Comparative Analysis of Information Fusion Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks

In Cognitive Radio Networks (CRN), Cooperative Spectrum Sensing (CSS) is used to improve performance of spectrum sensing techniques used for detection of licensed (Primary) user’s signal. In CSS, the spectrum sensing information from multiple unlicensed (Secondary) users are combined to take final decision about presence of primary signal. The mixing techniques used to generate final decision about presence of PU’s signal are also called as Fusion techniques / rules. The fusion techniques are further classified as data fusion and decision fusion techniques. In data fusion technique all the secondary users (SUs) share their raw information of spectrum detection like detected energy or other statistical information, while in decision fusion technique all the SUs take their local decisions and share the decision by sending ‘0’ or ‘1’ corresponding to absence and presence of PU’s signal respectively. The rules used in decision fusion techniques are OR rule, AND rule and K-out-of-N rule. The CSS is further classified as distributed CSS and centralized CSS. In distributed CSS all the SUs share the spectrum detection information with each other and by mixing the shared information; all the SUs take final decision individually. In centralized CSS all the SUs send their detected information to a secondary base station / central unit which combines the shared information and takes final decision. The secondary base station shares the final decision with all the SUs in the CRN. This paper covers overview of information fusion methods used for CSS and analysis of decision fusion rules with simulation results.