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

摘要

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.