计算机与数字工程2026，Vol.54Issue(4)：952-956,977,6.DOI:10.3969/j.issn.1672-9722.2026.04.008
基于深度学习的智能反射面辅助信道估计
Intelligent Reflecting Surface Aided Channel Estimation Based on Deep Learning
摘要
Abstract
Aiming at the problem that the intelligent reflecting surface(IRS)lacks a radio frequency(RF)chain and it is dif-ficult to obtain more accurate channel state information(CSI),this paper proposes a channel estimation method based on deep re-sidual learning.For an IRS-assisted orthogonal frequency division multiplexing(OFDM)system,in which a convolutional network(CNN)module with element-wise subtraction is set up to remove noise in the channel,IRS is considered a promising technique,which is achieved by adjusting wireless environment to improve spectrum and energy utilization,the industry regards it as a poten-tial 6G wireless communication technology,channel estimation is one of the main tasks of using IRS-assisted communication,CSI is to design the optimal wireless communication system in the IRS-assisted communication system.Source beamforming is a key fac-tor,so an accurate channel estimation algorithm is crucial for IRS-assisted communication.Simulation results show that the deep learning method proposed in this paper has better performance than traditional methods.关键词
深度残差学习/智能反射面/信道估计/OFDMKey words
deep residual learning/intelligent reflecting surface/channel estimation/OFDM分类
信息技术与安全科学引用本文复制引用
胡玉龙,周杰,刘骐榕..基于深度学习的智能反射面辅助信道估计[J].计算机与数字工程,2026,54(4):952-956,977,6.基金项目
国家自然科学基金面上项目(编号:61771248)资助. （编号:61771248）
