Real-Time Multi-Channel TVWS Sensing Prototype Using Software Defined Radio

Awin, Faroq; Younan, Aarron; Corral-De-Witt, Danilo; Tepe, Kemal; Abdel-Raheem, Esam

doi:10.1109/isspit.2018.8642660

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Table 1 illustrates the frequency ranges, channel numbers, bandwidth for each band, and the respective ITU regions [19] were the bands are allocated. Our work focuses on bands IV and V, i.e., 500 MHz to 698 MHz [20,21]. It should be noted that channel 37 (608 MHz to 614 MHz) is used for radio astronomy and it is excluded from TV broadcast services.…”

Section: Tvws Basicsmentioning

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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Section: Tvws Basicsmentioning

confidence: 99%

An Accurate Probabilistic Model for TVWS Identification

et al. 2019

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“…The results showed that the portable prototype was capable of detecting unoccupied frequency bands from 500 MHz to 698 MHz in urban areas. Moreover, in [ 20 ], a prototype able to detect up to 10 TV channels was presented. The sensing device was examined in a real environment, and a spectrum occupancy of 30.25% was calculated.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology

Molina-Tenorio

Prieto‐Guerrero

Aguilar-Gonzalez

2021

Sensors

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In this work, a novel multiband spectrum sensing technique is implemented in the context of cognitive radios. This technique is based on multiresolution analysis (wavelets), machine learning, and the Higuchi fractal dimension. The theoretical contribution was developed before by the authors; however, it has never been tested in a real-time scenario. Hence, in this work, it is proposed to link several affordable software-defined radios to sense a wide band of the radioelectric spectrum using this technique. Furthermore, in this real-time implementation, the following are proposed: (i) a module for the elimination of impulsive noise, with which the appearance of sudden changes in the signal is reduced through the detail coefficients of the multiresolution analysis, and (ii) the management of different devices through an application that updates the information of each secondary user every 100 ms. The performance of these linked devices was evaluated with encouraging results: 95% probability of success for signal-to-noise ratio (SNR) values greater than 0 dB and just five samples (mean) in error of the edge detection (start and end) for a primary user transmission.

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Real-Time Multi-Channel TVWS Sensing Prototype Using Software Defined Radio

Cited by 2 publications

References 7 publications

An Accurate Probabilistic Model for TVWS Identification

An Accurate Probabilistic Model for TVWS Identification

Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology

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