一种新型的基于深度学习的时变信道预测方法

张捷; 杨丽花; 王增浩; 呼博; 聂倩

doi:10.11959/j.issn.1000-0801.2021011

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一种新型的基于深度学习的时变信道预测方法

研究与开发 | 更新时间：2024-06-05

- 一种新型的基于深度学习的时变信道预测方法
- A novel deep learning based time-varying channel prediction method
- 电信科学 2021年37卷第1期页码：39-47
- 作者机构：
- 作者简介：
  
  [ "张捷（1996- ），女，南京邮电大学硕士生，主要研究方向为宽带移动通信。" ]
  [ "杨丽花（1984- ），女，南京邮电大学副教授，主要研究方向为移动无线通信、通信信号处理、多载波通信系统等。" ]
  [ "王增浩（1994- ），男，南京邮电大学硕士生，主要研究方向为移动通信。" ]
  [ "呼博（1996- ），男，南京邮电大学硕士生，主要研究方向为宽带移动通信。" ]
  [ "聂倩（1997- ），女，南京邮电大学硕士生，主要研究方向为移动通信。" ]
- 基金信息：
  
  江苏省科技厅自然科学基金资助项目;The National Science Foundation Program of Jiangsu Province(BK20191378);江苏省高等学校自然科学研究面上项目;The National Science Research Project of Jiangsu Higher Education Institutions(18KJB510034);第11批中国博士后科学基金特别资助项目;The 11th Batch of China Postdoctoral Science Fund Special Funding Project(2018T110530);国家自然科学基金资助项目;The National Natural Science Foundation of China(61771255)
- DOI：10.11959/j.issn.1000-0801.2021011
  中图分类号： TN911
- 网络出版日期：2021-01，
  
  纸质出版日期：2021-01-20
- 稿件说明：
移动端阅览
张捷, 杨丽花, 王增浩, 等. 一种新型的基于深度学习的时变信道预测方法[J]. 电信科学, 2021,37(1):39-47.

Jie ZHANG, Lihua YANG, Zenghao WANG, et al. A novel deep learning based time-varying channel prediction method[J]. Telecommunications science, 2021, 37(1): 39-47.
张捷, 杨丽花, 王增浩, 等. 一种新型的基于深度学习的时变信道预测方法[J]. 电信科学, 2021,37(1):39-47. DOI： 10.11959/j.issn.1000-0801.2021011.

Jie ZHANG, Lihua YANG, Zenghao WANG, et al. A novel deep learning based time-varying channel prediction method[J]. Telecommunications science, 2021, 37(1): 39-47. DOI： 10.11959/j.issn.1000-0801.2021011.

摘要

针对高速移动正交频分复用系统，提出了一种新型的基于深度学习的时变信道预测方法。为了避免网络参数随机初始化造成的影响，本文方法首先基于数据与导频信息获取较理想的信道估计，利用其对 BP神经网络进行预训练处理，以获取理想的网络初始参数；然后，基于预训练获取网络初始值，利用基于导频获取的信道估计对BP神经网络进行再次训练，以获取最终的信道预测网络模型；最后，本文方法基于该预测网络模型通过线上预测实现了时变信道的单时刻与多时刻预测。仿真结果表明，本文方法可以显著地提高时变信道预测精度，且具有较低的计算复杂度。

Abstract

For high-speed mobile orthogonal frequency division multiplexing (OFDM) systems

a novel time-varying channel prediction method based on deep learning was proposed.To avoid the influence caused by random initialization of network parameters

the proposed method firstly obtains an ideal channel estimation based on data and pilot

and then pre-trains the back propagation (BP) neural network based on the channel estimation to obtain the ideal network initial parameters.Then

based on the initial network value obtained by pre-training

the proposed method uses the channel estimation based on pilot to train the BP neural network again.Finally

the proposed method realizes the single-time-and multi-time prediction of time-varying channels through on-line prediction.Simulation results show that the proposed method can significantly improve the prediction accuracy of time-varying channels and has a low computational complexity.

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Keywords

references

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