Theoretical Analysis of Cyclic Frequency Domain Noise and Feature Detection for Cognitive Radio Systems

In cognitive radio systems, cyclostationary feature detection plays an important role in spectrum sensing, especially in low SNR cases. To configure the detection threshold under a certain noise level and a pre-set miss detection probability Pf, it's important to derive the theoretical distribution of the observation variable. In this paper, noise distribution in cyclic frequency domain has been studied and Generalized Extreme Value (GEV) distribution is found to be a precise match. Maximum likelihood estimation is applied to estimate the parameters of GEV. Monte Carlo simulation has been carried out to show that the simulated ROC curve is coincided with the theoretical ROC curve, which proves the efficiency of the theoretical distribution model.

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