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

Author

Feng Li ; Zongben Xu

Author_Institution

Dept. of Inf. & Commun. Eng., Xian Jiaotong Univ., Xian, China

Volume

21

Issue

5

fYear

2014

fDate

May-14

Firstpage

586

Lastpage

590

Abstract

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 l₁ 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.

Keywords

belief networks; cognitive radio; cooperative communication; learning (artificial intelligence); message passing; network theory (graphs); radio spectrum management; signal representation; spectral analysis; variational techniques; PSD map; VMP algorithm; cooperative spectrum sensing; factor graph; penalization; power spectral density; signal model representation; sparse Bayesian hierarchical prior modeling; sparse Bayesian learning; variational message passing; wideband cognitive radio network; Bayes methods; Cognitive radio; Estimation; Niobium; Sensors; Signal processing algorithms; Vectors; Bayesian hierarchical model; cognitive radio; compressive sensing; cooperative spectrum sensing; sparse estimation; variational message passing;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2014.2311902

Filename

6766728