基于神经网络的可见光通信系统信道估计方法  

Neural-Network-Based Channel Estimation Method for Visible Light Communication

作　　者：陈勇[1] 吴志倩 刘焕淋[2] 胡陈毅 吴金兰 王创世 Chen Yong;Wu Zhiqian;Liu Huanlin;Hu Chenyi;Wu Jinlan;Wang Chuangshi(Key Laboratory of Industrial Intermet of Things&Network Control,Ministry of Education,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学工业物联网与网络化控制教育部重点实验室,重庆400065 [2]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《光学学报》2023年第7期38-47,共10页Acta Optica Sinica

基　　金：国家自然科学基金(51977021)、重庆市自然科学基金(cstc2020jcyj-msxmX0682)、重庆市研究生科研创新项目(CYS22483)。

摘　　要：针对现有的非对称限幅光正交频分复用(ACO-OFDM)可见光通信(VLC)系统中信道估计方法存在导频数量过大、精度低、估计效率不高的问题,提出一种基于深度神经网络(DNN)的VLC信道估计方法。利用梯度集中化(GC)方法进行模型优化,并采用端到端的方式跟踪信道信息并恢复失真信号。仿真结果表明:所提方法的误码率(BER)和均方误差(MSE)性能均优于传统方法;在使用较少的导频和省略循环前缀(CP)进行信道估计时,所提方法具有更强的鲁棒性。此外,在DNN训练过程中引入GC方法,可以加快网络的收敛速度,提高其优化能力。Objective It has been widely noticed that visible light communication(VLC)has the advantages of anti-electromagnetic interference,abundant spectrum resources,and low cost.This paper introduces an efficient asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)modulation method to accommodate visible light communication systems using orthogonal frequency division multiplexing(OFDM)with positive real number constraints.However,the signal is easily distorted by multi-path interference of the channel during transmission,which results in poor communication quality of the VLC system.The VLC system mainly recovers the signal by obtaining the channel state information,and how to provide accurate feedback on the high-dimensional state information is particularly important to improve the communication quality of the VLC system.The commonly employed channel estimation method is based on the guide frequency assisted method.Among the existing methods,least squares(LS)method treats the channel as an ideal one and ignores its noise for channel estimation.Despite low complexity,the estimation accuracy is not high.Minimum mean square error(MMSE)is utilized for channel estimation due to the assumption that the second-order statistical information of the channel is known and adopted for channel estimation,but the estimation accuracy increases with the complexity.Deep learning provides a new solution for accurate feedback of channel state information,but few deep learning methods for channel estimation in ACO-OFDM systems have been reported.To improve the problems of low estimation accuracy and efficiency,and a large number of leads in channel estimation of ACO-OFDM systems,this paper proposes a deep neural network channel estimation method to improve the communication quality of the system.Methods A deep neural network(DNN)-based channel estimation method is proposed for the channel estimation of the ACO-OFDM visible light communication system.Within this scheme,an end-to-end approach is applied to impl

关 键 词：光通信 非对称限幅光正交频分复用 信道估计 深度神经网络 

分 类 号：TN929.1[电子电信—通信与信息系统]