Research Article
Significant cycle frequency based feature detection for cognitive radio systems
@INPROCEEDINGS{10.1109/CROWNCOM.2009.5189106, author={Shen Da and Gan Xiaoying and Chen Hsiao-Hwa and Qian Liang and Xu Miao}, title={Significant cycle frequency based feature detection for cognitive radio systems}, proceedings={4th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications}, publisher={IEEE}, proceedings_a={CROWNCOM}, year={2009}, month={8}, keywords={Cognitive radio cycle frequency cyclostationary detection energy detection}, doi={10.1109/CROWNCOM.2009.5189106} }- Shen Da
Gan Xiaoying
Chen Hsiao-Hwa
Qian Liang
Xu Miao
Year: 2009
Significant cycle frequency based feature detection for cognitive radio systems
CROWNCOM
IEEE
DOI: 10.1109/CROWNCOM.2009.5189106
Abstract
In cognitive radio systems, one of the main requirements is to detect the presence of the primary users' transmission, especially in weak signal cases. Cyclostationary detection is always used to solve weak signal detection, however, the computational complexity prevents it from wide usage. In this paper, a significant cycle frequency based feature detection algorithm has been proposed, in which only cycle frequency with significant cyclic cumulant is considered for a certain modulation mode. The proposed algorithm greatly reduces the computation complexity for cyclic feature detection. Simulation results show that the proposed algorithm has a remarkable performance gain than energy detection when supporting fast detection with low computational complexity.
