A review on energy based spectrum sensing in Cognitive Radio Networks

Malhotra, Meenakshi; Aulakh, Inderdeep Kaur; Vig, Renu

doi:10.1109/ablaze.2015.7154925

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…The first step aims at defining a test statistic, which varies according to the technique applied. For instance, energy-based detection takes the sum energy of the received signal as a test statistic [35]. The second step consists of a comparison between the test statistics and a predefined threshold to take a final decision as a last step.…”

Section: Scalogram-based Cnn Models For Spectrum Sensingmentioning

confidence: 99%

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

Benazzouza

Ridouani²,

Salahdine³

et al. 2022

Sensors

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Cooperative network is a promising concept for achieving a high-accuracy decision of spectrum sensing in cognitive radio networks. It enables a collaborative exchange of the sensing measurements among the network users to monitor the primary spectrum occupancy. However, the presence of malicious users leads to harmful interferences in the system by transmitting incorrect local sensing observations.To overcome this security related problem and to improve the accuracy decision of spectrum sensing in cooperative cognitive radio networks, we proposed a new approach based on two machine learning solutions. For the first solution, a new stacking model-based malicious users detection is proposed, using two innovative techniques, including chaotic compressive sensing technique-based authentication for feature extraction with a minimum of measurements and an ensemble machine learning technique for users classification. For the second solution, a novel deep learning technique is proposed, using scalogram images as inputs for the primary user spectrum’s classification. The simulation results show the high efficiency of both proposed solutions, where the accuracy of the new stacking model reaches 97% in the presence of 50% of malicious users, while the new scalogram technique-based spectrum sensing is fast and achieves a high probability of detection with a lower number of epochs and a low probability of false alarm.

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Section: Scalogram-based Cnn Models For Spectrum Sensingmentioning

confidence: 99%

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

Benazzouza

Ridouani²,

Salahdine³

et al. 2022

Sensors

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show abstract

“…Secondary User signal is obtained using OFDM parameters. Energy Detection-based spectrum sensing [18] method is used by the secondary units to detect the vacant spectrum pertaining to possible spectral access by the secondary device. Secondary Users are allowed to coexist with primary user for transmission using the same channel until their interference does not cross the allowable interference limit.…”

Section: System Modelmentioning

confidence: 99%

Enhancing the Date Rate by the Management of Bandwidth and Power Using Interference Temperature Constraint in Cognitive Radio Networks

Goel

Singh

Goel³

2021

ICT With Intelligent Applications

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As the network nowadays is overcrowded, due to increase in demand of wireless applications requiring high data rates with full signal strength still there are parts in our overall wireless spectrum that are unutilized. Cognitive radio networks technology is a one step ahead in solving the problem of underutilization of certain parts of spectrum. Interference plays a negative role in the success of cognitive radio networks technology. Interference temperature gives the closest approximation of overall interference at a particular time and position. The proposed approach implements a feedback system in cognitive radio networks using interference temperature and achievable capacity as a constraint using back propagation neural network. The goal is to maximize data rate at a particular time at designated channel, and channel utilization is maximized.

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Spectrum Sharing in Cognitive Radio Networks

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A review on energy based spectrum sensing in Cognitive Radio Networks

Cited by 9 publications

References 21 publications

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

Enhancing the Date Rate by the Management of Bandwidth and Power Using Interference Temperature Constraint in Cognitive Radio Networks

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