通信学报 ›› 2012, Vol. 33 ›› Issue (Z2): 118-124.doi: 10.3969/j.issn.1000-436x.2012.z2.015

• 学术论文 • 上一篇    下一篇

认知无线网络中基于Cholesky分解的统计协方差频谱检测算法

李映雪1,沈树群1,胡浪涛2,王秋才2   

  1. 1 北京邮电大学 电子工程学院,北京100876
    2 北京邮电大学 信息与通信工程学院,北京100876
  • 出版日期:2012-11-25 发布日期:2017-08-03
  • 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

Statistical covariance blind detection algorithm based on cholesky factorization in cognitive radio network

Ying-xue LI1,Shu-qun SHEN1,Lang-tao HU2,Qiu-cai WANG2   

  1. 1 School of Electronic Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China
    2 School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Online:2012-11-25 Published:2017-08-03
  • Supported by:
    The National Natural Science Foundation of China;The National Natural Science Foundation of China

摘要:

针对认知无线网络中协方差检测算法均通过渐进方法得到性能参数的缺点,提出了改进的cholesky的协方差盲检测算法,利用RMT(random matrix theory)理论,推导了非渐进条件下该算法性能参数的数学表达式。所提算法无需PU信号的先验信息和信道条件信息,对不确定噪声具有很强的适应能力。理论分析和仿真证明,性能参数表达式正确,所提算法相对于其他协方差盲检测算法,性能有了一定的提升。

关键词: 协方差矩阵, 认知无线网络, Wishart分布, cholesky分解, 盲检测

Abstract:

As the blind covariance detection algorithm has the shortcoming that the performance parameters are obtained using non-asymptotic method,a new blind detection algorithm was presented using cholesky factorization.Utilizing random matrix theory,the performance parameters was derived using non-asymptotic method.The proposed method overcomes the noise uncertainty problem and performs well without information about the channel,primary user and noise.Numerical simulation results demonstrate that the performance parameters expressions are correct and the new detector outperforms the other blind covariance detectors.

Key words: covariance matrix, cognitive radio network, wishart distribution, choleskyfactorization, blind detection

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