RIS辅助的OFDM系统中时变信道估计方法  

Time-varying channel estimation in RIS-assisted OFDM system

作　　者：邵永琪 杨丽花 常澳 任露露 SHAO Yongqi;YANG Lihua;CHANG Ao;REN Lulu(College of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Jiangsu Key Laboratory of Wireless Communication,Nanjing 210003,China)

机构地区：[1]南京邮电大学通信与信息工程学院,江苏南京210003 [2]江苏省无线通信重点实验室,江苏南京210003

出　　处：《系统工程与电子技术》2025年第1期324-331,共8页Systems Engineering and Electronics

基　　金：江苏省重点研发计划(产业前瞻与关键核心技术)(BE2022067,BE2022067-1,BE2022067-2);江苏省重点研发计划(BE2020084-3)资助课题。

摘　　要：为了克服在可重构智能反射面(reconfigurable intelligent surface,RIS)辅助的正交频分复用(orthogonal frequency division multiplexing,OFDM)系统中现有基于深度学习的信道估计方法计算复杂度过高的问题,在RIS下利用基扩展模型(base extension model,BEM)对时变信道进行建模,并提出基于残差链接超分辨率卷积神经网络的时变信道估计方法。具体来说,所提方法首先将参数较多的信道系数估计转换为参数较少的基系数估计,以降低所提方法计算复杂度。在线下训练中,利用低分辨率的基系数估计对神经网络进行训练,仅需要少量的输入即可获取高分辨率的信道估计。为了提高所提方法的实用性,将网络训练的标签设置为具有高精度的信道估计值,而非理想的信道信息。仿真实验验证,所提方法在RIS辅助移动通信系统下能够准确获取时变信道信息,且具有更高的估计精度和更低的计算复杂度。To overcome the problem of high computational complexity in existing deep learning channel estimation methods in reconfigurable intelligent surface(RIS)-assisted orthogonal frequency division multiplexing(OFDM)system,the base extension model(BEM)is used to model the time-varying channel under RIS,and a time-varying channel estimation method based on residual link super-resolution convolutional neural network is proposed.Specifically,the proposed method firstly converts the channel coefficient estimation with more parameters to the base coefficient estimation with fewer parameters to reduce the computational complexity of the proposed method.In offline training process,the neural network is trained with low-resolution basis coefficient estimation,where only a small amount of input is required to obtain high-resolution channel estimation.In order to improve the practicability of the proposed method,the training network label is set to have high-precision channel estimation value instead of ideal channel information.The proposed method is verified by simulation test,which proves that it can accurately obtain time-varying channel information in RIS-assisted mobile communication system,and has higher estimation accuracy and lower computational complexity.

关 键 词：可重构智能反射面 正交频分复用 时变信道估计 基扩展模型 残差链接超分辨率卷积神经网络 

分 类 号：TN929.5[电子电信—通信与信息系统]