差分编码与神经网络辅助的OFDM系统信道估计方法

卿朝进; 凌国伟; 王莉; 胡文权; 陈金良

doi:10.12263/DZXB.20230030

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差分编码与神经网络辅助的OFDM系统信道估计方法

学术论文 | 更新时间：2025-12-11

- 差分编码与神经网络辅助的OFDM系统信道估计方法
- Differential Coding and Neural Network-Aided Channel Estimation in OFDM Systems
- 电子学报 2024年52卷第6期页码：1852-1861
- 作者机构：
  
  1.西华大学电气与电子信息学院，四川成都 610039
  2.西华大学航空航天学院，四川成都 610039
- 作者简介：
  
  [ "卿朝进男，1978年出生，四川安岳人.教授、硕导、博士.主要研究方向为无线网络与通信.中国电子学会会员编号：E190158574M.E-mail: qingchj@mail.xhu.edu.cn" ]
  [ "凌国伟男，1998年出生，四川遂宁人.硕士研究生.主要研究方向为信道估计.E-mail: lgwbest1234@163.com" ]
- 基金信息：
  
  四川省科技计划项目(2021JDRC0003);四川省科技计划项目重大科技专项基金(19ZDZX0016);四川省产业发展专项资金(ZYF-2018-056);2020年成都市第二批重大科技应用示范项目(2020-YF09-00048-SN);四川省科技厅科技攻关项目(2021YFG0064)
- DOI：10.12263/DZXB.20230030
  中图分类号： TN914;
- 收稿：2023-01-06，
  
  修回：2023-03-01，
  
  纸质出版：2024-06-25
- 稿件说明：
移动端阅览
卿朝进,凌国伟,王莉,等. 差分编码与神经网络辅助的OFDM系统信道估计方法[J]. 电子学报,2024,52(06):1852-1861.

QING Chao-jin, LING Guo-wei, WANG Li, et al. Differential Coding and Neural Network-Aided Channel Estimation in OFDM Systems[J]. Acta Electronica Sinica, 2024, 52(06): 1852-1861.
卿朝进,凌国伟,王莉,等. 差分编码与神经网络辅助的OFDM系统信道估计方法[J]. 电子学报,2024,52(06):1852-1861. DOI：10.12263/DZXB.20230030

QING Chao-jin, LING Guo-wei, WANG Li, et al. Differential Coding and Neural Network-Aided Channel Estimation in OFDM Systems[J]. Acta Electronica Sinica, 2024, 52(06): 1852-1861. DOI：10.12263/DZXB.20230030

摘要

正交频分复用系统中，用于信道估计的导引占用宝贵的传输资源且消耗用户设备发射机能量.为应对这一困境，提出差分检测与神经网络相结合的信道估计方法.在发射端，将发送数据进行差分编码.在接收端，将差分译码后的数据视为发射的导引，借助面向判决信道估计思想，捕获信道估计的初始特征；在捕获到的初始特征的辅助下，构建增强信道估计网络（Enhanced Channel Estimation Network，En-CENet），融合差分与神经网络捕获的信道特征，改进信道估计精度.仿真结果表明，相对导引辅助信道估计和机器学习叠加信道估计方法，本文方法在提高系统频谱效率、节省发射机能量消耗、降低接收机计算复杂度和运行时间的同时，改善了信道估计精度.

Abstract

In orthogonal frequency division multiplexing systems

the pilot used for channel estimation occupies valuable transmission resources and consumes user equipment energy. To tackle this issue

a channel estimation method combining differential detection and deep neural network is proposed. At the transmitter

the transmitted data are differentially encoded. At the receiver

according to the idea of decision-directed channel estimation

the recovered data with differential decoding are regarded as the transmitted pilot to capture the initial features of the channel estimation. With the help of the captured initial features

an enhanced channel estimation network (En-CENet) is built to improve the channel estimation accuracy by integrating the differential features and channel features captured by the neural network. The simulation results show that

compared with the pilot-based channel estimation method and machine learning superposition channel estimation method

the proposed method improves the channel estimation accuracy while improving the spectral efficiency

saving the energy consumption of user equipment and reducing the computational complexity and running time of receiver.

关键词

Keywords

references

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