电信科学 ›› 2015, Vol. 31 ›› Issue (11): 98-102.doi: 10.11959/j.issn.1000-0801.2015306

• 研究与开发 • 上一篇    下一篇

认知无线网络中基于特征向量的协方差盲检测方法

李映雪1,雷静2,钟士元1,黄春明1,黄超1   

  1. 1 国网江西省电力公司经济技术研究院 南昌 330043
    2 国网江西省电力公司 南昌 330077
  • 出版日期:2015-11-20 发布日期:2015-12-14
  • 基金资助:
    国家自然科学基金资助项目;国家自然科学基金资助项目

Covariance Blind Detection Method Based on Eigenvector in Cognitive Radio Network

Yingxue Li1,Jing Lei2,Shiyuan Zhong1,Chunming Huang1,Chao Huang1   

  1. 1 State Grid Jiangxi Economic Research Institute,Nanchang 330043,China
    2 State Grid Jiangxi Electric Power Company,Nanchang 330077,China
  • Online:2015-11-20 Published:2015-12-14
  • Supported by:
    The National Natural Science Foundation of China;The National Natural Science Foundation of China

摘要:

传统的协方差检测算法需要信道条件信息和多个认知节点参与。利用主用户信号与噪声信号协方差矩阵相关性的差异,提出了一种基于特征向量的协方差盲检测算法,推导出了所提算法的虚警概率和门限值的闭合表达式。该算法仅需要2个认知节点,不需要任何先验信息,对不确定噪声有很强的适应能力。仿真结果表明,所提算法性能优于其他协方差盲检测算法。

关键词: 认知无线电, 盲检测, 特征向量, 协方差矩阵, wishart分布, Haar分布

Abstract:

As the blind detection algorithm has the shortcoming that they need information about the channel and more than two cognitive users to detect the primary user,a new blind detection algorithm based on eigenvector using the difference of correlation between the primary user signal and noise signal covariance matrix was presented,and a closed expression was derived for the probability of false alarm and threshold.The proposed method overcomes the noise uncertainty problem only with two cognitive relays and performs well without information about the channel,primary user and noise.Numerical simulations show that the new detector performs better than other detectors.

Key words: cognitive radio, blind detection, wishart distribution, Haar distribution, eigenvector, covariance matrix

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