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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)
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)
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)
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