A binary genetic algorithm (BGA)-based soft fusion (SF) scheme for cooperative spectrum sensing in CRN has been proposed in [9] to show as fast and efficient asset designation; calculations to empower SUs to adjust CRN parameters in the rapidly evolving environment. It also checks that the computation complexity of the proposed method meets real time requirements of the CR spectrum optimization. And it outperforms conventional SDF schemes. In this paper, Neyman-Pearson criterion is considered where probability of detection is maximized for a given false alarm probability, and the optimal set of BGA parameters have been discovered using set-and test approach.
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