IEICE TRANSACTIONS on Fundamentals
Cooperative Bayesian Compressed Spectrum Sensing for Correlated Wideband Signals
Summary :
We propose a cooperative compressed spectrum sensing scheme for correlated signals in wideband cognitive radio networks. In order to design a reconstruction algorithm which accurately recover the wideband signals from the compressed samples in low SNR (Signal-to-Noise Ratio) environments, we consider the multiple measurement vector model exploiting a sequence of input signals and propose a cooperative sparse Bayesian learning algorithm which models the temporal correlation of the input signals. Simulation results show that the proposed scheme outperforms existing compressed sensing algorithms for low SNRs.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E97-A No.6 pp.1434-1438
- Publication Date
- 2014/06/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E97.A.1434
- Type of Manuscript
- LETTER
- Category
- Communication Theory and Signals
Authors
Honggyu JUNG
Soongsil University
Kwang-Yul KIM
Soongsil University
Yoan SHIN
Soongsil University
Keyword
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Honggyu JUNG, Kwang-Yul KIM, Yoan SHIN, "Cooperative Bayesian Compressed Spectrum Sensing for Correlated Wideband Signals" in IEICE TRANSACTIONS on Fundamentals,
vol. E97-A, no. 6, pp. 1434-1438, June 2014, doi: 10.1587/transfun.E97.A.1434.
Abstract: We propose a cooperative compressed spectrum sensing scheme for correlated signals in wideband cognitive radio networks. In order to design a reconstruction algorithm which accurately recover the wideband signals from the compressed samples in low SNR (Signal-to-Noise Ratio) environments, we consider the multiple measurement vector model exploiting a sequence of input signals and propose a cooperative sparse Bayesian learning algorithm which models the temporal correlation of the input signals. Simulation results show that the proposed scheme outperforms existing compressed sensing algorithms for low SNRs.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E97.A.1434/_p
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@ARTICLE{e97-a_6_1434,
author={Honggyu JUNG, Kwang-Yul KIM, Yoan SHIN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Cooperative Bayesian Compressed Spectrum Sensing for Correlated Wideband Signals},
year={2014},
volume={E97-A},
number={6},
pages={1434-1438},
abstract={We propose a cooperative compressed spectrum sensing scheme for correlated signals in wideband cognitive radio networks. In order to design a reconstruction algorithm which accurately recover the wideband signals from the compressed samples in low SNR (Signal-to-Noise Ratio) environments, we consider the multiple measurement vector model exploiting a sequence of input signals and propose a cooperative sparse Bayesian learning algorithm which models the temporal correlation of the input signals. Simulation results show that the proposed scheme outperforms existing compressed sensing algorithms for low SNRs.},
keywords={},
doi={10.1587/transfun.E97.A.1434},
ISSN={1745-1337},
month={June},}
Copy
TY - JOUR
TI - Cooperative Bayesian Compressed Spectrum Sensing for Correlated Wideband Signals
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1434
EP - 1438
AU - Honggyu JUNG
AU - Kwang-Yul KIM
AU - Yoan SHIN
PY - 2014
DO - 10.1587/transfun.E97.A.1434
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E97-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2014
AB - We propose a cooperative compressed spectrum sensing scheme for correlated signals in wideband cognitive radio networks. In order to design a reconstruction algorithm which accurately recover the wideband signals from the compressed samples in low SNR (Signal-to-Noise Ratio) environments, we consider the multiple measurement vector model exploiting a sequence of input signals and propose a cooperative sparse Bayesian learning algorithm which models the temporal correlation of the input signals. Simulation results show that the proposed scheme outperforms existing compressed sensing algorithms for low SNRs.
ER -
English
