Spectrum sensing method based on the likelihood ratio goodness of fit test under noise uncertainty

In cognitive radio, spectrum sensing is one of the most important tasks. In this article, a blind spectrum sensing method based on goodness-of-fit (GoF) test using likelihood ratio (LLR) is studied. In the proposed method, a chi-square distribution is used for GoF testing. The performance of the method is evaluated through Monte Carlo simulations. It is shown that the proposed spectrum sensing method outperforms the GoF test using Anderson Darling (AD) and the conventional energy detection (ED) in case of a limited number of received samples and low signal to noise ratio (SNR). We also evaluate the proposed method in case of a non-Gaussian noise and in case of noise uncertainty. It is shown that the GoF based spectrum sensing methods are less sensitive to both impairments, than the conventional ED. Finally, this paper investigates the influence of the number of samples on the detection performance. The performance difference between the GoF based sensing (LLR and AD) and ED increases with decreasing number of samples for sensing, which makes the proposed method very effective in CR systems with short sensing periods.