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Spectrum sensing is a key technology in cognitive radio networks (CRNs) to detect the unused spectrum. To achieve better performance cognitive radio (CR) users need to be able to adapt their transmission parameters according to the rapid changes in the surroundings. This paper proposes multi-objective hybrid invasive weed optimization and particle swarm(More)
Energy efficiency (EE) maximization with limited interference to the primary user (PU) is one of the primary concerns in cognitive radio networks (CRNs). To achieve this objective, we first propose an algorithm to select less spatially-correlated secondary users (SUs) to lessen the shadowing effect in wireless environment. Further, the aid of parametric(More)
Cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs) enhances the spectrum efficiency by allowing the secondary users (SUs) to access the underutilized licensed spectrum without causing undue interference with the primary user (PU).The main objective of this paper is to develop double threshold soft decision fusion (SDF) based CSS in order(More)
Recent advancements in artificial neural networks (ANNs) motivated us to design a simple and faster spectrum prediction model termed the functional link artificial neural network (FLANN). The main objective of this paper is to gather realistic data to obtain utilization statistics for the industrial, scientific and medical band of 2.4–2.5 GHz. To present(More)
Cognitive radio (CR) is a promising technology in order to solve the spectrum scarcity problem by allocating the spectrum dynamically to the unlicensed users. However, it is also vulnerable to various attacks, and can not offer sufficient defense towards its security. Primary user emulation attack (PUEA) is one of the major threats to CR which significantly(More)
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