Sparse Bayesian Hierarchical Prior Modeling Based Cooperative Spectrum Sensing in Wideband Cognitive Radio Networks

被引：11
|
作者：
Li, Feng [1 ]
Xu, Zongben [2 ]
机构：
[1] Xi An Jiao Tong Univ, Dept Informat & Commun Engn, Xian, Shaanxi, Peoples R China
[2] Xi An Jiao Tong Univ, Inst Informat & Syst Sci, Xian, Shaanxi, Peoples R China
来源：
IEEE SIGNAL PROCESSING LETTERS | 2014年 / 21卷 / 05期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Bayesian hierarchical model; cognitive radio; compressive sensing; cooperative spectrum sensing; sparse estimation; variational message passing;
D O I：
10.1109/LSP.2014.2311902
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This letter proposes a new method for cooperative spectrum sensing by exploiting sparsity. The novel scheme uses the theory of Bayesian hierarchical prior modeling in the framework of sparse Bayesian learning. This model has sparsity-inducing penalization terms leading to sparser solutions compared with typically norm based ones. Based on the factor graph that represents the signal model of the hierarchical prior models, the variational message passing (VMP) algorithm is implemented to estimate the power spectral density (PSD) map.
引用
收藏
页码：586 / 590
页数：5
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