Fast and Robust Spectrum Sensing via Kolmogorov-Smirnov Test

Abstract

A new approach to spectrum sensing in cognitive radio systems based on the Kolmogorov-Smirnov (K-S) test is proposed. The K-S test is a non-parametric method to measure the goodness of fit. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic obtained from the received signal, and comparing it with the ECDF of the channel noise samples. A sequential version of the K-S-based spectrum sensing technique is also proposed. Extensive simulation results demonstrate that compared with the existing spectrum detection methods, such as the energy detector and the eigenvalue-based detector, the proposed K-S detectors offer superior detection performance and faster detection, and is more robust to channel uncertainty and non-Gaussian noise.