From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks

Strategies to acquire white space information is the single most significant functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution to enhance information accuracy. The evolution trends are spectrum sensing, prediction algorithm and recently, geo‐location database technique. Previously, spectrum sensing was the main technique for detecting the presence/absence of a primary user (PU) signal in a given radio frequency (RF) spectrum. However, this expectation could not materialized as a result of numerous technical challenges ranging from hardware imperfections to RF signal impairments. To convey the evolutionary trends in the development of white space information, we present a survey of the contemporary advancements in PU detection with emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks. It is found that geo‐location database is the most reliable technique to acquire TVWS information although, it is financially driven. Finally, using financially driven database model, this study compared the data‐rate and spectral efficiency of FCC and Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the adoption of an allinclusive TVWS information acquisition model as the future research direction for TVWS information acquisition techniques

Learning-based short-time prediction of photovoltaic resources for pre-emptive excursion cancellation

There is a growing interest in using renewable energy resources (RES) such as wind, solar, geothermal and biomass in power systems. The main incentives for using renewable energy resources include the growing interest in sustainable and clean generation as well as reduced fuel cost. However, the challenge with using wind and solar resources is their indeterminacy which leads to voltage and frequency excursions. In this dissertation, first, the economic dispatch (ED) problem for a community microgrid is studied which explores a community energy market. As a result of this work, the importance of modeling and predicting renewable resources is understood. Hence, a new algorithm based on dictionary learning for prediction of solar production is introduced. In this method, a dictionary is trained to carry various behaviors of the system. Prediction is performed by reconstructing the tail of the upcoming signal using this dictionary. To improve the accuracy of prediction, a new approach based on a novel clustering-based Markov Switched Autoregressive Model is proposed that is capable of predicting short-term solar production. This method extracts autoregressive features of the training data and partitions them into multiple clusters. Later, it uses the representative feature of each cluster to predict the upcoming solar production level. Additionally, a Markov jump chain is added to improve the robustness of this scheme to noise. Lastly, a method to utilize these prediction mechanisms in a preemptive model predictive control is explored. By incorporating the expected production levels, a model predictive controller is designed to preemptively cancel the upcoming excursions --Abstract, page iv

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