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.

本文讨论了在不同脉冲噪声环境影响下的集中式协作频谱感知方案的性能评估。广义似然比检验（GLRT）。脉冲噪声（IN）被认为是导致任何通信系统性能下降的最主要因素。我们提出加权样本融合方案。根据Neyman–Pearson引理，对似然比检验（LRT）闭式表达式进行了分析，以获得最佳加权解决方案。尽管LRT是最佳解决方案，但由于它依赖于主要用户和噪声功率的知识，因此在实际考虑中是不可能的。因此，设计了三种盲经验最大似然估计（MLE）近似加权方案。提出了四种加权样本融合方案，以控制在融合中心（FC）接收的样本的组合，以针对IN严酷条件的影响赋予样本融合鲁棒性。进行IN条件和系统参数的不同配置以研究IN对频谱感测性能的影响。仿真结果表明，与传统的GLRT方法相比，我们提出的方案具有有趣的性能。该研究还讨论了以下事实：当我们考虑采用适当的缓解方法以减少IN影响时，IN不会被视为严重问题。进行IN条件和系统参数的不同配置，以研究IN对频谱感测性能的影响。仿真结果表明，与传统的GLRT方法相比，我们提出的方案具有有趣的性能。该研究还讨论了以下事实：当我们考虑采用适当的缓解方法以减少IN影响时，IN不会被视为严重问题。进行IN条件和系统参数的不同配置，以研究IN对频谱感测性能的影响。仿真结果表明，与传统的GLRT方法相比，我们提出的方案具有有趣的性能。该研究还讨论了以下事实：当我们考虑采用适当的缓解方法以减少IN影响时，IN不会被视为严重问题。