# An Accurate Probabilistic Model for TVWS Identification  
[PERSON]  
1 Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada; [EMAIL_ADDRESS] (S.A.); [EMAIL_ADDRESS] (F.A.); [EMAIL_ADDRESS] (K.T.)  
1 Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada; [EMAIL_ADDRESS] (S.A.); [EMAIL_ADDRESS] (F.A.); [EMAIL_ADDRESS] (K.T.)  
2 Departamento de Electrica y Electronica, Universidad de las Fuerzas Armadas ESPE, Sangolqui 171103, Ecuador  
3 Department of Electrical and Electronic Engineering, University of Tripoli, Tripoli 13555, Libya  
4 Departamento de Teoria de la Senal y Comunicaciones, Sistemas Telematicos y Computacion, Universidad Rey Juan Carlos, 28933 Mostoles, Spain; [EMAIL_ADDRESS]  
5 Correspondence: [EMAIL_ADDRESS]; Tel.: +1-226-246-5278  
Spaces (TVWS) are of special interest and this increasingly used term refers to WS that are located in any of the bands assigned to TV-broadcasting service bands [4]. TV spectrum bands often show underutilization by PU and hence the use of TVWS in CR is a promising solution to spectrum scarcity. In this setting, the Institute of Electrical and Electronics Engineers (IEEE) approved the 802.22 standard in 2011 [5]. It is important to note that independently of which spectrum band is being used, CR must avoid interference with PU at any time. But even before that, the task of finding a model that allows accurate identification of TVWS minimizing false alarms errors needs attention.  
In this article, we propose an accurate statistical model to identify the presence of TVWS based on the False Alarm Probability (\(P_{fa}\)) and on the Detection Probability (\(P_{d}\)). These parameters are obtained by simultaneous sensing the noise intensity (\(W(f)\)) and the signal of the PU (\(X(f)\)) in the environmental spectrum. The functional block diagram representing the system that corresponds to our proposed model is shown in Figure 1. As observed, \(W(f)\) and \(X(f)\) are the two inputs to the collection block where data processing takes place. Next, the processed data is fed to the analysis block, where all the stochastic and mathematical analyses are performed. Then follows the decision block, where the presence or absence of the PU is decided. Finally, results are visually demonstrated in the results block. As finally concluded, this model gives about 9.6% increase in \(P_{d}\) compared to other existing models. The motivation behind undertaking this research endeavour is the fact that CR TVWS has a huge potential in many diverse wireless services and applications, for example, internet access for rural areas, Wireless Regional Area Networks (WRAN), Wireless Sensor Networks (WSN), Emergency Communications (EC), and Telecommunications for Disaster Relief (TDR), among many others. Particularly, the last two applications (i.e., EC and TDR) have drawn significant research interest as they are both related to rescuing lives.  
The rest of the manuscript is organized as follows. Section 2 provides a review of the background for this research. Section 3 is a description of the used hardware and data collection patterns. Section 4 describes the proposed model and the mechanism to find TVWS. Experimental results are shown and discussed in Section 5. Conclusions are presented in Section 6.