数据采集与处理2025，Vol.40Issue(6)：1424-1433,10.DOI:10.16337/j.1004-9037.2025.06.004
非地面网络场景中基于全局超分去噪的信道估计
Channel Estimation Based on Global Super-Resolution Denoising in Non-terrestrial Network Scenarios
摘要
Abstract
In non-terrestrial network(NTN)scenarios,to overcome the effect of large Doppler frequency offset on the communication,a channel estimation method based on global information super resolution denoising neural network(GSRDnNet)is proposed.This method considers the channel estimation matrix at the pilot obtained by the least square(LS)estimation algorithm as a low-resolution small-size image and takes it as the input to the neural network.The input data is then processed by the GSRDnNet network to obtain a more accurate high-resolution image with a complete channel response matrix for the time-frequency resource block.Four NTN-tapped delay line(TDL)A,B,C and D channel models are used for simulation verification.Simulation results indicate that GSRDnNet improves mean squared error(MSE)performance by 3.37-8.83 dB compared to the traditional LS algorithm.Compared with the practical channel estimation(PCE)algorithm,the MSE is improved by 2.11-6.06 dB,and compared with the SRCNN+DnCNN method,which requires pre-interpolation processing,the MSE is improved by 1.37-4.40 dB.And compared with super resolution convolutional neural network(SRCNN)+denoising convolutional neural network(DnCNN),the input of GSRDnNet network model only considers the channel estimation matrix at the pilot,so it not only has higher estimation accuracy,but also reduces the computational complexity by about 84%.关键词
多普勒频偏/全局信息/超分/去噪/信道估计Key words
Doppler frequency offset/global information/super-resolution/denoise/channel estimation分类
信息技术与安全科学引用本文复制引用
任晓宁,段红光,黄凤翔,董诗康..非地面网络场景中基于全局超分去噪的信道估计[J].数据采集与处理,2025,40(6):1424-1433,10.基金项目
重庆市自然科学基金(cstc2019jcyj-msxmX0079). （cstc2019jcyj-msxmX0079）
