通信学报2025，Vol.46Issue(1)：144-156,13.DOI:10.11959/j.issn.1000-436x.2025018
超大规模太赫兹系统深度学习信道估计算法
Deep learning channel estimation algorithm for ultra-massive terahertz systems
摘要
Abstract
In order to further improve the hybrid-field channel estimation performance in terahertz ultra-massive multiple-input multiple-output systems,an efficient cross channel Transformer module for image restoration and a fast Fourier transform convolutional network were introduced based on the fixed point network,and a scalable and efficient deep learning model FPN-OTFN was proposed,which models the channel estimation problem as an image restoration problem.Firstly,the least squares algorithm was used to obtain the channel information at the pilot location,and then the channel information was input into the proposed FPN-OTFN algorithm.By training and learning the mapping relation-ship between low precision channel images and high-precision images,the true channel state information was restored.The simulation results show that the proposed scheme not only inherits the high efficiency and adaptivity of the FPN framework,but also possesses high estimation accuracy and good robustness for THz channels.关键词
信道估计/THz超大规模MIMO系统/深度学习/图像恢复/注意力机制Key words
channel estimation/THz ultra-massive MIMO system/deep learning/image restoration/attention mechanism分类
信息技术与安全科学引用本文复制引用
于舒娟,赵阳,魏玉尧,张昀,高贵,赵生妹..超大规模太赫兹系统深度学习信道估计算法[J].通信学报,2025,46(1):144-156,13.基金项目
国家自然科学基金资助项目(No.62375140) （No.62375140）
江苏省研究生科研与实践创新计划基金资助项目(No.KYCX23_0994)The National Natural Science Foundation of China(No.62375140),Postgraduate Research&Practice Innova-tion Program of Jiangsu Province(No.KYCX23_0994) （No.KYCX23_0994）
