Research Article
Wideband Spectrum Detection Based On Compressed Sensing in Cooperative Cognitive Radio Networks
@INPROCEEDINGS{10.1109/ChinaCom.2013.6694671, author={Chengyu Liu and Aixiang Qi and Pu Zhang and Linjie Bu and Keping Long}, title={Wideband Spectrum Detection Based On Compressed Sensing in Cooperative Cognitive Radio Networks}, proceedings={8th International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2013}, month={11}, keywords={cognitive radio compressed wideband sensing iterative scheme cooperative sensing}, doi={10.1109/ChinaCom.2013.6694671} }- Chengyu Liu
Aixiang Qi
Pu Zhang
Linjie Bu
Keping Long
Year: 2013
Wideband Spectrum Detection Based On Compressed Sensing in Cooperative Cognitive Radio Networks
CHINACOM
IEEE
DOI: 10.1109/ChinaCom.2013.6694671
Abstract
Compressed Sensing (CS) is a promising theory that has the power to reconstruct a certain signal from far fewer samples than conventional methods. Wideband detection is a challenge in Cognitive Radio (CR) networks because of its requirement for high sampling rate. Recent research shows that CS theory can be well applied to wideband detection with much lower sampling rates. In this paper, we propose a novel iterative algorithm for the noise-involved wideband detection in CR networks. In the proposed scheme, based on the different current detection results, the weights of the Weighted l 1 Minimization (WP1) are adjusted adaptively with the aim of improving the detection result in the next iteration. We also utilize M-out-of-N method in the fusion center to improve our detection result. Finally we introduce a metric which provides a better measurement for the detection performance. Simulation results prove our algorithm to be effective with lower sampling rate.