On the relevance of using affordable tools for white spaces identification

Zennaro, Marco; Pietrosémoli, Ermanno; Bagula, Antoine; Nleya, Sindiso

doi:10.1109/wimob.2012.6379138

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…Since the introduction of such a low-cost spectrum analyzer, many researchers have carried out spectrum measurements around the world. For example, [20] carried out spectrum measurement in Trieste, Italy, from 400 MHz up to 800 MHz frequency band, while [21] performed spectrum occupancy measurement in UHF TV band in Venezuela. The use of such a low-cost spectrum analyzer enables us to gather a huge amount of data, paving the way towards deep learning approaches.…”

Section: Related Workmentioning

confidence: 99%

A Deep Convolutional Neural Network Based Transfer Learning Method for Non-Cooperative Spectrum Sensing

2020

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In this paper, we investigate machine learning methods for enabling high performance noncooperative spectrum sensing, for future cognitive radio systems. The fulfillment of sensing requirements is crucial for ensuring an efficient reuse of the scarce spectrum by unlicensed users, without causing harmful interference to primary users. In this work, we propose a deep convolutional neural network-based transfer learning framework for non-cooperative spectrum sensing in TV bands, applicable across various locations, wireless environments and even frequency assignments. Specifically, we design a four-layer convolutional neural network for limiting the computational costs while satisfying the sensing requirements, and apply transfer learning by freezing the first two convolutional layers. The performance of the proposed method is evaluated against benchmarks, based on over 29,000 spectrograms collected in UHF TV band from a recent measurement campaign. The experiments show that thanks to transfer learning, the proposed method is able to detect TV signals with high accuracy despite a significantly reduced amount of data, thereby providing a high adaptability to various locations, environments, and frequencies. Furthermore, the proposed method with transfer learning not only guarantees the sensing requirements but also realizes up to 94% reduction of training time of the network, as well as 20% reduction of the required sensing time, compared to the case without transfer learning.

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Section: Related Workmentioning

confidence: 99%

A Deep Convolutional Neural Network Based Transfer Learning Method for Non-Cooperative Spectrum Sensing

2020

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“…To capture and store the realtime data for future analysis, the RF-Explorer was connected to an RF-Explorer for Windows PC client software running on a Dell laptop through a USB cable. The RF-Explorer has already been proven to be efficient in spectrum use advocacy and measurements [16]. Picture of the experimental setup is shown in Fig.1.…”

Section: Measurement Setup and Choice Of Locationsmentioning

confidence: 99%

Spectrum Occupancy in Rural Nigeria: A Case for A Lightly Licensed Spectrum Band for Rural Broadband Enhancement

Ubom¹,

Akpanobong²,

Ukommi³

2019

IJCSIT

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A detailed report of the spectrum occupancy measurement drive conducted outdoors in three rural villages of Asong in Mkpat Enin, Ikot Akpabio in Etinan, and Afaha Offioing in Nsit Ibom Local Government Areas of Akwa Ibom state in Nigeria on the UHF and VHF broadcast frequency bands is here presented. The spectrum usage on a highly conservative noise floor, was high in the VHF band, thereby not having enough free bandwidth for secondary spectrum access while the UHF band percentage utilization for the three villages were found to be at 33%, 41% and 55% with available free cumulative bandwidths of 264MHz, 232MHz, and 176MHz respectively. From the findings, Nigeria can comfortably accommodate a lightly licensed spectrum band in the UHF band to enhancer rural broadband services.

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“…In the first method, the power pattern of the sensor was measured with and without the parasitic element on the body. Fig.16 shows the measurement setup which includes a wooden tripod (160 cm height), a RF explorer handheld spectrum analyzer [19] model 2.4G and a wooden rotator. The power measurement resolution for RF explorer is 0.5 dBm which is enough for desired measurements.…”

Section: Measurementmentioning

confidence: 99%

External Parasitic Elements on Clothing for Improved Performance of Wearable Antennas

Varnoosfaderani

Thiel

2015

IEEE Sensors J.

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On the relevance of using affordable tools for white spaces identification

Cited by 9 publications

References 15 publications

A Deep Convolutional Neural Network Based Transfer Learning Method for Non-Cooperative Spectrum Sensing

A Deep Convolutional Neural Network Based Transfer Learning Method for Non-Cooperative Spectrum Sensing

Spectrum Occupancy in Rural Nigeria: A Case for A Lightly Licensed Spectrum Band for Rural Broadband Enhancement

External Parasitic Elements on Clothing for Improved Performance of Wearable Antennas

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