Implementation of cognitive radio networks for optimum spectrum utilization through feed forward backpropagation artificial neural network

Abstract

In wireless Radio Frequency (RF) networks, a huge amount of available bandwidth of the RF spectrum is wasted, because the users are not able to fully utilize the frequency band of the spectrum. The Cognitive Radio Networks provide an effective solution for optimum utilization of the frequency band of the RF spectrum. Cognitive Radio Networks consist of two types of users: primary users, which utilizes their allocated license frequency band of the RF spectrum and secondary users, which uses the license frequency band of the primary users when they are not using the RF spectrum. Primary users pre-empt the secondary users when they want to use the RF spectrum. To know when the spread spectrum is not occupied by the primary users, the secondary users must have some mechanism to sense the environment, learn the parameters and take a decision on the basis of it. The users of Cognitive Radio Networks enhance these capabilities by using Artificial Neural Networks (ANN). This paper discusses how the presence of primary users in the particular frequency band of the spectrum could be detected by the secondary users by using Feed Forward Back Propagation ANN. For this a CRN simulation model is designed and developed which uses a Feed forward back propagation ANN for optimum use of the spectrum.