基于特征值极限分布的合作频谱感知算法

弥寅; 卢光跃

doi:10.11959/j.issn.1000-436x.2015010

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基于特征值极限分布的合作频谱感知算法

学术论文 | 更新时间：2024-06-05

- 基于特征值极限分布的合作频谱感知算法
- Cooperative spectrum sensing algorithm based on limiting eigenvalue distribution
- 通信学报 2015年36卷第1期页码：84-89
- 作者机构：
  
  西安邮电大学无线网络安全技术国家工程实验室，陕西西安 710121
- 作者简介：
  
  [ "弥寅（1986-），男，甘肃庆阳人，西安邮电大学助理工程师，主要研究方向为认知无线电频谱感知技术。" ]
  [ "卢光跃（1971-），男，河南南阳人，博士，西安邮电大学教授、博士生导师，主要研究方向为现代移动通信中的信号处理等。" ]
- 基金信息：
  
  国家科技重大专项基金资助项目(2012ZX03001025-004);国家自然科学基金资助项目(61271276);国家自然科学基金资助项目(61301091);陕西省自然科学基金资助项目(2012JQ8011);陕西省自然科学基金资助项目(2010JQ80241);陕西省自然科学基金资助项目(2014JM8299);陕西省教育厅专项科研计划基金资助项目(14JK1681)
- DOI：10.11959/j.issn.1000-436x.2015010
  中图分类号： TN92
- 网络出版日期：2015-01，
  
  纸质出版日期：2015-01-25
- 稿件说明：
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弥寅, 卢光跃. 基于特征值极限分布的合作频谱感知算法[J]. 通信学报, 2015,36(1):84-89.

Yin MI, Guang-yue LU. Cooperative spectrum sensing algorithm based on limiting eigenvalue distribution[J]. Journal on communications, 2015, 36(1): 84-89.
弥寅, 卢光跃. 基于特征值极限分布的合作频谱感知算法[J]. 通信学报, 2015,36(1):84-89. DOI： 10.11959/j.issn.1000-436x.2015010.

Yin MI, Guang-yue LU. Cooperative spectrum sensing algorithm based on limiting eigenvalue distribution[J]. Journal on communications, 2015, 36(1): 84-89. DOI： 10.11959/j.issn.1000-436x.2015010.

摘要

采用最新的随机矩阵理论，对多个认知用户接收信号采样协方差矩阵的最小特征值的极限分布进行了分析，提出了一种改进的最大最小特征值合作感知和门限判决方法。该算法不需预知授权用户信号的先验知识，且能有效克服噪声不确定度的影响。与现有算法相比，在给定虚警概率时，仿真结果显示该算法判决门限更低、检测概率更高；而且在认知用户和采样数较少时，也能获得很好的检测性能。

Abstract

A novel maximum-minimum eigenvalue (NMME) cooperative spectrum sensing algorithm and threshold decision rule are proposed via analyzing minimum eigenvalue limiting distribution of the covariance matrix of the received signals from multiple cognitive users (CU) by means of latest random matrix theory (RMT).The proposed scheme could not need the prior knowledge of the signal transmitted from primary user (PU) and could effectively overcome the noise uncertainty.At a given probability of false alarm (P<sub>fa</sub>)

simulation results show that the proposed scheme can get lower decision threshold and higher probability of detection (P<sub>d</sub>) compared with the original algorithm

and it can also get better detection performance with fewer CU and smaller sample numbers.

关键词

Keywords

references

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