An efficient dynamic thresholds energy detection technique for Cognitive Radio spectrum sensing

Farag, Hossam M.; Ehab, Muhammad

doi:10.1109/icenco.2014.7050446

Cited by 20 publications

(4 citation statements)

References 11 publications

(10 reference statements)

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“…To enhance the performance and the accuracy of energy detection, several methods based on the use of dynamics thresholds are investigated in [67][68][69][70]. 110 | P a g e www.ijacsa.thesai.org b) Matched filter detection: The matched filter detection technique is viewed as the best method for identifying the unutilized bands when the PU transmission characteristics are known a priori, e.g., bandwidth, operating frequency, the modulation type and order, packet format, and pulse shaping [70][71][72].…”

Section: ) Narrowband Sensing Techniquesmentioning

confidence: 99%

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

Kassri¹,

En‐Nouaary²,

Bah³

et al. 2021

IJACSA

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The inherent scarcity of frequency spectrum, along with the fixed spectrum allocation adopted policy, has led to a dire shortage of this indispensable resource. Furthermore, with the tremendous growth of wireless applications, this problem is intensified as the unlicensed frequency spectrum becomes overcrowded and unable to meet the requirement of emerging radio devices operating at higher data rates. Additionally, the already assigned spectrum is underutilized. That has prompted researchers to look for a way to address spectrum scarcity and enable efficient use of the available spectrum. In this context, Cognitive Radio (CR) technology has been proposed as a potential means to overcome this issue by introducing opportunistic usage to less congested portions of the licensed spectrum. In addition to outlining the fundamentals of Cognitive Radio, including Dynamic Spectrum Access (DSA) paradigms and CR functions, this paper has a three-fold objective: first, providing an overview of Software Defined Radio (SDR), in which the architecture, benefits, and ongoing challenges of SDR are presented; second, giving an extensive review of spectrum sensing, covering sensing types, narrowband and wideband sensing schemes with their pros and cons, Machine Learningbased sensing, and open issues that need to be further addressed in this field; third, exploring the use of Cognitive Radio in the Internet of Things (IoT) while highlighting the crucial contribution of CR in enabling IoT. This Review is elaborated in an informative fashion to help new researchers entering the area of Cognitive Radio Networks (CRN) to easily get involved.

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Section: ) Narrowband Sensing Techniquesmentioning

confidence: 99%

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

Kassri¹,

En‐Nouaary²,

Bah³

et al. 2021

IJACSA

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“…The distribution of T is chi-squared with 2N s degrees of freedom [14]. The received samples are compared with predefined threshold computed based on Constant False Alarm probability (CFA) [15]- [17] . The performance of the detection in energy detector schemes is evaluated based on Receiver Operation Characteristics (ROC) that showing the detection probability (PD) , False Alarm Probability (PFA) , Signal to Noise Ratio (SNR) , and threshold .…”

Section: Conventional Energy Detector ( Ced)mentioning

confidence: 99%

DWT Based Energy Detection Spectrum Sensing Method for Cognitive Radio System

Falih

Abdullah²

2020

Iraqi Journal of ICT

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In this paper a new blind energy detection spectrum-sensing method based on Discreet Wavelet Transform (DWT) is proposed. The method utilizes the DWT sub-band to collects the received energy. The proposed method recognizes the Primary User (PU) signal from noise only signal using the differences in the collected energy in first and last sub-bands of one level DWT. The simulation results show that the proposed method achieves improved detection probability especially at low Signal to Noise Ratio (SNR) compared to Conventional Energy Detector (CED). The results also show that the proposed method has shorter sensing time and less Energy Consumption (EC) compared to CED due to using small number of processed sample. Therefore, this method is suitable for Cognitive Radio (CR) applications where only limited energy like device battery is available.

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“…• Energy detected on each sub-channel: the series of energy measurements are parameters used for prediction of future channel states (busy or idle). Energy detection technique as detailed by Farag and Ehab (2014), with threshold mechanism, is subsequently used to determine whether the sub-channel appear free or busy.…”

Section: Prediction Algorithm Input Datamentioning

confidence: 99%

Towards cognitive radio-based eHealth systems

Ouattara

Chalouf

Krief

2019

IJAACS

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In this paper, we investigate the use of cognitive radio systems in medical data transmission. Therefore, based on the state-of-the-art, we propose a complete cognitive-radio communication architecture for eHealth systems. The goal is to point out significant technical challenges required for more flexible and effective transmissions in eHealth contexts. After that, we study connectivity, interference management and performance criteria in cognitive-radio based eHealth systems. For connectivity, we show that cognitive-radio technology allows exploring opportunistically frequencies to find available channels. Then, to avoid interference, we propose a strategy-based management plan to reduce disruption risks. An example of strategy, frequency hopping, is then implemented and tested. Finally, we analyse the performance of our proposal for some categories of medical data. We propose therefore a function that reinforces the cognitive radio decision-making and spectrum sharing modules by controlling some quality parameters.

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An efficient dynamic thresholds energy detection technique for Cognitive Radio spectrum sensing

Cited by 20 publications

References 11 publications

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

DWT Based Energy Detection Spectrum Sensing Method for Cognitive Radio System

Towards cognitive radio-based eHealth systems

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