Adaptive Compressed Wideband Spectrum Detection Based on Optimized Measurement Matrix

DOI

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
PB  - Atlantis Press
SP  - 637
EP  - 642
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-17.2017.117
DO  - 10.2991/icmeit-17.2017.117
ID  - Yu2017/05
ER  -