基于Wishart矩阵特征值的频谱感知算法

杨雪梅; 何希; 徐家品

doi:10.11959/j.issn.1000-0801.2017255

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基于Wishart矩阵特征值的频谱感知算法

研究与开发 | 更新时间：2024-06-05

- 基于Wishart矩阵特征值的频谱感知算法
- Spectrum sensing algorithm based on the eigenvalue of Wishart random matrix
- 电信科学 2017年33卷第9期页码：69-75
- 作者机构：
  
  1. 四川大学锦江学院，四川眉山 620860
  2. 四川大学电子信息学院，四川成都 610065
- 作者简介：
  
  [ "杨雪梅（1983-），女，四川大学锦江学院讲师，四川省通信学会会员，主要研究方向为通信与信息系统、多媒体通信等。" ]
  [ "何希（1991-），女，四川大学电子信息学院硕士生，主要研究方向为通信与信息系统。" ]
  [ "徐家品（1957-），男，四川大学电子信息学院教授，主要研究方向为通信与信息系统。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2017255
  中图分类号： TN925
- 网络出版日期：2017-09，
  
  纸质出版日期：2017-09-20
- 稿件说明：
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杨雪梅, 何希, 徐家品. 基于Wishart矩阵特征值的频谱感知算法[J]. 电信科学, 2017,33(9):69-75.

Xuemei YANG, Xi HE, Jiapin XU. Spectrum sensing algorithm based on the eigenvalue of Wishart random matrix[J]. Telecommunications science, 2017, 33(9): 69-75.
杨雪梅, 何希, 徐家品. 基于Wishart矩阵特征值的频谱感知算法[J]. 电信科学, 2017,33(9):69-75. DOI： 10.11959/j.issn.1000-0801.2017255.

Xuemei YANG, Xi HE, Jiapin XU. Spectrum sensing algorithm based on the eigenvalue of Wishart random matrix[J]. Telecommunications science, 2017, 33(9): 69-75. DOI： 10.11959/j.issn.1000-0801.2017255.

摘要

为了提高频谱感知性能，克服经典算法的缺点，提出了一种新的基于Wishart随机矩阵理论的协作频谱感知算法。根据多个认知用户接收信号样本协方差矩阵特征值的对数分布特性，利用样本协方差矩阵最大特征值与几何平均特征值的比值，得到简单的判决阈值闭式表达式，实现频谱感知判决。该算法不需要知道主用户的任何先验信息，不受噪声不确定性的影响。仿真结果表明，所提算法在协作用户数少、信噪比低、采样点数极少的情况下，仍能获得较高的感知性能。该算法受虚警概率和极端值的影响较小，比同类算法有更好的检测性能。

Abstract

In order to improve the spectrum sensing performance and overcome the shortcomings of the classical algorithm

a new cooperative spectrum sensing algorithm based on Wishart random matrix theory was proposed.According to the logarithmic distribution characteristics of the sampled covariance matrix eigenvalues and using the ratio of maximum eigenvalue and geometric mean eigenvalue

a simple closed-form threshold expression could be obtained

and the spectrum sensing decision could be performed depend on the threshold.The simulation results show that the proposed algorithm can get better sensing performance even under the conditions of a few number of cooperative users

low signal to noise ratio and a few samples.It is less affected by false-alarm probability and the extreme values

and has better detection performance than similar algorithms.

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references

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