Estimation based cyclostationary detection for energy harvesting cooperative cognitive radio network

Talukdar, Banani; Kumar, Deepak; Hoque, Shanidul; Arif, Wasim

doi:10.1007/s11235-021-00846-2

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…Various signal detection methods are used in radiocommunications, surveying, radar, sonar or other signal processing systems. These include the energy detector [ 9 , 10 ], matched filter [ 11 ], correlation detector [ 12 ], cyclostationarity detector [ 13 ], wavelet detector [ 14 ] or covariance detector [ 15 ]. In the case of devices or systems analyzing the radio frequency spectrum, such as cognitive radio, the correct detection of radio signals in the electromagnetic spectrum is a key issue defining the usability of a given solution in a specific operational scenario.…”

Section: Wideband Spectrum Sensingmentioning

confidence: 99%

A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing

Olesiński

Piotrowski

2023

Sensors

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Wideband spectrum sensing plays a crucial role in various wireless communication applications. Traditional methods, such as energy detection with thresholding, have limitations like detecting signals with low signal-to-noise ratio (SNR). This article proposes a novel deep learning-based approach for RF signal detection in the wideband spectrum. The objective is to accurately estimate the noise distribution in a wideband radio spectrogram and improve the detection performance by substracting it. The proposed method utilizes convolutional neural networks to analyze radio spectrograms. Model evaluation demonstrates that the RFROI-CNN approach outperforms the traditional energy detection with thresholding method by achieving significantly better detection results, even up to 6 dB, and expanding the capabilities of wideband spectrum sensing systems. The proposed approach, with its precise estimation of noise distribution and consideration of neighboring signal power values, proves to be a promising solution for RF signal detection.

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Section: Wideband Spectrum Sensingmentioning

confidence: 99%

A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing

Olesiński

Piotrowski

2023

Sensors

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“…Various signal detection methods are used in radiocommunications, surveying, radar, sonar or other signal processing systems. These include the energy detector [9], [10], matched filter [11], correlation detector [12], cyclostationarity detector [13], wavelet detector [14] or covariance detector [15]. In the case of devices or systems analysing the radio frequency spectrum, such as cognitive radio, the correct detection of radio signals in the electromagnetic spectrum is a key issue defining the usability of a given solution in a specific operational scenario.…”

Section: Wideband Spectrum Sensingmentioning

confidence: 99%

RFROI–CNN WSS – Radio Frequency Regions of Interests Convolutional Neural Network for Wideband Spectrum Sensing

Olesiński¹,

Piotrowski²

2022

Preprint

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<p>In this paper, we propose the application of deep convolutional neural networks for the detection of radio signals in the broadband frequency spectrum. The RFROI–CNN (Radio Frequency Regions of Interests) approach uses a different way of proposing regions than in typical convolutional object detectors in images – instead of proposing regions based on the distinguishing features of specific radio transmissions, the radio signals are treated as masks that obscure the actual noise distribution, and a deep network is used to estimate the noise distribution in the broadband radio frequency spectrum. This approach allows the SNR of the signals to be maximised in signal processing and, consequently, detection characterised by higher values of quality indicators.</p>

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An energy-optimized green cooperative cognitive radio network for better spectrum sharing

Poonguzhali,

Murthy,

Godi

et al. 2024

Telecommun Syst

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Estimation based cyclostationary detection for energy harvesting cooperative cognitive radio network

Cited by 6 publications

References 30 publications

A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing

A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing

RFROI–CNN WSS – Radio Frequency Regions of Interests Convolutional Neural Network for Wideband Spectrum Sensing

An energy-optimized green cooperative cognitive radio network for better spectrum sharing

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