超宽带通信压缩感知信道估计与信号检测方法

张先玉; 刘郁林; 王开

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超宽带通信压缩感知信道估计与信号检测方法

更新时间：2025-07-09

- 超宽带通信压缩感知信道估计与信号检测方法
- Ultra Wide-Band Channel Estimation and Signal Detection Through Compressed Sensing
- 西安交通大学学报 2010年44卷第2期页码：88-91+124
- 作者机构：
  
  重庆通信学院DSP实验室,重庆,400035
- 作者简介：
- 基金信息：
  
  : 2009-07-27. 作者简介: 张先玉(1986-),男,硕士生;刘郁林(联系人),男,教授. 基金项目: 国家自然科学基金资助项目(60672157,60672158);重庆市自然科学基金资助项目(CSTC2009BB2028)
- DOI：
  中图分类号： TN911.5
- 网络首发：2010-02-10，
  
  纸质出版：2010
- 稿件说明：
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张先玉, 刘郁林, 王开. 超宽带通信压缩感知信道估计与信号检测方法[J]. 西安交通大学学报, 2010,44(2):88-91+124.

Ultra Wide-Band Channel Estimation and Signal Detection Through Compressed Sensing[J]. 2010, 44(2): 88-91+124.
张先玉, 刘郁林, 王开. 超宽带通信压缩感知信道估计与信号检测方法[J]. 西安交通大学学报, 2010,44(2):88-91+124. DOI：

Ultra Wide-Band Channel Estimation and Signal Detection Through Compressed Sensing[J]. 2010, 44(2): 88-91+124. DOI：

摘要

针对超宽带信号在采样速率过高时难以采样的问题

利用信号稀疏性提出一种基于压缩感知的信道估计和信号检测算法(CS算法).将信号重复送入随机滤波器后发送

对接收信号进行欠采样

利用调制信号、滤波器、信道的圆周卷积关系建立压缩感知的数学模型

从而可采用基追踪算法实现信道估计和信号检测.仿真结果表明

CS算法所需的采样数据量仅为最小二乘算法的1/3或更少

而在中等信噪比(15～25 dB)的情况下

估计性能可以提高约4.5 dB

且可以准确检测出原始信号.

Abstract

A novel algorithm(CS algorithm)for channel estimation and signal detection is proposed to resolve the problem of excessively high sampling rate of the ultra-wideband signals.The algorithm is based on the compressed sensing(CS)theory. The modulated signal is transmitted after random-filtering

and the received signal is sub-sampled. The mathematical model of CS is developed by cyclic convolution of the modulated signals

the random filter and the channel

so the basic pursuit algorithm can be utilized to perform the task of channel estimation and signal detection. Simulation results show that the number of the sampling data required by the proposed algorithm is only one-third or less of the number of the sampling data needed by the least squares algorithm

while the estimation performance is improved by 4.5 dB under the moderate signal-to-noise ratio(15～25 dB)condition

and that the original transmitted signals are correctly detected.

关键词

Keywords

references

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BAJWA W U, HAUPT J, RAZ G, et al. Toeplitz-structured compressed sensing matrices[C]∥Proceedings of IEEE SSP'07.Piscataway, NJ,USA: IEEE, 2007:294-298.

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DONOHO D. SparseLab [EB/OL]. [2009-04-20]. http:∥sparselab.stanford.edu./SparseLab_files/Download_files/SparseLab21-Core.zip.

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