RETRACTED ARTICLE: Classical energy detection method for spectrum detecting in cognitive radio networks by using robust augmented threshold technique

Sarala, B.; Devi, Swapna; Bhargava, D. S.

doi:10.1007/s10586-017-1311-8

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…Using the techniques of Machine Learning the cooperative spectrum sensing in CR network is done in [38]. The basic of CR architecture the advantages and properties are explained in [25] [26]. To enhance the spectrum efficiency higher and throughput the spectrum sensing in multiple input multiple output system hybrid model can be used [30] [31].…”

Section: Methodsmentioning

confidence: 99%

Encapsulation of Energy Efficient, Clustering Algorithm and Spectrum Sensing for Cognitive Radio Based Internet of Things Networks

Pravin Jaronde

2024

jes

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Since the two decade the Internet of Things (IoT) plays an important role in the field of communication technology. Out of maximum of IoT devices are battery operated. So there should be energy efficient devices that can operate more functions with less power consumption. The main power constraints in the IoT devices are in the communication like spectrum selection, data transmission, etc. To perform communication between the two nodes the spectrum must be available. But as spectrums are limited and a lot of data is to be transmitted there may the issue of spectrum scarcity. The dynamic spectrum access is done using Cognitive Radio (CR) technology that can overcome spectrum scarcity issue. This paper gives the research work on energy efficient, clustering algorithm and spectrum sensing for CR based IoT networks in terms of the methods, merits, demerits and implementation. For efficiency in the spectrum sensing and energy consumption in 5G wireless communication network and data transfer between the IoT devices this study is essential. The biblometric analysis is shown by using VOSviewer to visualize the bibliometric information and the result as an analysis of ROC curve for Rayleigh and Rician channel is plotted using Matlab.

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Section: Methodsmentioning

confidence: 99%

Encapsulation of Energy Efficient, Clustering Algorithm and Spectrum Sensing for Cognitive Radio Based Internet of Things Networks

Pravin Jaronde

2024

jes

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“…Numerous spectrum sensing methods have been investigated under varying conditions, including matched filter method [36], cyclostationary feature method [37], entropy-based method [39,40], eigenvalue-based method [38], and Energy Detection (ED) method [41,42]. The matched filter method and cyclostationary feature method are both easy to understand and execute.…”

Section: Introductionmentioning

confidence: 99%

An Energy Efficient Cooperative Spectrum Sensing for Cognitive Radio-Internet of Things with Interference Constraints

Miah

et al. 2021

Preprint

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Spectrum sensing plays a very important role in Cognitive Radio based Internet of Things (CR-IoT) networks for utilization of the licensed spectrum accurately. However, the performance of the conventional Energy Detector (ED) method is compromised in a noise-uncertain environment owing to interference constraints, i.e. the CR-IoT user interference with the licensed Primary User (PU) on the same licensed band. To overcome this drawback, we proposed an energy efficient Cooperative Spectrum Sensing (CSS) for a CR-IoT network with interference constraints using a novel ED method. In this method, each CR-IoT user is capable of spectrum sensing that makes both the local decision and the weight factor based on the sequential approach; we calculate the weight factor against each CR-IoT user based on the Kullback Leibler Divergence award score. After the local decision and the weight factor are made, each CR-IoT user transmits its measured both the local decisions, and the weight factor to a Fusion Center (FC), which is made a final decision about the PU activities based on the hard fusion rule. The simulation results demonstrates that the proposed ED method obtains an improved detection performance, an enhanced sum rate, a spectral efficiency, an energy efficiency, and a lower global error probability when compared to other conventional ED methods under time varying environments.

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“…Spectrum-sensing techniques, like energy detection (ED), matched filter detection, cyclostationary feature detection, entropy-based detection, and eigenvalue-based detection, were investigated under varying conditions [25][26][27][28][29][30]. Among the previously mentioned techniques of spectrum sensing, the ED technique has the advantages of low complexity and cost effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things

Miah

Ahmed

Islam

et al. 2020

Sensors

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Spectrum sensing plays a vital role in cognitive radio networks (CRNs) for identifying the spectrum hole. However, an individual cognitive radio user in a CRN does not obtain sufficient sensing performance and sum rate of the primary and secondary links to support the future Internet of Things (IoT) using conventional detection techniques such as the energy detection (ED) technique in a noise-uncertain environment. In an environment comprising noise uncertainty, the performance of conventional energy detection techniques is significantly degraded owing to the noise fluctuation caused by the noise temperature, interference, and filtering. To mitigate this problem, we present a cooperative spectrum sensing technique that comprises the use of the Kullback–Leibler divergence (KLD) in cognitive radio-based IoT (CR-IoT). In the proposed method, each unlicensed IoT device that is capable of spectrum sensing, which is called a CR-IoT user, makes a local decision using the KLD technique. The spectrum sensing performed with the KLD requires a smaller number of samples than other conventional approaches, e.g., energy detection, for reliable sensing even in a noise uncertain environment. After the local decision is made, each CR-IoT user sends its own local decision result to the corresponding fusion center, which makes a global decision using the soft fusion rule. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, i.e., higher detection and lower false-alarm probabilities, enhances the sum rate, and reduces the total time as compared to the conventional ED scheme under various fading channels.

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RETRACTED ARTICLE: Classical energy detection method for spectrum detecting in cognitive radio networks by using robust augmented threshold technique

Cited by 7 publications

References 22 publications

Encapsulation of Energy Efficient, Clustering Algorithm and Spectrum Sensing for Cognitive Radio Based Internet of Things Networks

Encapsulation of Energy Efficient, Clustering Algorithm and Spectrum Sensing for Cognitive Radio Based Internet of Things Networks

An Energy Efficient Cooperative Spectrum Sensing for Cognitive Radio-Internet of Things with Interference Constraints

Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things

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