An Accurate Probabilistic Model for TVWS Identification

Abstract

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.

Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters

“…An MSSS was implemented with the objective to sense the TV-UHF spectrum, as explained in [6]. By analyzing the results obtained therein, the authors realized the need for estimating TVWS in a rapid manner, for saving time, and for using open hardware and software tools.…”

“…To obtain accurate information regarding W(p), two devices sensed simultaneously the TV-UHF spectrum, as seen in Figure 2; signal Xo(p) ws collected through the Winegard antenna [16] and processed with one RFE [7]. At the same time, another RFE device with similar characteristics and a 50 Ω matched impedance replacing the antenna was used to sense the noise of the system [6]. As a result, both signals Xo(p) and W(p) were collected by the MSSS and processed as separated signals.…”

“…Based on this, a good option to estimate T is explained in [6]. Now, let us consider that, for each reading, in which we measure W(p), its mean value W represents the noise floor that we will use as a reference to calculate our T explained:…”

“…By using a Mobile Spectrum Sensing Station (MSSS), information on the TV-UHF spectrum (500 MHz to 698 MHz) is collected and analyzed. This station is mounted on a vehicle, and it allowed for sensing of a wide area across the city of Windsor in Canada [6]. The MSSS uses two Radio Frequency Explorer (RFE) devices [7] connected in parallel to sense the noise of PU signal W(p) and of composite signal Xo(p), which has noise and PU signals combined.…”

“…The presented paper is derived from the research detailed in [6]. Other interesting applications envisaged are the usage of TVWS in smart grid communications, as is explained in [10], or in Cognitive Vehicular Networks (CVN), as proposed in [11], or the indoor access to TVWS, also known as TV Gray Spaces (TVGS), detailed in [12,13].…”