Robust Sequential Spectrum Sensing Based on the Goodness-of-Fit Test

This paper proposes a sequential spectrum sensing detector based on the Kolmogorov-Smirnov test. The Kolmogorov-Smirnov test is the most widely applied goodness-of-fit test for continuous data. It exploits the largest absolute difference between the empirical cumulative distribution function and the null cumulative distribution function to decide whether the observed samples are drawn from the assumed population or not. The proposed sequential detector can enhance the sensing agility without the prior knowledge of the statistics of the primary user's signal. Simulations confirm the efficiency and advantage of our proposed sequential detector over other existing sequential detectors. Specially, the proposed detector shows great performance improvement in the situation of non-Gaussian noise, where the unavailable distribution function of the mixture of noise and signal makes other detectors' failure.