Performance analysis of double threshold energy detection-based spectrum sensing in low SNRs over Nakagami-m fading channels with noise uncertainty

Eslami, Amir; Karamzadeh, Saeid

doi:10.1109/siu.2016.7495739

Cited by 10 publications

(6 citation statements)

References 7 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

341

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

341

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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“…Assuming the number of collected signals large enough, using central limit theorem, the probability density function (PDF) of E(t) under hypothesis H 0 becomes a normal distribution with mean equal to N 2 and variance equal to N 4 . PDF of E(t) under hypothesis H 1 becomes a normal distribution with mean equal to N(1+β) 2 and variance equal to (1+2β)N 4 . The probability that the detector detects the presence of signal under hypothesis H 0 is called probability of false alarm (P fa ) and the probability that the detector detects the presence of signal under hypothesis H 1 is called probability of detection (P d ), can be shown as follow:…”

Section: Energy Detection System Model and Backgroundmentioning

confidence: 99%

Proposal and Analysis of a New Spectrum Sensing Algorithm for Cognitive Radio Driven Hospitals

Eslami

Karamzadeh

2016

International Journal of Applied Mathematics, Electronics and Computers

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Wireless technology is the key technology to eliminate the dense wire ropes from hospitals and far access to medical devices. In order to overcome the problem of bandwidth scarcity, cognitive radio driven hospitals are introduced and devices are divided in two categories. The first category is primary devices and the second one is secondary devices. Primary devices has very high priority and their communication is vital for the hospital and patients, so that no interference should be made with such devices. Secondary devices are the ones which has lower priority and they can wait until the primary devices do their communication and then, they begin to use the allocated spectrum. One of the key functions to assure that there will be no interference is a reliable spectrum sensing method. This method should be a simple one to be able to implement it in the secondary devices. Among all the sensing methods, energy detection (ED) based spectrum sensing is very popular. In order to improve the performance of ED, double threshold ED (DTED) method is introduced in literature. In this paper, a new algorithm is intruded for DTED considering previous sensing period results in detection procedure by using a memory stick. Memoryful DTED (MDTED) improves the performance of DTED considerably by only the cost of delay in secondary devices communication which has less value than the improvement of the detection method performance.

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Performance analysis of double threshold energy detection-based spectrum sensing in low SNRs over Nakagami-m fading channels with noise uncertainty

Cited by 10 publications

References 7 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

Proposal and Analysis of a New Spectrum Sensing Algorithm for Cognitive Radio Driven Hospitals

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