Modelo de propagación para un entorno urbano que identifica las oportunidades espectrales para redes móviles de radio cognitiva

El pronóstico de ocupación del espectro radioeléctrico es útil en el diseño de sistemas inalámbricos que aprovechan las oportunidades en el espectro como la radio cognitiva. En este documento se propone el desarrollo de un modelo de propagación, que a través del pronóstico de la potencia recibida, identifica las oportunidades espectrales en canales de una red móvil celular para un entorno urbano. El modelo propuesto integra un modelo de propagación a gran escala con un modelo neuronal wavelet, que combina las pérdidas promedio con las pérdidas instantáneas. Los resultados del modelo, obtenidos a través de simulaciones, son consistentes con el comportamiento observado en experimentos de este tipo de sistemas inalámbricos.Abstract. The forecast of the radioelectric spectrum occupancy is useful for wireless systems designs that take advantage of spectrum opportunities, such as cognitive radio. In this document the development of a propagation model is proposed, that through the forecasting of received power, identifies the spectral opportunities in channels of a cellular mobile network for an urban environment. The proposed model integrates a large-scale propagation model with a wavelet neural model, which combines the average losses with the instantaneous losses. The results of this model, which are obtained through simulations, are consistent with the behavior observed experimentally of this class of wireless systems.Doctorad

Implementation of Hidden Markov Model spectrum prediction algorithm

The demand for spectrum is at an all time high due to the increasing popularity of wireless devices. As such it is imperative that technological and regulatory mechanisms are developed to maximise spectral efficiency. This paper documents a method for increasing spectral efficiency through the prediction of spectrum "holes" for use with cognitive radio technologies. An algorithm is developed based upon a Hidden Markov Model of the spectral environment. The Baum-Welch algorithm is employed to dynamically calculate the transition parameters of the model. The algorithm is tested upon data collected from the 450-470 MHz band in Australia with a reward function implemented to analyse the performance of the algorithm

Implementation of Hidden Markov Model spectrum prediction algorithm

The demand for spectrum is at an all time high due to the increasing popularity of wireless devices. As such it is imperative that technological and regulatory mechanisms are developed to maximise spectral efficiency. This paper documents a method for increasing spectral efficiency through the prediction of spectrum "holes" for use with cognitive radio technologies. An algorithm is developed based upon a Hidden Markov Model of the spectral environment. The Baum-Welch algorithm is employed to dynamically calculate the transition parameters of the model. The algorithm is tested upon data collected from the 450-470 MHz band in Australia with a reward function implemented to analyse the performance of the algorithm