Abstract
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 optimization (MO hybrid IWO/PSO) based soft decision fusion (SDF) approach for optimizing the global decision threshold and weight coefficient vector assigned to each cognitive users (CUs) in order to maximize the detection probability, and minimize the false alarm probability and overall probability of error at the same time. Simulation results are analyzed, and performance metrics are compared qualitatively to evaluate the different multiobjective evolutionary algorithms. It is observed that our proposed method outperforms the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO) and nondominated sorting invasive weed optimization (NSIWO) in the terms of detection accuracy and nondominated solutions.
Published Version
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