Blind detection of cyclostationary features in the context of Cognitive Radio

Kadjo, Jean-Marie; Yao, Koffi Clément; Mansour, Ali

doi:10.1109/isspit.2016.7886025

Cited by 11 publications

(12 citation statements)

References 19 publications

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“…To compare the proposed ensemble classifier based detector (ECD) with non-machine-learning detectors, a cyclostationary detector and energy detector are considered. The crest factor based cyclostationary detector (CFCD) compared here is presented by [21]. This detector uses crest factor for threshold calculation.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…If, for a signal ( ), the discrete time version is taken as [ ], the estimation of CS takes the form [21]…”

Section: Fft Accumulation Methodsmentioning

confidence: 99%

“…In [20], a joint energy and cyclostationary method is proposed for blind spectrum sensing. In [21], another cyclostationary approach is presented for blind signal detection. This approach uses a crest factor to obtain a variable threshold.…”

Section: Introductionmentioning

confidence: 99%

“…These weak learners are computationally inexpensive and thus offer an advantage over complex learning techniques. 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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Section: Simulation Resultsmentioning

confidence: 99%

“…If, for a signal ( ), the discrete time version is taken as [ ], the estimation of CS takes the form [21]…”

Section: Fft Accumulation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…Signal detection and preprocessing for signal classification using cycle frequency domain profile is discussed in [10]. A FPGA implementation of a CFD using decimation to control detection time and improve the probability of detection is discussed in [11], assuming test statistic to be χ 2 2 distributed and hypothesis tests are performed with known cyclic frequencies. Frequency Shift (FRESH) filters are used in [12] to enable SS at low SNR by estimating a cyclostationary signal using its spectral coherence properties.…”

Section: Introductionmentioning

confidence: 99%

Cyclostationary Feature Detection Based Blind Approach for Spectrum Sensing and Classification

George¹,

Prema²

2019

RADIOENGINEERING

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A Spectrum Sensing (SS) device, regardless of its location, should be able to detect the presence of signal over noise. In certain applications, SS should be able to correctly identify and classify the received signal. These functions are to be performed with little or no prior information about the incoming signal or channel noise. Cyclostationary Feature Detection (CFD) can be used to detect primary users (PU) using periodicity in autocorrelation of the modulated signals. These algorithms attempt to differentiate signal from noise based on the uncorrelated nature of noise. CFD is often considered as a semi-blind approach, since it requires prior information about the PU signal for detection. For identification and classification of PU signal, existing algorithms make use of CFD and neural networks. This paper proposes a novel algorithm to obtain completely blind detection performance based on CFD. Classification of PU signals is based on the basic statistics regarding cyclic spectrum. Further, an algorithm is formulated to identify modulation scheme of the signal and classify it without making use of any training algorithms. The proposed approach is capable of detecting PU reliably for SNR as low as-8 dB with no prior information about PU or noise in the channel.

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A Narrowband Interference Detection Technique Based on Spectral Autocoherence Function

Chen

Yang

2019

2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

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Blind detection of cyclostationary features in the context of Cognitive Radio

Cited by 11 publications

References 19 publications

Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

Cyclostationary Feature Detection Based Blind Approach for Spectrum Sensing and Classification

A Narrowband Interference Detection Technique Based on Spectral Autocoherence Function

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