Primary user characterization for cognitive radio wireless networks using a neural system based on Deep Learning

Cognitive radio is a paradigm that proposes maximizing the utilization of the usable radio-electric spectrum, allowing licensed users (PUs) and non-licensed users (SUs) to simultaneously coexist through the dynamic management and assignment of spectrum resources, by integrating the stages of spectrum sensing, decision, sharing and mobility. Spectrum decision is one of the most important stages, but its optimal operation depends on the characterization sub-stage, which is in charge of efficiently estimating time gaps in which a PU won’t make use of the assigned spectrum, so that it can be used in an opportunistic fashion by SUs. The design and implementation of an algorithm based on the Long Short-Term Memory (LSTM) recurrent neural network is proposed in order to increase the success percentage in the forecasting (presence/absence) of PUs in spectrum channels. The accuracy level exhibited in the results indicates LSTM increases the prediction percentage as compared to the Multilayer Perceptron Neural Network (MLPNN) and the Adaptative Neuro-Fuzzy Inference System (ANFIS) learning models, which means it could be implemented in cognitive networks with centralized physical topologies.