Optimized Neural Network for Spectrum Prediction Scheme in Cognitive Radio

Cognitive radio technologies permit the sharing of spectrums between unlicensed as well as licensed customers, based on the principle of non-interference. Spectrum sensing, in this field, is hence a vital tool that assists in the ascertainment of availability of a particular channel within the licensed spectrum, for unlicensed customers. But this function uses significant power that could be lessened through the employment of predictive mechanisms to discover spectrum holes. The traffic features of licensed customer systems in the real world are not known beforehand. In this paper, a spectrum predictor based on Neural Networks model Multi-Layer Perceptron and Back Propagation that do not need prior information regarding traffic features of licensed customers is designed. Binary Shuffled Frog Leaping Algorithm is proposed for structure optimization, the binary structure is suggested to show the memes with the purpose of developing a sub-collection with lesser dimensions than that of the original collection where detecting sensitivity and accuracy would be scalable with that of the primary status. Spectrum Predictor’s performance is examined through exhaustive experiments.