Enhanced spectrum sensing based on Energy detection in cognitive radio network using adaptive threshold

Alom, Md. Zulfikar; Godder, Tapan Kumar; Morshed, M. N.; Maali, Asmaa

doi:10.1109/nsyss.2017.7885815

Cited by 41 publications

(25 citation statements)

References 4 publications

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“…The state ℋ0 corresponds primary user absence and state ℋ1 corresponds primary user presence. For the sensing decision, several of the previously mentioned spectrum sensing techniques can be used, including energy detection [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], cyclostationary detection [21,22,23,24,25,26,27], matched filter detection [28,29,30,31], covariance-based detection [32,33,34,35,36,37,38,39], and machine-learning based detection [40,41,42,43,44,45,46,47,48,49,50,51] which are discussed below. These techniques are often evaluated using the probabilities of false alarm and probability of detection.…”

Section: Narrowband Spectrum Sensingmentioning

confidence: 99%

“…Energy detection [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] computes the energy of the samples and compares it to a threshold. If the energy is higher than this threshold, the primary user signal is considered present; otherwise, the primary user is considered absent.…”

Section: Narrowband Spectrum Sensingmentioning

confidence: 99%

“…Next generation communication systems are expected to operate at very low SNR values where compressive sensing techniques are usually inaccurate. Very little research has investigated wideband spectrum sensing efficiency at very low SNR although this is a promising area of research that ought to be considered as a possible future direction [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,131,132,133].…”

Section: Challenges and Future Research Directionsmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

Arjoune

Kaabouch

2019

Sensors

345

196

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Cognitive radio technology has the potential to address the shortage of available radio spectrum by enabling dynamic spectrum access. Since its introduction, researchers have been working on enabling this innovative technology in managing the radio spectrum. As a result, this research field has been progressing at a rapid pace and significant advances have been made. To help researchers stay abreast of these advances, surveys and tutorial papers are strongly needed. Therefore, in this paper, we aimed to provide an in-depth survey on the most recent advances in spectrum sensing, covering its development from its inception to its current state and beyond. In addition, we highlight the efficiency and limitations of both narrowband and wideband spectrum sensing techniques as well as the challenges involved in their implementation. TV white spaces are also discussed in this paper as the first real application of cognitive radio. Last but by no means least, we discuss future research directions. This survey paper was designed in a way to help new researchers in the field to become familiar with the concepts of spectrum sensing, compressive sensing, and machine learning, all of which are the enabling technologies of the future networks, yet to help researchers further improve the efficiently of spectrum sensing.

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Section: Narrowband Spectrum Sensingmentioning

confidence: 99%

Section: Narrowband Spectrum Sensingmentioning

confidence: 99%

Section: Challenges and Future Research Directionsmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

Arjoune

Kaabouch

2019

Sensors

345

196

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“…The ED is a simple and straightforward technique, because it does not require any previous information about the signal characteristics, and hence it is straightforward and easy to implement. The ED 5–18 is based on computing the energy of the samples and comparing it with a threshold. If the energy is higher than this threshold, the PU signal is present; otherwise, it is absent.…”

Section: Related Workmentioning

confidence: 99%

Adaptive two‐stage spectrum sensing model using energy detection and wavelet denoising for cognitive radio systems

Fawzi

El‐Shafai

Abd‐Elnaby

et al. 2020

Int J Communication

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This paper presents an efficient adaptive two-stage spectrum sensing model for the cognitive radio (CR) systems. The proposed model combines two wellknown techniques: energy detection (ED) and wavelet denoising (WD). The ED technique is employed to identify the existence of the primary user (PU) signal in the case of high signal-to-noise ratio (SNR) by comparing the received signal with a threshold. In the case of low SNR, the ED technique alone cannot be adopted for decision making about the status of the PU due to the noise impact on the received signals. Hence, the stage of WD is exploited prior to the ED for reducing the noise effect and detecting the PU signal in the presence of noise. The detection performance of the proposed model is compared with those of the ED alone and the previous related two-stage spectrum sensing methods. Simulation results show that the proposed model improves the false alarm rate compared to the ED alone by 36% and 6%, respectively, at low SNRs of −15 and −20 dB. Also, the proposed model yields the smallest detection time compared to the previous two-stage spectrum sensing methods. The proposed model can be used to minimize the probability of false alarm, achieve a better probability of detection, and improve the sensing process in CR systems.

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“…To overcome the limitation of fixed threshold, spectrum sensing with the adaptive threshold is presented. Adaptive threshold detects the signal at low SNR rates [6]. The fundamental and important mechanism of cognitive radio networks (CRN) is to sense the spectrum.…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

Pethunachiyar

Sankaragomathi

2021

IJEECS

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<p class="IJASEITAbtract"><span>Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum, the cost for computation and difficult in finding the user in low signal-to noise ratio (SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like decision tree, support vector machines, naive bayes, ensemble based trees, nearest neighbour’s and logistic regression are used for testing the algorithm. As a first step, the spectrum sensing is done in two stages with Orthogonal Frequency Division Multiplexing and Energy Detection algorithm at the various values of SNR. The results generated from the above algorithm is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction. The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, k-nearest neighbour (KNN) algorithm produces the better performance in a minimized time.</span></p>

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Enhanced spectrum sensing based on Energy detection in cognitive radio network using adaptive threshold

Cited by 41 publications

References 4 publications

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

Adaptive two‐stage spectrum sensing model using energy detection and wavelet denoising for cognitive radio systems

Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

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