An Enhanced Probabilistic Based Hybrid Neural Network for Spectrum Sensing In Cognitive Radio Networks

  • P Pavithra Roy

Abstract

Cognitive radio is the important key to the shortage of spectrum and for identifying the unused spectrum by a way of the spectrum sensing method. Although a huge load of energy is applied by spectrum sensing, it can be minimized by using various artificial neural network techniques to determine the appropriate spectrum vacancy. In this paper, an enhanced probabilistic based hybrid neural network is presented for spectrum sensing in cognitive radio network. To optimize the weight and bias of probabilistic neural network (PNN), spider monkey optimization (SMO) algorithm is presented. Simulation results showed that the performance of the proposed approach outperforms that of the existing approaches in terms of throughput.

Published
2020-02-02
How to Cite
Roy, P. P. (2020). An Enhanced Probabilistic Based Hybrid Neural Network for Spectrum Sensing In Cognitive Radio Networks. International Journal of Advanced Science and Technology, 29(04), 922 - 930. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/4762