Channel Estimation Method of OFDM System based on Compressed Sensing

LI Gui-yong; Lü Jing-zhao; CHEN Bo; QIN Hong; FANG Ze-sheng

doi:10.13756/j.gtxyj.2022.01.010

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Channel Estimation Method of OFDM System based on Compressed Sensing

更新时间：2022-06-15

- Channel Estimation Method of OFDM System based on Compressed Sensing
- Study on Optical Communications Issue 1, Pages: 52-57(2022)
- 作者机构：
  
  重庆邮电大学　通信与信息工程学院，重庆　400065
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2022.01.010
  CLC： TN929.5
- Published：10 February 2022，
  
  Received：09 March 2021，
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LI Gui-yong, Lü Jing-zhao, CHEN Bo, et al. Channel Estimation Method of OFDM System based on Compressed Sensing. [J]. Study on Optical Communications (1):52-57(2022)
DOI：

LI Gui-yong, Lü Jing-zhao, CHEN Bo, et al. Channel Estimation Method of OFDM System based on Compressed Sensing. [J]. Study on Optical Communications (1):52-57(2022) DOI： 10.13756/j.gtxyj.2022.01.010.

摘要

针对无线信道的时域稀疏性以及稀疏度未知的问题，文章将压缩感知技术应用到正交频分复用(OFDM)系统信道估计中，提出了一种稀疏度自适应正交匹配追踪信道估计算法。算法利用离散傅里叶变换(DFT)信道估计算法对循环前缀内和外的噪声进行处理，估计得到的信道频率响应作为正交匹配追踪(OMP)算法稀疏迭代终止的判断条件，实现稀疏度自适应信号重建。同时在原子预选阶段，采用Dice系数准则代替内积准则作为相关性度量准则，可达到更优的估计性能。仿真结果表明，该算法相比于传统的压缩感知信道估计算法具有较好的性能，可以提高系统的归一化均方误差(NMSE)和误码率(BER)性能。

Abstract

Aiming at the time-domain sparsity and unknown sparsity of wireless channels

compressed sensing technology is applied to the channel estimation of Orthogonal Frequency Division Multiplexing (OFDM) system. This paper proposes a sparsity adaptive matching pursuit channel estimation algorithm. It uses the Discrete Fourier Transform (DFT) channel estimation algorithm to process the noise inside and outside the cyclic prefix. The estimated channel frequency response is used to terminate the sparse iteration of the Orthogonal Matching Pursuit (OMP) algorithm and realize the sparsity adaptive signal reconstruction. At the same time

in the atomic preselection stage

the Dice coefficient criterion is used instead of the inner product criterion as the correlation measurement criterion to achieve better estimation performance. The simulation results show that the algorithm has better performance than the traditional compressed sensing channel estimation algorithm

and can improve the system’s Normalized Mean Square Error (NMSE) and Bit Error Rate (BER) performance.

关键词

压缩感知信道估计稀疏度自适应

Keywords

compressed sensingchannel estimationsparsity adaptive

references

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