快时变信道下基于深度学习的OFDM系统信道估计

doi:10.12305/j.issn.1001-506X.2023.11.33

系统工程与电子技术 ›› 2023, Vol. 45 ›› Issue (11): 3649-3655.doi: 10.12305/j.issn.1001-506X.2023.11.33

快时变信道下基于深度学习的OFDM系统信道估计

季策¹^,², 宋博翰¹^,*, 耿蓉¹, 梁敏骏³

1. 东北大学计算机科学与工程学院, 辽宁沈阳 110169
2. 东北大学医学影像智能计算教育部重点实验室, 辽宁沈阳 110169
3. 东北大学信息科学与工程学院, 辽宁沈阳 110819

收稿日期:2022-10-31 出版日期:2023-10-25 发布日期:2023-10-31
通讯作者: 宋博翰
作者简介:季策 (1969—), 女, 副教授, 博士, 主要研究方向为OFDM关键技术研究、盲信号处理
宋博翰 (1997—), 男, 硕士研究生, 主要研究方向为深度学习OFDM系统信道估计
耿蓉 (1979—), 女, 副教授, 博士, 主要研究方向为无线网络、空间网络、网络安全及安全路由
梁敏骏 (2002—), 男, 本科生, 主要研究方向为深度学习
基金资助:
国防预研项目-国防重大培育项目(N2116015)

Deep learning based channel estimation for OFDM systems in fast time-varying channel

Ce JI¹^,², Bohan SONG¹^,*, Rong GENG¹, Minjun LIANG³

1. School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
2. Key Laboratory of Intelligent Computing for Medical Imaging, Ministry of Education, Northeastern University, Shenyang 110169, China
3. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Received:2022-10-31 Online:2023-10-25 Published:2023-10-31
Contact: Bohan SONG

摘要/Abstract

摘要：

针对快时变信道的非平稳特性会造成信道估计性能变差的问题, 在基扩展模型下提出了一种基于深度学习的信道估计算法, 并将其应用于正交频分复用(orthogonal frequecy division multiplexing, OFDM)系统中。首先, 根据快时变信道矩阵的局部相关特性, 构建时频特征提取网络, 利用卷积结构提取快时变信道在时域和频域的相关特征, 并嵌入到下一级网络中进行特征的融合。其次, 利用门控循环网络捕捉信道在不同符号处的变化相关性, 在快时变信道环境下实现更准确的信道估计。仿真结果表明, 与其他快时变环境下的信道估计算法相比, 算法的估计性能提升明显; 同时, 网络的轻量化结构使算法的复杂度最低下降20%。

关键词: 信道估计, 正交频分复用系统, 快时变, 深度学习, 基扩展模型

Abstract:

In order to solve the problem of poor channel estimation performance caused by non-stationary characteristics of fast time-varying channels, a channel estimation algorithm based on deep learning under basis expansion model is proposed and applied to orthogonal frequency division multiplexing (OFDM) systems. Firstly, according to the local correlation characteristics of the fast time-varying channel matrix, a time-frequency feature extraction network is constructed to extract the relevant features of the channel in time domain and frequency domain by using the convolution structure, and is embedded in the next network for feature fusion. Secondly, the gated recurrent unit is used to capture the time correlation of channel changes at different symbols, so as to achieve more accurate channel estimation in the fast time-varying channel environment. Simulation results show that compared with other channel estimation algorithms in fast time-varying environments, the performance of the proposed algorithm is improved obviously. Meanwhile, the lightweight structure of the network reduces the complexity of the algorithm by at least 20%.

Key words: channel estimation, northogonal frequecy division multiplexing (OFDM), fast time-varing, deep learning, basis expansion model

中图分类号:

TN911.72

季策, 宋博翰, 耿蓉, 梁敏骏. 快时变信道下基于深度学习的OFDM系统信道估计[J]. 系统工程与电子技术, 2023, 45(11): 3649-3655.

Ce JI, Bohan SONG, Rong GENG, Minjun LIANG. Deep learning based channel estimation for OFDM systems in fast time-varying channel[J]. Systems Engineering and Electronics, 2023, 45(11): 3649-3655.

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参考文献 17

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参数	数值
载波频率/GHz	2.8
系统采样率/MHz	2.52
子载波数目	180
调制方式	QPSK
基向量维度Q	4
导频数量	3
循环前缀	18

算法	乘法运算次数
BEM_LS	8.0e6
TF_GRU	4.9e7
BEM_LMMSE	6.1e7
T-ChanEstNet	7.1e7
BEM_UKF	9.3e7
BEM_EKF_RTSS	1.2e8

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快时变信道下基于深度学习的OFDM系统信道估计

Deep learning based channel estimation for OFDM systems in fast time-varying channel

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