BP neural network based Doppler frequency offset estimation method for 5G high-speed mobile system

Zenghao WANG; Lihua YANG; Lu CHENG; Jie ZHANG; Yan LIANG

doi:10.11959/j.issn.1000-0801.2020124

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BP neural network based Doppler frequency offset estimation method for 5G high-speed mobile system

Research and Development | 更新时间：2024-06-05

- BP neural network based Doppler frequency offset estimation method for 5G high-speed mobile system
- Telecommunications Science Vol. 36, Issue 4, Pages: 83-90(2020)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020124
  CLC： TN929.5
- Published Online：2020-04，
  
  Published：20 April 2020
- 稿件说明：
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Zenghao WANG, Lihua YANG, Lu CHENG, et al. BP neural network based Doppler frequency offset estimation method for 5G high-speed mobile system[J]. Telecommunications science, 2020, 36(4): 83-90.
DOI：

Zenghao WANG, Lihua YANG, Lu CHENG, et al. BP neural network based Doppler frequency offset estimation method for 5G high-speed mobile system[J]. Telecommunications science, 2020, 36(4): 83-90. DOI： 10.11959/j.issn.1000-0801.2020124.

摘要

提出了一种基于反向传播（back propagation，BP）算法训练的神经网络的多普勒频偏估计方法。所提方法主要分成线下训练与线上估计两个阶段，首先利用随机多普勒频偏与接收的导频符号构建训练样本，然后利用训练样本对BP神经网络进行线下训练，完成输入与输出数据之间的映射关系，最后基于训练后的网络利用接收导频符号数据，进行线上多普勒频偏估计。仿真结果表明，所提方法的估计性能远远优于现有方法，且具有较低的计算复杂度。

Abstract

A BP neural network based Doppler frequency offset estimation method was proposed.The proposed method was mainly divided into two stages:offline training and online estimation.Firstly

the training samples were constructed by using random Doppler frequency offset and received pilot signals

and then the training samples were used to train the BP neural network offline

which could complete the mapping relationship between input and output data.Then

based on the trained network

the received pilot signal was used to estimate the Doppler frequency offset online.Simulation results show that the performance of the proposed method is far superior to the existing method

and it has low computational complexity.

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Keywords

references

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