A Survey of TV White Space Measurements

Brown, Timothy X.; Pietrosémoli, Ermanno; Zennaro, Marco; Bagula, Antoine; Mauwa, Hope; Nleya, Sindiso

doi:10.1007/978-3-319-16886-9_17

Cited by 20 publications

(16 citation statements)

References 15 publications

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“…While similar measurements have previously been conducted [7,8,9,10,11,12,13,14,2,15,16,17,18,19], this article provides practical insight into the challenge faced by CR networks due to SS and the hidden node problem.…”

Section: Introductionmentioning

confidence: 65%

Spectral occupation of TV broadcast bands: Measurement and analysis

2016

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The findings of a TV broadcast spectrum measurement campaign, performed at six different locations on the Hatfield campus of the University of Pretoria, are presented. Since the use of television white spaces (TVWS) could help to alleviate the impending spectrum crunch, the motivation for the study was to identify possibly unused bands for use by emerging technologies, such as cognitive radio, and also to address the hidden node problem associated with spectrum sensing (SS). This was achieved by comparing measured data to both actual TV channel allocations and a geo-location database (GLDB) for the Tshwane metropolitan area (city of Pretoria). Localised measurements indicated that a number of TVWS opportunities existed, with between 216 and 376 MHz of spectrum found to be potentially available for secondary usage. However, a comparison with TV channel allocations (256 MHz free) and the GLDB (96 MHz free) highlighted the effect of the hidden node problem.

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Section: Introductionmentioning

confidence: 65%

Spectral occupation of TV broadcast bands: Measurement and analysis

2016

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“…The propagation models can be statistical, based only on distance and frequency, or they can based on ray tracing techniques that require detailed information about the terrain elevation in the area of interest. Therefore, correct identification of the presence of a TV signal at a given location depends on the fidelity of the database information and quality of the propagation model used to predict signal coverage [14].…”

Section: Geo-location Spectrum Databasementioning

confidence: 99%

“…The energy detection can be performed with a spectrum analyzer like the Radio Frequency (RF) Explorer (see Figure 2), while feature extraction is based on specific characteristics of the type of signal to be detected and is therefore, more sensitive but also more complex. With the appropriately detection threshold, the secondary user can identify TVWSs accurately and be able to transmits in those bands without causing interference to the primary users [14]. Energy detection is the commonly proposed method due to its simplicity.…”

Section: Spectrum Sensingmentioning

confidence: 99%

“…These unused radio frequency spectrum in the Ultra High Frequency (UHF) TV radio frequency band (so called TV white spaces) have been suggested as the solution to meet the growing demand for the wireless data transmission and have the potential to boost broadband communication in both rural and urban areas to support applications that can benefit communities such as drought mitigation [9,10], community healthcare [11,12], smart parkings [13], and many others. An efficient long-term solution that has been proposed for accessing the radio frequency spectrum is dynamic spectrum access (DSA) [14], in which devices are able to make real-time adjustment of spectrum utilization in response to changing environment using cognitive radio (CR) technology. Currently, CR technology has not been adopted yet as the technical details for its implementation have not been completely solved.…”

Section: Introductionmentioning

confidence: 99%

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Systematic Analysis of Geo-Location and Spectrum Sensing as Access Methods to TV White Space

Mauwa

Bagula

Zennaro

et al. 2017

JICTS

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Access to the television white space by white space devices comes with a major technical challenge: white space devices can interfere with existing television signals. Two methods have been suggested in the literature to help white space devices identify unused channels in the TV frequency band so that they can avoid causing harmful interference to primary users; geo-location spectrum database and spectrum sensing. Discussions in the literature have placed much emphasis on the limitations of the spectrum sensing approach mainly from the perspective of the developed world environment in which its drawbacks are significant. Little attention has been placed to the limitations of the geo-location database approach when applied in a developing regions. H. Mauwa et al.This paper considers a broader analysis of the approaches by looking at factors that can affect their performance and how the presence or absence of these factors in a developed region or developing region can affect their applicability. In so doing, the paper highlights the need to conduct more research on the performance of spectrum sensing in developing regions where there are much less TV broadcasting stations and therefore, white spaces are more abundant.

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“…The use of Cognitive Radio (CR) with different sensing techniques allows us to detect unused channels, known as White Spaces (WS), for their opportunistic usage by Secondary Users (SU) without affecting the Primary Users (PU), through a process called Dynamic Spectrum Access (DSA) [3]. In this context, Television White 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.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Probabilistic Model for TVWS Identification

et al. 2019

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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.

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A Survey of TV White Space Measurements

Cited by 20 publications

References 15 publications

Spectral occupation of TV broadcast bands: Measurement and analysis

Spectral occupation of TV broadcast bands: Measurement and analysis

Systematic Analysis of Geo-Location and Spectrum Sensing as Access Methods to TV White Space

An Accurate Probabilistic Model for TVWS Identification

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