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Open AccessArticle

An Accurate Probabilistic Model for TVWS Identification

Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada
Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas ESPE, Sangolquí 171103, Ecuador
Department of Electrical and Electronic Engineering, University of Tripoli, Tripoli 13555, Libya
Departamento de Teoría de la Señal y Comunicaciones, Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, 28933 Móstoles, Spain
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(20), 4232;
Received: 16 September 2019 / Revised: 4 October 2019 / Accepted: 5 October 2019 / Published: 10 October 2019
(This article belongs to the Special Issue Multi-Channel and Multi-Agent Signal Processing)
Television White Spaces (TVWS)-based cognitive radio systems can improve spectrum efficiency by facilitating opportunistic usage of television broadcasting spectrum by secondary users without interfering with primary users. Previously applied models introduce missed detection errors, giving a limited estimation of the spectrum occupancy, which does not correspond to the reality of its usage, hence resulting in a partial waste of this resource. Considering jointly parameters like false alarm probability and detection probability, this article proposes a probabilistic model that can identify TVWS with improved accuracy. The proposed model considers energy detection criteria, combined with simultaneous sensing of the noise and of the signal from primary users. In order to demonstrate the model effectiveness, a low-cost Mobile Spectrum Sensing Station prototype was designed, implemented, and subsequently mounted on a vehicle. More than eight million spatio-temporally variant data samples were collected by scanning the UHF-TV spectrum of 500–698 MHz in the city of Windsor, ON, Canada. Analysis of the collected data showed that the proposed model achieves an accuracy improvement of about 9.6% compared to existing models, demonstrating that TVWS vary with spatial displacement and increasing significantly in the rural areas. Even in the most crowded spectrum zone, about 28% of the channels are identified as TVWS, and this number increases to a maximum of 60% in less crowded regions in urban areas. We conclude that the proposed model improves the TVWS detection compared with other used models, and also that the elements considered in this research contribute to reduce the complexity of the mathematical calculations while maintaining the accuracy. A low-cost open-source sensing station has been designed and tested, which represents an operative and useful data source in this setting. View Full-Text
Keywords: TVWS; dynamic spectrum access; false alarm probability; detection probability TVWS; dynamic spectrum access; false alarm probability; detection probability
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MDPI and ACS Style

Corral-De-Witt, D.; Ahmed, S.; Awin, F.; Rojo-Álvarez, J.L.; Tepe, K. An Accurate Probabilistic Model for TVWS Identification. Appl. Sci. 2019, 9, 4232.

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