  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.
