Neural Networks and PCA for Spectrum Sensing in the Context of Cognitive Radio

Elrharras, Abdessamad; Saadane, Rachid; Wahbi, Mohamed; Hamdoun, Abdellatif

doi:10.1007/978-3-319-30298-0_18

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…Substituting ( 17) and ( 18) into Equation ( 16), the optimal separating hyperplane can be obtained by solving the following dual representation of the optimization problem: 𝑦 𝑖 = 0 , 𝛼 𝑖 > 0 By solving this dual Lagrange function (19), 𝛼 is evaluated. Consequently, 𝜔 is evaluated out from (17), and 𝑏 can be easily calculated from (20):…”

Section: Linearly Separable Classificationmentioning

confidence: 99%

“…If the signal energy exceeds the threshold, we declare the presence of the PU, otherwise it is absent. During the last years soft computing techniques like artificial neural networks (ANN) and support vector machine (SVM), have become extremely successful discriminative approaches to pattern classification [14][15][16][17][18][19][20]. In our context, we propose an implementation of ANN and SVM for SS operation to detect the PU signal; we focus on different ANN training algorithms and SVM functions that can be applied on the set of input data patterns.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neural Networks, Support Vector Machine And Energy Detection For Spectrum Sensing Based On Real Signals

Saber

Rharras

Saadane

et al. 2022

Int. j. commun. netw. inf. secur.

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A Cognitive Radio (CR) is an intelligent wireless communication system, which is able to improve the utilization of the spectral environment. Spectrum sensing (SS) is one of the most important phases in the cognitive radio cycle, this operation consists in detecting signals presence in a particular frequency band. In order to detect primary user (PU) existence, this paper proposes a low cost and low power consumption spectrum sensing implementation. Our proposed platform is tested based on real world signals. Those signals are generated by a Raspberry Pi card and a 433 MHz Wireless transmitter (ASK (Amplitude-Shift Keying) and FSK (Frequency-Shift Keying) modulation type). RTL-SDR dongle is used as a reception interface. In this work, we compare the performance of three methods for SS operation: The energy detection technique, the Artificial neural network (ANN) and the support vector machine (SVM). So, the received data could be classified as a PU or not (noise) by the ED method, and by training and testing on a proposed ANN and SVM classification model. The proposed algorithms are implemented under MATLAB software. In order to determine the best architecture, in the case of ANN, two different training algorithms are compared. Furthermore, we have investigated the effect of several SVM functions. The main objective is to find out the best method for signal detection between the three methods. The performance evaluation of our proposed system are the probability of detection and the false alarm probability . This Comparative work has shown that the SS operation by SVM can be more accurate than ANN and ED.

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Section: Linearly Separable Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks, Support Vector Machine And Energy Detection For Spectrum Sensing Based On Real Signals

Saber

Rharras

Saadane

et al. 2022

Int. j. commun. netw. inf. secur.

Self Cite

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“…Several researchers have discussed different simulation algorithms [24][25][26][27] and have researched various artificial neural network tools, interpretation, and modeling .…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Spectrum Sensing Techniques in 5G Cognitive Radio Networks in Soft Computing: A Review

Neshe¹,

Singh²

2021

CIML

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This paper describes an updated and efficient method for Hybrid spectrum sensing in cognitive radio (CR) system utilizing soft computing paradigms. The suggested soft computing approach utilizes an artificial neural network and for learning and decision making as a solution to the problems when a new product is subjected to the CR framework, developed the ability for unlicensed cognitive users to access radio frequencies through a spectrum hole and understand its implications through mechanisms spectrum sensing. The suggested soft computing approach could then be referred to as the Neuro technique. The need for higher bandwidth is important with the rise in the number of communication devices. Usage of cognitive radio for the fifth generation 5G communication network of the next generation Consider the fact that CR technology will efficiently optimize the use of much of the unused communication spectrum bands for the future 5G of wireless network and beyond. Keyword : Hybrid spectrum sensing, 5G Cognitive Radio (CR), ANN technique.

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“…The eigenvalues describe the amount of information recovered by the corresponding eigenvector and give the components in order of significance. In fact, it turns out that the eigenvector with the highest eigenvalue is the principle component of the data set [20]. The first principal component u 1 contains maximum information as its corresponding eigenvalue λ 1 is the maximum, the second principal component u 2 contains about the following maximum information as its corresponding eigenvalue λ 2 is the 2nd-maximum, etc.…”

Section: B Spectrum Reduction Dimension With Pcamentioning

confidence: 99%

“…. 10 is enough for high quality reconstruction of original spectral sets [20]. And the lossy information defined as reconstruction error J e is:…”

Section: B Spectrum Reduction Dimension With Pcamentioning

confidence: 99%

Computation of Atmospheric Optical Parameters Based on Deep Neural Network and PCA

et al. 2020

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The effect of the atmosphere on the propagating energy during the remote sensing imaging simulation is one of the most critical factors affecting the quality of image. The classical methods retrieving atmospheric optical parameters (AOP) have shortcomings in GPU-based imaging simulation application. This paper proposed a method based on deep neural networks (DNN) and principal component analysis (PCA) to compute AOP. Firstly, MODTRAN is employed to obtain large numbers of AOP as original spectrum set in different weather and observation geometry conditions. Then the dimension of original spectrum is reduced by PCA. Next, a DNN is constructed and trained using compressed spectral signatures. Finally, estimated AOP are obtained through inverse PCA by decompressing the output of DNN. The results show that original AOP and estimated AOP have a high spectral similarity which relative error is less than 2%. Compared with the classical methods, DNN can be used to accurately and fast compute AOP with any kind of conditions in remote sensing imaging applications, without consuming large of graphic memory.INDEX TERMS Atmospheric optical parameters, imaging simulation, deep neural networks, principal component analysis, atmospheric downward radiation.

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Neural Networks and PCA for Spectrum Sensing in the Context of Cognitive Radio

Cited by 3 publications

References 6 publications

Artificial Neural Networks, Support Vector Machine And Energy Detection For Spectrum Sensing Based On Real Signals

Artificial Neural Networks, Support Vector Machine And Energy Detection For Spectrum Sensing Based On Real Signals

Hybrid Spectrum Sensing Techniques in 5G Cognitive Radio Networks in Soft Computing: A Review

Computation of Atmospheric Optical Parameters Based on Deep Neural Network and PCA

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