SVM-Based Spectrum Sensing in Cognitive Radio

Zhang, Dandan; Zhai, Xiao

doi:10.1109/wicom.2011.6040028

Cited by 28 publications

(20 citation statements)

References 3 publications

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“…Since [70], more recent works on the use of machine learning for spectrum sensing include [71] that used artificial neural network technique to detect primary user in low signal-to-noise ratio scenarios, [72] that used unsupervised learning to evolve the classifier in sensing with security countermeasures, [73] that used support vector machine to outperform energy detection, [74] that used unsupervised K-means clustering and Gaussian mixture model as well as supervised support vector machine and K-nearest neighbour for cooperative spectrum sensing, and [75] that also used support vector machine to detect weak primary user signals, to name a few. These works do not use measurement data for verification.…”

Section: Spectrum Occupancy Predictionmentioning

confidence: 99%

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Chen

2016

IEEE Commun. Surv. Tutorials

215

105

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Section: Spectrum Occupancy Predictionmentioning

confidence: 99%

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Chen

2016

IEEE Commun. Surv. Tutorials

215

105

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“…where ( )'s sampling period is denoted by , the data tapering window with a width = seconds is represented by ( ), and ( )'s complex demodulate is represented by the term ( , V+ /2). FAM, derived through (22), works by dividing the bifrequency plane (V, ) into small sections and then calculating the CS for each section.…”

Section: Fft Accumulation Methodsmentioning

confidence: 99%

“…To address the spectrum sensing task, recently, machine learning (ML) techniques have also been applied [22][23][24][25]. Spectrum sensing by employing support vector machine (SVM) is proposed in [22]. Spectrum sensing by using a combination of eigenvalue and SVM is proposed in [23].…”

Section: Introductionmentioning

confidence: 99%

“…None of the existing spectrum sensing techniques use ensemble machine learning as [17] used cyclostationary features and statistical test, [18] used FRESH filters, [19] incorporated hybrid algorithm involving statistics, [20] used energy and cyclostationary feature based learning algorithm, and [21] used a random variable based detection threshold. References [22][23][24][25] have used machine learning techniques such as SVM and neural networks for spectrum sensing but no kind of ensemble machine learning has been applied till date.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

Ahmad

2019

Wireless Communications and Mobile Computing

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Spectrum sensing is one of the most important and challenging tasks in cognitive radio. To develop methods of dynamic spectrum access, robust and efficient spectrum sensors are required. For most of these sensors, the main constraints are the lack of information about the primary user's (PU) signal, high computational cost, performance limits in low signal-to-noise ratio (SNR) conditions, and difficulty in finding a detection threshold. This paper proposes a machine learning based novel detection method to overcome these limits. To address the first constraint, detection is achieved using cyclostationary features. The constraints of low SNR, finding detection threshold, and computational cost are addressed by proposing an ensemble classifier. First, a dataset is generated containing different orthogonal frequency-division multiplexing signals at different SNRs. Then, cyclostationary features are extracted using FFT accumulation method. Finally, the proposed ensemble classifier has been trained using the extracted features to detect PU's signal in low SNR conditions. This ensemble classifier is based on decision trees and AdaBoost algorithm. A comparison of the proposed classifier with another machine learning classifier, namely, support vector machine (SVM), is presented, clearly showing that the ensemble classifier outperforms SVM. The results of the simulation also prove the robustness and superior efficiency of the detector proposed in this paper in comparison with a cyclostationary detector without machine learning as well as the classical energy detector.

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“…In the family of supervised learning techniques, the SVM-based and the weighted k-nearest neighbor-based classifiers were recommended due to high receiver operating characteristic performance and, in some applications, low training and classification time. Zhang and Zhai [29] presented a performance study comparing energy detector-based and SVM-based spectrum sensing considering a conventional two-class hypothesis, i.e., PU presence and absence. Hou et al [30] utilized a multi-layer perceptron network with backpropagation to improve throughput performance of a secondary user in a cognitive radio network.…”

Section: Related Workmentioning

confidence: 99%

Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

Jan

Koo

2018

Applied Sciences

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A framework of spectrum sensing with a multi-class hypothesis is proposed to maximize the achievable throughput in cognitive radio networks. The energy range of a sensing signal under the hypothesis that the primary user is absent (in a conventional two-class hypothesis) is further divided into quantized regions, whereas the hypothesis that the primary user is present is conserved. The non-radio frequency energy harvesting-equiped secondary user transmits, when the primary user is absent, with transmission power based on the hypothesis result (the energy level of the sensed signal) and the residual energy in the battery: the lower the energy of the received signal, the higher the transmission power, and vice versa. Conversely, the lower is the residual energy in the node, the lower is the transmission power. This technique increases the throughput of a secondary link by providing a higher number of transmission events, compared to the conventional two-class hypothesis. Furthermore, transmission with low power for higher energy levels in the sensed signal reduces the probability of interference with primary users if, for instance, detection was missed. The familiar machine learning algorithm known as a support vector machine (SVM) is used in a one-versus-rest approach to classify the input signal into predefined classes. The input signal to the SVM is composed of three statistical features extracted from the sensed signal and a number ranging from 0 to 100 representing the percentage of residual energy in the node's battery. To increase the generalization of the classifier, k-fold cross-validation is utilized in the training phase. The experimental results show that an SVM with the given features performs satisfactorily for all kernels, but an SVM with a polynomial kernel outperforms linear and radial-basis function kernels in terms of accuracy. Furthermore, the proposed multi-class hypothesis achieves higher throughput compared to the conventional two-class hypothesis for spectrum sensing in cognitive radio networks.

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SVM-Based Spectrum Sensing in Cognitive Radio

Cited by 28 publications

References 3 publications

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

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