Processing and Communication Technology (SPCT 2022), Harbin, China, 2023: 1261519. doi: 10.1117/12.2674104.
    [4] CHENG Xing, LIU Dejun, WANG Chen, et al. Deep learning-based channel estimation and equalization scheme for FBMC/OQAM systems[J]. IEEE Wireless Communications Letters, 2019, 8(3): 881–884. doi: 10.1109/LWC.2019.2898437.
    [5] CHEN S, GIBSON G J, COWAN C F N, et al. Adaptive equalization of finite non-linear channels using multilayer perceptrons[J]. Signal Processing, 1990, 20(2): 107–119. doi: 10.1016/0165-1684(90)90122-F.
    [6] LAVANIA S, KUMAM B, MATEY P S, et al. Adaptive channel equalization using recurrent neural network under SUI channel model[C]. 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 2015: 1–6. doi: 10.1109/ICIIECS.2015.7193035.
    [7] ZHANG Youwen, LI Junxuan, ZAKHAROV Y, et al. Deep learning based underwater acoustic OFDM communications[J]. Applied Acoustics, 2019, 154: 53–58. doi: 10.1016/j.apacoust.2019.04.023.
    [8] JARUWATANADILOK S. Underwater wireless optical communication channel modeling and performance evaluation using vector radiative transfer theory[J]. IEEE Press, 2008, 26(9): 1620–1627. doi: 10.1109/JSAC.2008.081202.
    [9] STOJANOVIC M, CATIPOVIC J, and PROAKIS J G. Adaptive multichannel combining and equalization for underwater acoustic communications[J]. The Journal of the Acoustical Society of America, 1993, 94(3): 1621–1631. doi: 10.1121/1.408135.
    [10] CHOI J W, RIEDL T J, KIM K, et al. Adaptive linear turbo equalization over doubly selective channels[J]. IEEE Journal of Oceanic Engineering, 2011, 36(4): 473–489. doi: 10.1109/JOE.2011.2158013.
    [11] LIU Zhiyong, WANG Yinghua, SONG Lizhong, et al. Joint adaptive combining and variable tap-length multiuser detector for underwater acoustic cooperative communication[J]. KSII Transactions on Internet and Information Systems, 2018, 12(1): 325–339. doi: 10.3837/tiis.2018.01.016.
    [12] QARABAQI P and STOJANOVIC M. Statistical characterization and computationally efficient modeling of a class of underwater acoustic communication channels[J]. IEEE Journal of Oceanic Engineering, 2013, 38(4): 701–717. doi: 10.1109/JOE.2013.2278787.
  • 加载中
图(7) / 表(2)
计量
  • 文章访问数:  64
  • HTML全文浏览量:  31
  • PDF下载量:  11
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-10-31
  • 修回日期:  2024-03-27
  • 网络出版日期:  2024-05-07
  • 刊出日期:  2024-05-10

目录

    /

    返回文章
    返回