Performance Comparison of the Neyman-pearson Fusion Rule with Counting Rules for Spectrum Sensing in Cognitive Radio

Distributed spectrum sensing (DSS) is of great importance in Cognitive Radio, especially under fading or shadowing effects. In order to evaluate the performance of a distributed system, it is commonly compared with the centralized system as an upper performance bound. Now the question is whether or not one can obtain a distributed strategy serving as an upper bound to benchmark any distributed strategy, tighter than that of the centralized scheme. Here, we suggest employing the Neyman-Pearson (NP) fusion rule to achieve an upper bound. Furthermore, the analysis of a randomized fusion rule has been provided, which is a long-existing problem in this field. For this purpose, theoretical analysis on the performance of the NP fusion rule is carried out. Next, we compare the traditional fusion rules with the proposed bound and observe in which special cases of the probability of false alarm at the fusion center these counting rules are optimum. We further study the effects of varying the number of participating sensors on fusion performance in detail. Remarkably, simulation results in some applicable examples illustrate the significant cooperative gain achieved by the proposed NP fusion rule. Keywords– Cognitive radio, distributed spectrum sensing, neyman-pearson criterion, decision fusion rule, data fusion