华南理工大学学报（自然科学版）2026，Vol.54Issue(1)：53-59,7.DOI:10.12141/j.issn.1000-565X.240594
基于增强型残差递归门控网络的信道估计方法
Channel Estimation Method Based on Channel Enhanced Deep Horblock Network
摘要
Abstract
In high-mobility scenarios,wireless communications undergo time and frequency doubly selective fa-ding,making channel estimation essential for accurately obtaining channel state information(CSI),which in turn en-hances the perfor-mance of communication systems.The Time-Frequency Doubly Selective Channel is a channel model that characterizes signal fading with selective properties in both time and frequency dimensions.To address the challenges of channel estimation in such environments,deep learning methods have been widely adopted in re-cent years.Networks that originally excelled in computer vision and natural language processing,such as Convolu-tional Neural Networks(CNN)and Long Short-Term Memory networks(LSTM),have been applied to channel estima-tion techniques.However,due to significant differences in data characteristics and task objectives between channel estimation and image processing,these approaches still face numerous challenges.This study introduced a novel channel estimation deep learning algorithm based on a Channel Enhanced Deep Horblock Network(CEHNet).The proposed algorithm treats the time-frequency grid of the doubly selective channel as a two-dimensional image and employs a Super-Resolution(SR)network to reconstruct the CSI.Additionally,a preprocessing method that in-creases amplitude features is utilized to expand the dataset,and Lasso regression is incorporated as a constraint to accelerate the network convergence speed.Experimental results demonstrate that,across various channel models,the proposed CEHNet algorithm outperforms traditional channel estimation methods such as Super-Resolution Con-volutional Neural Networks(SRCNN)when the number of pilots is limited.Furthermore,CEHNet exhibits signifi-cantly faster convergence rates,achieving a fourfold performance improvement over SRCNN at a signal-to-noise ra-tio(SNR)of 22 dB.关键词
信道估计/超分网络/时频双选信道/递归门控卷积Key words
channel estimation/super-resolution network/dual-selection channel/recursive gated convolution分类
信息技术与安全科学引用本文复制引用
刘娇蛟,王若尘,马碧云..基于增强型残差递归门控网络的信道估计方法[J].华南理工大学学报（自然科学版）,2026,54(1):53-59,7.基金项目
广东省基础与应用基础研究基金项目(2022A1515011830,2023A1515011420) （2022A1515011830,2023A1515011420）
国家重点研发计划项目(2024YFE0105400) （2024YFE0105400）
国家外国专家项目(H20241005)Supported by the Guangdong Basic and Applied Basic Research Foundation(2022A1515011830,2023A1515011420),the National Key Research and Development Program of China(2024YFE0105400)and the National Foreign Expert Project(H20241005) （H20241005）
