An Improved Wideband Compressed Spectrum Sensing Scheme Based on Binomial Distribution

MA Bin; WANG Hong-ming; XIE Xian-zhong

doi:10.3969/j.issn.0372-2112.2020.02.003

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An Improved Wideband Compressed Spectrum Sensing Scheme Based on Binomial Distribution

更新时间：2025-07-16

- An Improved Wideband Compressed Spectrum Sensing Scheme Based on Binomial Distribution
- Acta Electronica Sinica Vol. 48, Issue 2, Pages: 243-248(2020)
- 作者机构：
  
  1. 重庆邮电大学移动通信技术重庆市重点实验室,重庆,400065
  2. 重庆邮电大学通信与信息工程学院,重庆,400065
  3. 重庆邮电大学移动通信技术重庆市重点实验室,重庆,400065
  4. 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：10.3969/j.issn.0372-2112.2020.02.003
  CLC： TN929.5
- Published Online：25 February 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
MA Bin, WANG Hong-ming, XIE Xian-zhong. An Improved Wideband Compressed Spectrum Sensing Scheme Based on Binomial Distribution[J]. Acta Electronica Sinica, 2020, 48(2): 243-248.
DOI：

MA Bin, WANG Hong-ming, XIE Xian-zhong. An Improved Wideband Compressed Spectrum Sensing Scheme Based on Binomial Distribution[J]. Acta Electronica Sinica, 2020, 48(2): 243-248. DOI： 10.3969/j.issn.0372-2112.2020.02.003.

摘要

宽带压缩频谱检测存在依赖稀疏度先验信息和信号重构时延较高的问题.因此，本文提出了一种高效可靠的宽带压缩频谱检测方案.首先，推导出了基于二项分布精确置信区间改进的稀疏度估计模型.其次，利用稀疏度估计上下界改进了稀疏度自适应匹配追踪算法.最后，提出了一种宽带压缩频谱检测方案.仿真结果表明，本文所提出方法可以同时精确的估计信号稀疏度的上下界，提高了频谱检测的效率和可靠性，加快了算法的收敛速度.

Abstract

Wideband compressed spectrum sensing has the problem of relying on sparsity prior information and high signal reconstruction delay. Therefore

this paper proposes an efficient and reliable wideband compressed spectrum sensing scheme. Firstly

the sparsity estimation model based on the improved confidence interval of binomial distribution is derived. Secondly

using the sparsity estimation upper and lower bounds improves the sparsity adaptive matching pursuit algorithm. Finally

a wideband compressed spectrum sensing scheme is proposed. The simulation results show that the proposed method can accurately estimate the upper and lower bounds of signal sparsity at the same time

improve the efficiency and reliability of spectrum sensing

and accelerate the convergence speed of the algorithm.

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