Cooperative Spectrum Sensing Algorithm Based on Double Eigenvalue Limiting Distribution

Zhijin zhao; Weikang Hu; Haiquan Wang

doi:10.3969/j.issn.1000-0801.2014.04.012

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Cooperative Spectrum Sensing Algorithm Based on Double Eigenvalue Limiting Distribution

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

- Cooperative Spectrum Sensing Algorithm Based on Double Eigenvalue Limiting Distribution
- Telecommunications Science Vol. 30, Issue 4, Pages: 82-87(2014)
- 作者机构：
  
  1. 杭州电子科技大学通信工程学院杭州 310018
  2. 中国电子科技集团第36研究所通信系统信息控制技术国家级重点实验室嘉兴 314001
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1000-0801.2014.04.012
  CLC：
- Published Online：2014-04，
  
  Published：20 April 2014
- 稿件说明：
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Zhijin zhao, Weikang Hu, Haiquan Wang. Cooperative Spectrum Sensing Algorithm Based on Double Eigenvalue Limiting Distribution[J]. Telecommunications science, 2014, 30(4): 82-87.
DOI：

Zhijin zhao, Weikang Hu, Haiquan Wang. Cooperative Spectrum Sensing Algorithm Based on Double Eigenvalue Limiting Distribution[J]. Telecommunications science, 2014, 30(4): 82-87. DOI： 10.3969/j.issn.1000-0801.2014.04.012.

摘要

以最大最小特征值之差作为检测统计量，从提高算法判决门限估计精度出发，利用双特征值极限分布，用双特征值估计判决门限，提出了基于双特征值极限分布的频谱感知算法。相比于单特征值门限方法，理论上证明了用双特征值极限分布估计的门限小于或等于用单特征值极限分布估计的门限，提高了算法检测性能。仿真结果表明，该算法不受噪声不确定性影响，不需要主用户信息，并且在低虚警概率、采样次数相对少的情况下，检测性能优于最大最小特征值之差频谱感知算法。

Abstract

A spectrum sensing algorithm using double eigenvalue limiting（DEL）distributions was presented. The difference between the maximum and the minimum eigenvalue was exploited as the test statistic. The double eigenvalue were used to estimate a threshold based on the double eigenvalue limiting distributions to heighten an accuracy of the threshold. Compared with the algorithm of single eigenvalue

the theoretical analyses demonstrate that the threshold estimated by DEL was less than its threshold and the performance of DEL was improved. In addition

simulations results show the proposed algorithm needs neither the prior acknowledge of the signal transmitted from primary user

nor the noise power in advance

and gains better performance against the short of sampling number than the difference between the maximum and the minimum eigenvalue（DMM）algorithm.

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references

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