Numerical Analysis of Histogram-based Estimation Techniques for Entropy-based Spectrum Sensing
Guillermo Prieto, Ángel G. Andrade, Daniela M. Martínez · IETE Technical Review · 2019
Due to its robustness to noise uncertainty, Entropy-Based Spectrum Detection (EnBD) has been proposed to sense primary transmissions in cognitive radio networks. Based on the histogram method, the number of bins must be optimal to accurately estimate the entropy of the samples received. In this work, the performance of the EnBD with respect to several rules for determining the number of bins in the histogram is evaluated. And, it is demonstrated that detection performance is different for each of the aforementioned rules due to the probability distribution of the primary signal.