A compressive sensing recovery algorithm based on sparse Bayesian learning for block sparse signal

Wang Wei; Jia Min; Guo Qing

doi:10.1109/WPMC.2014.7014878

A compressive sensing recovery algorithm based on sparse Bayesian learning for block sparse signal

Wang Wei
, Jia Min
, Guo Qing^*

^*Corresponding author for this work

School of Electronics and Information Engineering

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

Compressive sensing offers a new wideband spectrum sensing scheme in cognitive radio. In this paper, a sparse signal recovery algorithm based on sparse Bayesian learning (SBL) framework is proposed. By exploiting intrablock correlation in a block sparse model and using Expectation-Maximization (EM) method, this algorithm achieves superior performance. The results of experiments show that this algorithm is robust to noise and has better performance than other algorithms in signal recovery. Then we apply it to wideband spectrum sensing, we find that proposed algorithm not only guarantees accurate signal estimation, but also obtains higher correct detection probability.

Original language	English
Title of host publication	2014 International Symposium on Wireless Personal Multimedia Communications, WPMC 2014
Publisher	IEEE Computer Society
Pages	547-551
Number of pages	5
ISBN (Electronic)	9789860334074
DOIs	https://doi.org/10.1109/WPMC.2014.7014878
State	Published - 19 Jan 2015
Event	2014 International Symposium on Wireless Personal Multimedia Communications, WPMC 2014 - Sydney, Australia Duration: 7 Sep 2014 → 10 Sep 2014

Publication series

Name	International Symposium on Wireless Personal Multimedia Communications, WPMC
Volume	2015-January
ISSN (Print)	1347-6890

Conference

Conference	2014 International Symposium on Wireless Personal Multimedia Communications, WPMC 2014
Country/Territory	Australia
City	Sydney
Period	7/09/14 → 10/09/14

Keywords

Compressive sensing
intra-block correlation
signal recovery algorithm
sparse Bayesian learning (SBL)
wideband spectrum sensing

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10.1109/WPMC.2014.7014878

Cite this

Wei, W., Min, J., & Qing, G. (2015). A compressive sensing recovery algorithm based on sparse Bayesian learning for block sparse signal. In 2014 International Symposium on Wireless Personal Multimedia Communications, WPMC 2014 (pp. 547-551). Article 7014878 (International Symposium on Wireless Personal Multimedia Communications, WPMC; Vol. 2015-January). IEEE Computer Society. https://doi.org/10.1109/WPMC.2014.7014878

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title = "A compressive sensing recovery algorithm based on sparse Bayesian learning for block sparse signal",

abstract = "Compressive sensing offers a new wideband spectrum sensing scheme in cognitive radio. In this paper, a sparse signal recovery algorithm based on sparse Bayesian learning (SBL) framework is proposed. By exploiting intrablock correlation in a block sparse model and using Expectation-Maximization (EM) method, this algorithm achieves superior performance. The results of experiments show that this algorithm is robust to noise and has better performance than other algorithms in signal recovery. Then we apply it to wideband spectrum sensing, we find that proposed algorithm not only guarantees accurate signal estimation, but also obtains higher correct detection probability.",

keywords = "Compressive sensing, intra-block correlation, signal recovery algorithm, sparse Bayesian learning (SBL), wideband spectrum sensing",

author = "Wang Wei and Jia Min and Guo Qing",

note = "Publisher Copyright: {\textcopyright} 2014 National Institute of Information and Communicatio.; 2014 International Symposium on Wireless Personal Multimedia Communications, WPMC 2014 ; Conference date: 07-09-2014 Through 10-09-2014",

year = "2015",

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day = "19",

doi = "10.1109/WPMC.2014.7014878",

language = "英语",

series = "International Symposium on Wireless Personal Multimedia Communications, WPMC",

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Wei, W, Min, J & Qing, G 2015, A compressive sensing recovery algorithm based on sparse Bayesian learning for block sparse signal. in 2014 International Symposium on Wireless Personal Multimedia Communications, WPMC 2014., 7014878, International Symposium on Wireless Personal Multimedia Communications, WPMC, vol. 2015-January, IEEE Computer Society, pp. 547-551, 2014 International Symposium on Wireless Personal Multimedia Communications, WPMC 2014, Sydney, Australia, 7/09/14. https://doi.org/10.1109/WPMC.2014.7014878

A compressive sensing recovery algorithm based on sparse Bayesian learning for block sparse signal. / Wei, Wang; Min, Jia ; Qing, Guo.
2014 International Symposium on Wireless Personal Multimedia Communications, WPMC 2014. IEEE Computer Society, 2015. p. 547-551 7014878 (International Symposium on Wireless Personal Multimedia Communications, WPMC; Vol. 2015-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Compressive sensing offers a new wideband spectrum sensing scheme in cognitive radio. In this paper, a sparse signal recovery algorithm based on sparse Bayesian learning (SBL) framework is proposed. By exploiting intrablock correlation in a block sparse model and using Expectation-Maximization (EM) method, this algorithm achieves superior performance. The results of experiments show that this algorithm is robust to noise and has better performance than other algorithms in signal recovery. Then we apply it to wideband spectrum sensing, we find that proposed algorithm not only guarantees accurate signal estimation, but also obtains higher correct detection probability.

AB - Compressive sensing offers a new wideband spectrum sensing scheme in cognitive radio. In this paper, a sparse signal recovery algorithm based on sparse Bayesian learning (SBL) framework is proposed. By exploiting intrablock correlation in a block sparse model and using Expectation-Maximization (EM) method, this algorithm achieves superior performance. The results of experiments show that this algorithm is robust to noise and has better performance than other algorithms in signal recovery. Then we apply it to wideband spectrum sensing, we find that proposed algorithm not only guarantees accurate signal estimation, but also obtains higher correct detection probability.

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Wei W, Min J , Qing G. A compressive sensing recovery algorithm based on sparse Bayesian learning for block sparse signal. In 2014 International Symposium on Wireless Personal Multimedia Communications, WPMC 2014. IEEE Computer Society. 2015. p. 547-551. 7014878. (International Symposium on Wireless Personal Multimedia Communications, WPMC). doi: 10.1109/WPMC.2014.7014878

A compressive sensing recovery algorithm based on sparse Bayesian learning for block sparse signal

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