A combination of double sided neighbor distance and Genetic Algorithm in cooperative spectrum sensing against malicious users

Abstract

In Cognitive Radio Network (CRN) secondary users (unlicensed users) try to access radio spectrum of the primary users (licensed user) if not utilized by the licensed user. Cooperative spectrum sensing (CSS) is one way to acquire accurate status of primary user for achieving maximum utilization of the vacant spectrum. Existence of malicious users in CSS can drastically reduce the performance of the system. In this paper, we study how to minimize the effect of false spectrum sensing data coming from abnormal secondary users. The proposed work focuses on the use of double sided neighbour distance (DSND) algorithm along-with Genetic Algorithm (GA) for the detection and avoidance of misbehaving users in CSS. The scheme is tested against the existence of opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU) and always no malicious user (ANMU) sending spectrum sensing data to the fusion centre (FC) with normal secondary users (SUs). Simulation result shows effectiveness of the proposed method in making the results of the majority voting hard fusion combination scheme more accurate and reliable against simple hard fusion schemes and equal gain combination (EGC) in the presence of malicious users in CSS.