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  

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

作　　者：刘晓东 宁依婷 董帆 汤力为 王玉皞[1,2] 王金元 LIU Xiaodong;NING Yiting;DONG Fan;TANG Liwei;WANG Yuhao;WANG Jinyuan(School of Information Engineering,Nanchang University,Nanchang 330031,China;Digital Technology Application Industry College,Shangrao Normal University,Shangrao 334001,China;School of Communications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)

机构地区：[1]南昌大学信息工程学院,南昌330031 [2]上饶师范学院数字技术应用产业学院,上饶334001 [3]南京邮电大学通信与信息工程学院,南京210003

出　　处：《电子与信息学报》2024年第6期2391-2400,共10页Journal of Electronics & Information Technology

基　　金：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)。

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。Benefiting from rich spectrum and lamps,Integrated Visible Light Communication and Positioning(IVLCP)systems provide powerful technological solution to meet the high performance communication and positioning in indoor wireless networks.Meanwhile,the massive Multiple Input Multiple Output(m-MIMO)effectively enhance both service coverage and quality of IVLCP systems.However,the channel environment is more complex and the priori information rapidly changed in the m-MIMO-enabled IVLCP systems,making traditional methods challenging for fast and accurate channel estimation and positioning.In order to tackle this challenge,a Channel State Information Restoration and Positioning(CSIRP)network is proposed in this paper.The network not only effectively captures complex distribution feature of channel but also addresses the temporal variations in location,thereby enhancing the robustness and dynamic adaptability of channel and location estimation.Specifically,the CSIRP network employs a conditional generative adversarial process to adaptively train the generator and discriminatorr and thus achieves the channel estimation from the received signals.Then,the Long Short-Term Memory(LSTM)is introduced to estimate the location of the receiver from the estimated channel.Simulation results demonstrate that the accuracy of both channel and location estimation achieved by the proposed CSIRP network outperforms existing deep learning benchmark schemes.This provides m-MIMO-enabled IVLCP systems with more reliable and accurate channel state information and positioning.

关 键 词：可见光通感一体化 大规模多输入多输出 深度学习 信道估计和定位 

分 类 号：TN911.23[电子电信—通信与信息系统]