4th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

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
Shen Da1, Gan Xiaoying1,*, Chen Hsiao-Hwa2, Qian Liang1, Xu Miao1
  • 1: Dept. of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
  • 2: Dept. of Engineering Science, National Cheng Kung University, Tainan City, Taiwan
*Contact email: ganxiaoying@sjtu.edu.cn

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.