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Nonparametric Cyclic Correlation Based Detection for Cognitive Radio Systems

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 non-parametric spectrum sensing method in both Gaussian and non-Gaussian noise environments.