Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)

Adaptive Compressed Wideband Spectrum Detection Based on Optimized Measurement Matrix

Authors
Yongle Yu, Zhengbao Zhang, Chao Guo, Gaofeng Fan
Corresponding Author
Yongle Yu
Available Online May 2017.
DOI
https://doi.org/10.2991/icmeit-17.2017.117How to use a DOI?
Keywords
cognitive radio, compressed sensing, spectrum detection, measurement matrix.
Abstract
The emergence of compressed sensing technology brought a revolutionary opportunity to wideband spectrum sensing, which could be used to reduce the sampling rate and reconstruct the sparse spectrum. In combination of measurement matrix optimization and adaptive process of compressive sampling, a modified adaptive compressed wideband spectrum sensing algorithm is proposed. In this paper, the column vector autocorrelation of the observation matrix was reduced, and the impact of optimized matrix on reconstruction algorithm was analyzed. The simulation results show that the proposed algorithm has a lower mean square error (MSE) than that of the traditional algorithm, and the detection probability is higher at the same number of observations.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
Part of series
Advances in Computer Science Research
Publication Date
May 2017
ISBN
978-94-6252-338-8
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-17.2017.117How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yongle Yu
AU  - Zhengbao Zhang
AU  - Chao Guo
AU  - Gaofeng Fan
PY  - 2017/05
DA  - 2017/05
TI  - Adaptive Compressed Wideband Spectrum Detection Based on Optimized Measurement Matrix
BT  - 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-17.2017.117
DO  - https://doi.org/10.2991/icmeit-17.2017.117
ID  - Yu2017/05
ER  -