Modeling and prediction primary nodes in wireless networks of cognitive radio using recurrent neural networks

Abstract

The cognitive radio is a methodology that proposes the management of the radio spectrum dynamically, by integrating the stages of sensing, decision making, sharing and spectral mobility. The spectral decision-making phase is in charge of deciding which is the best available channel to transmit the data of Secondary Users (SUs) in an opportunistic manner, and its success depends on how efficient is the Primary User characterization model (PUs). The use of Recurrent Neural Networks (RNNs) is proposed as a model to reduce the prediction error that is presented in the future estimation of channels in the frequency band of 2.4 GHz. The findings found that the RNNs have the necessary self-management to improve the forecast channels' use by PUs in the WiFi spectral band and with better levels of success than those delivered by the Multilayer Perceptron Neural Networks (MLPNN). 2514Leydy Johana Hernández Viveros et al.