无线电工程2026，Vol.56Issue(4)：668-674,7.DOI:10.3969/j.issn.1003-3106.2026.04.011
基于改进FSRCNN模型的OFDM图像传输系统信道估计方法
Channel Estimation Method for OFDM Image Transmission Systems Based on an Improved FSRCNN Model
摘要
Abstract
Orthogonal Frequency Division Multiplexing(OFDM)technology is widely used in modern communication systems.However,in end-to-end image transmission under low Signal to Noise Ratio(SNR)conditions from 0 to 10 dB,OFDM faces problems of insufficient channel estimation accuracy and memory overflow in local deployment.To address these problems,non-overlapping block partitioning is adopted for image preprocessing,which solves the memory overflow problem caused by single-block processing and improves dataset diversity.Meanwhile,an improved Fast Super-Resolution Convolutional Neural Network(FSRCNN)channel estimation model fusing Residual Network(ResNet)and Coordinate Attention(CA)mechanism is proposed,denoted as ResNet CA Fast Super-Resolution Convolutional Neural Network(RC-FSRCNN).The proposed model enhances feature extraction capability and alleviates the gradient vanishing problem in deep networks,thereby improving channel estimation performance for image transmission.Experimental results show that,compared with Least Square(LS)method with linear interpolation,Linear Minimum Mean Squared Error(LMMSE)method,and the original FSRCNN model,RC-FSRCNN achieves lower Mean Squared Error(MSE)in the low-SNR region.This study provides an efficient channel estimation scheme for OFDM image transmission systems.关键词
正交频分复用/快速超分辨率卷积神经网络/信道估计/残差网络/坐标注意力机制Key words
OFDM/FSRCNN/channel estimation/ResNet/CA mechanism分类
信息技术与安全科学引用本文复制引用
张学刚,王涛,马爱玲,李姝慧,王昱博,李思憶..基于改进FSRCNN模型的OFDM图像传输系统信道估计方法[J].无线电工程,2026,56(4):668-674,7.基金项目
2022年度青海省重点研发与转化计划科技援青合作专项项目(2022-QY-205)2022 Key R&D and Transformation Program-Science & Technology Aid-Qinghai Cooperation Special Project(2022-QY-205) （2022-QY-205）
