Abstract
This paper addresses the performance evaluation of a centralized cooperative spectrum sensing scheme under the effect of different impulsive noise environments; the generalized likelihood ratio test (GLRT). Impulsive noise (IN) is considered the most prevailing factor for the deterioration of any communication system performance. We propose weighting sample fusion schemes. The likelihood ratio test (LRT) closed-form expression is analyzed to obtain the optimal weighting solution according to the Neyman–Pearson lemma. Although LRT is an optimal solution, it is not possible in practical considerations due to its reliance on the knowledge of primary users and noise powers. Hence, three blind empirical maximum likelihood estimation (MLE) approximation weighting schemes are designed. The four weighting sample fusion schemes are proposed to control the combination of the samples received at the fusion center (FC) to confer robustness for the sample fusion against the influence of IN severe conditions. Different configurations of IN conditions and system parameters are conducted to study the influence of IN on the spectrum sensing performance. Simulation results show an interesting performance of our proposed schemes compared with the conventional GLRT method. The study also discusses the fact that IN is not considered as a severe problem when we take into account the appropriate mitigation method to reduce the IN effects.