基于1D-Concatenate的信道估计DNN模型优化方法

卢敏; 秦泽豪; 陈志辉; 张敏; 乐光学

doi:10.11959/j.issn.1000-0801.2023097

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基于1D-Concatenate的信道估计DNN模型优化方法

研究与开发 | 更新时间：2024-06-05

- 基于1D-Concatenate的信道估计DNN模型优化方法
- 1D-Concatenate based channel estimation DNN model optimization method
- 电信科学 2023年39卷第4期页码：71-86
- 作者机构：
  
  1. 江西理工大学理学院，江西赣州 341000
  2. 嘉兴学院信息科学与工程学院，浙江嘉兴 314000
  3. 浙江省医学电子与数字健康重点实验室，浙江嘉兴 314000
- 作者简介：
  
  [ "卢敏（1964– ），男，江西理工大学理学院教授，主要研究方向为电子材料与器件、智能通信" ]
  [ "秦泽豪（1998– ），男，江西理工大学理学院硕士生，嘉兴学院信息科学与工程学院联培研究生，主要研究方向为智能通信、深度学习" ]
  [ "陈志辉（1999– ），男，江西理工大学理学院硕士生，嘉兴学院信息科学与工程学院联培研究生，主要研究方向为人工智能、深度学习" ]
  [ "张敏（1996– ），女，江西理工大学理学院硕士生，嘉兴学院信息科学与工程学院联培研究生，主要研究方向为深度学习与智能通信" ]
  [ "乐光学（1963– ），男，博士，嘉兴学院信息科学与工程学院、浙江省医学电子与数字健康重点实验室教授，主要研究方向为多云融合与协同服务、边缘计算与一体化通信网络、深度学习与智能通信" ]
- 基金信息：
  
  国家自然科学基金重点项目;The National Natural Science Foundation Key Project of China(U19B2015)
- DOI：10.11959/j.issn.1000-0801.2023097
  中图分类号： TN393
- 网络出版日期：2023-04，
  
  纸质出版日期：2023-04-20
- 稿件说明：
移动端阅览
卢敏, 秦泽豪, 陈志辉, 等. 基于1D-Concatenate的信道估计DNN模型优化方法[J]. 电信科学, 2023,39(4):71-86.

Min LU, Zehao QIN, Zhihui CHEN, et al. 1D-Concatenate based channel estimation DNN model optimization method[J]. Telecommunications science, 2023, 39(4): 71-86.
卢敏, 秦泽豪, 陈志辉, 等. 基于1D-Concatenate的信道估计DNN模型优化方法[J]. 电信科学, 2023,39(4):71-86. DOI： 10.11959/j.issn.1000-0801.2023097.

Min LU, Zehao QIN, Zhihui CHEN, et al. 1D-Concatenate based channel estimation DNN model optimization method[J]. Telecommunications science, 2023, 39(4): 71-86. DOI： 10.11959/j.issn.1000-0801.2023097.

摘要

为提高DNN模型在无线通信中信道估计精度，提出一种基于1D-Concatenate的信道估计DNN模型优化方法。该方法将Concatenate进行一维（1D）数据转换，以跳跃连接的方式引入DNN模型，抑制梯度消失问题，运用1D-Concatenate恢复网络训练过程中丢失的数据特征，提高DNN信道估计精度。为验证优化方法的有效性，选取较典型的基于DNN的无线通信信道估计模型进行对比仿真实验。实验结果表明，本文提出的优化方法对已有DNN模型的估计增益提升可达77.10%，在高信噪比下信道增益提升可达3 dB。该优化方法能有效提高DNN模型在无线通信中的信道估计精度，特别是高信噪比下提升效果显著。

Abstract

In order to improve the channel estimation accuracy of DNN model in wireless communication

a DNN model optimization method based on 1D-Concatenate was proposed.In this method

Concatenate performs one-dimensional data transformation

the DNN model was introduced by hopping connection

the gradient disappearance problem was suppressed

and 1D-Concatenate was used to restore the data features lost during network training to improve the accuracy of DNN channel estimation.In order to verify the effectiveness of the optimization method

a typical DNN-based wireless communication channel estimation model was selected for comparative simulation experiments.Experimental results show that the estimated gain of the existing DNN model can be increased by 77.10% by the proposed optimization method

and the channel gain can be increased by up to 3 dB under high signal-to-noise ratio.This optimization method can effectively improve the channel estimation accuracy of DNN model in wireless communication

especially the improvement effect is significant under high signal-to-noise ratio.

关键词

Keywords

references

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