Title: Hybrid neural network training algorithm for spectrum sensing in cognitive radio networks

Authors: P. Pavithra Roy; Mahankali Muralidhar

Addresses: Faculty of Electronics and Communication Engineering, Vemana Institute of Technology, Bangalore, India ' Department of EEE, Sri Venkateswara College of Engineering and Technology (Autonomous), Chittoor, Andhra Pradesh, India

Abstract: Spectrum sensing in the cognitive radio networks has emerged as a highly gifted technique which has riveted the eagle eyes of the enthusiastic experimenters for the last few years. In the novel method, the Levenberg-Marquardt-based neural network is integrated with the most modern optimisation technique, known as the gravitational search algorithm with an eye on perking up the proficiency in sensing of the channel. The user data is communicated by means of the redundant or vacant channels presently in the system by the deft deployment of the Levenberg-Marquardt-based neural network (GS-LM) approach, where the channel is located; in accordance with the channel state forecast outcomes. The relative evaluation is performed by assessing and contrasting the outcomes of the innovative approach to those of the HMM, LM-based NN and arbitrary technique. The maximum SU and SUimp values attained by the novel method are approximately 0.58 and 0.41 correspondingly.

Keywords: channel status prediction; spectrum sensing; hybrid neural networks; CRNs; cognitive radio networks; channel status prediction; Levenberg-Marquardt; gravitational search; optimisation.

DOI: 10.1504/IJMNDI.2016.079005

International Journal of Mobile Network Design and Innovation, 2016 Vol.6 No.3, pp.174 - 184

Available online: 05 Sep 2016 *

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