首页|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

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

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

Integrated Visible Light Communication and Position(IVLCP)Massive Multiple Input Multiple Output(m-MIMO)Deep learningChannel estimation and positioning

刘晓东、宁依婷、董帆、汤力为、王玉皞、王金元

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南昌大学信息工程学院 南昌 330031

上饶师范学院数字技术应用产业学院 上饶 334001

南京邮电大学通信与信息工程学院 南京 210003

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

江西省青年基金国家自然科学基金江苏省自然科学基金可见光通信重点实验室开放课题国家级大学生创新创业训练计划项目

20224BAB21200462061030BK20221328HKLVLC2023-B02202210403095

2024

10.11999/JEIT231389

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子：1.302
ISSN：1009-5896
年,卷(期)：2024.46(6)