超大规模太赫兹系统深度学习信道估计算法

于舒娟; 赵阳; 魏玉尧; 张昀; 高贵; 赵生妹

doi:10.11959/j.issn.1000-436x.2025018

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超大规模太赫兹系统深度学习信道估计算法

学术论文 | 更新时间：2025-02-13

- 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
- 通信学报 2025年46卷第1期页码：144-156
- 作者机构：
  
  1.南京邮电大学电子与光学工程学院、柔性电子（未来技术）学院，江苏南京 210023
  2.南京邮电大学通信与信息工程学院，江苏南京 210003
- 作者简介：
  
  [ "于舒娟（1967- ），女，江苏南京人，南京邮电大学教授，主要研究方向为自适应信号处理、深度学习和智能大数据处理。" ]
  [ "赵阳（2000- ），男，江苏淮安人，南京邮电大学硕士生，主要研究方向为深度学习与信号处理。" ]
  [ "魏玉尧（2001- ），男，湖南邵阳人，南京邮电大学硕士生，主要研究方向为深度学习与信号处理。" ]
  [ "张昀（1975- ），女，江苏南京人，博士，南京邮电大学副教授，主要研究方向为智能化算法与通信信号处理。" ]
  [ "高贵（2000- ），男，安徽阜阳人，南京邮电大学硕士生，主要研究方向为深度学习与信号处理。" ]
  [ "赵生妹（1968- ），女，江苏丹徒人，博士，南京邮电大学教授，主要研究方向为量子通信与信息处理、无线通信与信号处理。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62375140);江苏省研究生科研与实践创新计划基金资助项目(KYCX23_0994)
- DOI：10.11959/j.issn.1000-436x.2025018
  中图分类号： TN92
- 收稿日期：2024-09-12，
  
  修回日期：2025-01-10，
  
  纸质出版日期：2025-01-25
- 稿件说明：
移动端阅览
于舒娟,赵阳,魏玉尧等.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(01):144-156.

YU Shujuan,ZHAO Yang,WEI Yuyao,et al.Deep learning channel estimation algorithm for ultra-massive terahertz systems[J].Journal on Communications,2025,46(01):144-156.
于舒娟,赵阳,魏玉尧等.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(01):144-156. DOI： 10.11959/j.issn.1000-436x.2025018.

YU Shujuan,ZHAO Yang,WEI Yuyao,et al.Deep learning channel estimation algorithm for ultra-massive terahertz systems[J].Journal on Communications,2025,46(01):144-156. DOI： 10.11959/j.issn.1000-436x.2025018.

摘要

为了进一步提升THz超大规模MIMO系统混合场信道估计性能，基于不动点网络（FPN）引入了一种基于跨通道信息交互的Transformer注意力机制模块与快速傅里叶变换卷积网络（FCN），提出了一种基于图像恢复网络的信道估计算法FPN-OTFN，将信道估计问题建模为图像恢复问题。在导频处采用最小二乘算法获得初始信道信息，并将其作为所提FPN-OTFN算法的输入，通过训练学习低精度信道图像和高精度图像间的映射关系，恢复出真实的信道状态信息。仿真实验结果表明，所提算法不仅继承了FPN框架的高效性、自适应性，同时对THz信道拥有较高的估计精度和良好的鲁棒性。

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 relationship 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

Keywords

references

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