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大规模多输入多输出可见光通感一体化系统的信道状态信息还原和定位

刘晓东 宁依婷 董帆 汤力为 王玉皞 王金元

刘晓东, 宁依婷, 董帆, 汤力为, 王玉皞, 王金元. 大规模多输入多输出可见光通感一体化系统的信道状态信息还原和定位[J]. 电子与信息学报. doi: 10.11999/JEIT231389
引用本文: 刘晓东, 宁依婷, 董帆, 汤力为, 王玉皞, 王金元. 大规模多输入多输出可见光通感一体化系统的信道状态信息还原和定位[J]. 电子与信息学报. doi: 10.11999/JEIT231389
LIU Xiaodong, NING Yiting, DONG Fan, TANG Liwei, WANG Yuhao, WANG Jinyuan. Channel State Information Restoration and Positioning of massive Multiple Input Multiple Output Integrated Visible Light Communication and Sensing System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231389
Citation: LIU Xiaodong, NING Yiting, DONG Fan, TANG Liwei, WANG Yuhao, WANG Jinyuan. Channel State Information Restoration and Positioning of massive Multiple Input Multiple Output Integrated Visible Light Communication and Sensing System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231389

大规模多输入多输出可见光通感一体化系统的信道状态信息还原和定位

doi: 10.11999/JEIT231389
基金项目: 江西省青年基金(20224BAB212004),国家自然科学基金(62061030),江苏省自然科学基金(BK20221328),可见光通信重点实验室开放课题(HKLVLC2023-B02),国家级大学生创新创业训练计划项目(202210403095)
详细信息
    作者简介:

    刘晓东:男,副教授,研究方向为可见光通信和感知网络

    宁依婷:女,本科生,研究方向为可见光通信与感知一体化设计

    董帆:男,硕士生,研究方向为深度学习赋能的可见光通信系统

    汤力为:男,硕士生,研究方向为可见光通信系统硬件系统设计

    王玉皞:男,教授, 研究方向为宽带无线通信网络和感知探测

    王金元:男,副教授,研究方向为可见光通信、无人机通信等

    通讯作者:

    王玉皞 wangyuhao@ncu.edu.cn

  • 中图分类号: TN911.23

Channel State Information Restoration and Positioning of massive Multiple Input Multiple Output Integrated Visible Light Communication and Sensing System

Funds: The Young Natural Science Foundation of Jiangxi Province (20224BAB212004), The National Natural Science Foundation of China (62061030), The Natural Science Foundation of Jiangsu Province (BK20221328), The Open Project of Key Laboratory of Visible Light Communication (HKLVLC2023-B02), The National Training Program of Innovation and Entrepreneurship for Undergraduates (202210403095)
  • 摘要: 得益于丰富的频谱和光源,可见光通信感知一体化(IVLCP)系统为解决高性能通信定位的室内无线网络需求提供强有力的技术支撑。同时,大规模多输入多输出(m-MIMO)技术能有效提高IVLCP网络的服务范围和质量。然而,m-MIMO赋能的IVLCP网络的信道环境更加复杂且先验信息更易变化,这使得传统方法难以快速准确地完成信道估计和定位。针对此,该文提出一种信道状态信息还原和定位(CSIRP)网络,该网络不仅能够有效地捕捉复杂分布的可见光通信信道特征,同时能够应对信道状态的时变性,从而提高信道和位置估计的鲁棒性和动态适应性。具体而言,CSIRP网络首先基于条件生成对抗思想自适应训练生成器和鉴别器,进而实现根据接收信号进行信道估计,接着结合长短期记忆网络(LSTM)从估计的信道中获取接收终端的位置估计值。仿真结果表明,采用CSIRP网络所获得的信道状态准确度和定位精度均优于现有的深度学习参考方法,这为m-MIMO赋能的IVLCP系统提供了可靠和精准的信道状态信息和位置感知能力。
  • 图  1  m-MIMO赋能IVLCP系统场景

    图  2  m-MIMO可见光通信系统

    图  3  所提CSIRP网络结构

    图  4  m-MIMO可见光室内场景下的信道特征

    图  5  生成对抗训练和部署的基本框架

    图  6  不同配置下所提网络的信道估计性能

    图  7  不同接收SNR下的网络性能对比

    图  8  不同网络定位性能对比

    表  1  所考虑场景的参数设置

    参数 名称 数值 参数 名称 数值
    ${N_{\text{r}}}$ PD阵列 64 ${\phi _{{\text{oc}}}}$ PD的视场角 70°
    $\tau $ 导频序列长度 8 ${\beta _{{\text{semi}}}}$ 半功率角 60°
    ${A_{\text{r}}}$ PD的有效光电接收区域面积 1 cm2 $ n $ OC的折射率 1.5
    下载: 导出CSV

    表  2  网络模型性能比较

    网络模型 模型深度
    (层)
    平均定位
    误差(cm)
    收敛时的
    RMSE
    (×10–2)
    收敛时的
    损失(×10–2)
    CSIRP 28 5.29 1.59 0.02
    FFDNet-LSTM 24 14.28 4.71 1.09
    CGAN-DNN 25 23.72 8.13 3.78
    CGAN-CNN 31 125.74 84.28 25.26
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-12-18
  • 修回日期:  2024-04-03
  • 网络出版日期:  2024-04-21

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