Pronóstico de Oportunidades de Espectro Radioeléctrico en una Red Móvil con base en el Modelo de Propagación Interino de la Universidad de Stanford

The development of a method that identifies radioelectric spectrum opportunities in a channel of a mobile cellular network for an urban environment using received signal power forecast is presented. Radioelectric spectrum occupancy forecast has proven useful in the design of wireless systems able to harness spectrum opportunities like cognitive radio, which is one of the technologies included in the Internet of things. The proposed method integrates the Stanford University Interim large-scale propagation model with a wavelet neural model. The results obtained through simulations are consistent with the observed behavior in experiments carried out in wireless systems of this type. The proposed model in the channel of global system for mobile communication band may help cognitive radio users to share channels and to avoid collisions.