3rd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

Research Article

Nonparametric Cyclic Correlation Based Detection for Cognitive Radio Systems

  • @INPROCEEDINGS{10.1109/CROWNCOM.2008.4562527,
        author={Jarmo Lunden and Saleem Kassam and Visa Koivunen},
        title={Nonparametric Cyclic Correlation Based Detection for Cognitive Radio Systems},
        proceedings={3rd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications},
        publisher={IEEE},
        proceedings_a={CROWNCOM},
        year={2008},
        month={7},
        keywords={},
        doi={10.1109/CROWNCOM.2008.4562527}
    }
    
  • Jarmo Lunden
    Saleem Kassam
    Visa Koivunen
    Year: 2008
    Nonparametric Cyclic Correlation Based Detection for Cognitive Radio Systems
    CROWNCOM
    IEEE
    DOI: 10.1109/CROWNCOM.2008.4562527
Jarmo Lunden1,*, Saleem Kassam2,*, Visa Koivunen1,*
  • 1: SMARAD CoE, Signal Processing Laboratory Helsinki University of Technology, Finland
  • 2: Department of Electrical and Systems Engineering University of Pennsylvania, Philadelphia, PA, USA
*Contact email: jrlunden@wooster.hut.fi, kassam@ee.upenn.edu, visa@wooster.hut.fi

Abstract

In this paper a nonparametric cyclic correlation estimator based on complex generalization of sign function is proposed. Theoretical justification for detecting cyclostationary signals is provided. Asymptotic distribution of the estimator under null hypothesis is established. Constant false alarm rate (CFAR) tests based on estimated sign cyclic correlation are derived for single-user and collaborative spectrum sensing. Simulation experiments comparing the proposed method with cyclostationarity based spectrum sensing methods employing the classical cyclic correlation estimator are performed. Nonparametric statistics provide additional robustness when noise statistics are non-Gaussian or not fully known. Simulations demonstrate the reliable performance and robustness of the proposed nonparametric spectrum sensing method in both Gaussian and non-Gaussian noise environments.