A Comparative Study of Different Entropies for Spectrum Sensing Techniques

In this paper, we propose entropy-based spectrum sensing schemes to detect the existence of a primary user in Cognitive Radio (CR). To support this proposal, we have studied four types of entropies [Approximate Entropy (ApEn), Bispectral Entropy (BispEn), Sample Entropy (SamEn) and Rényi Entropy (RenyiEn)] and their applications for spectrum sensing. The reason for investigating these entropies comes from the fact that different types of entropies have different characteristics which make them more or less suitable for specific applications. Monte Carlo simulations were executed to find out how suitable these measures are for CR. Averaged value curves, boxplots and Analysis of Variance (ANOVA) tests document the performance of these types of entropies. The results show that BispEn outperformed the other three entropy measures. The ANOVA test shows that BispEn can sense modulated signals when the (SNR) is as low as −15 dB. This is at least a 5 dB improvement compared to the other entropies studied in this paper.