Investigation and implementation of computationally-efficient algorithm for cyclic spectral analysis.

Tom, Caroline

doi:10.22215/etd/1995-03006

Cited by 3 publications

(2 citation statements)

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“…The CFD acts in a non-cooperative context [6] [7]. It does not require a prior knowledge of the characteristics of the PU signal and remains effective in a very low SNR channel.…”

Section: Introductionmentioning

confidence: 99%

Non-Cooperative Spectrum Sensing Based on Cyclostationary Model of Digital Signals in the Context of Cognitive Radio

Kadjo¹,

Agoua²,

Bamba³

et al. 2021

ENG

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This paper addresses the problem of the opportunistic spectrum access in Cognitive Radio. Indeed, most spectrum sensing algorithms suffer from a high computational cost to achieve the detection process. They need a prior knowledge of signal characteristics and present a bad performance in low Signal to Noise Ratio (SNR) environment. The choice of the optimal detection threshold is another issue for these spectrum sensing algorithms. To overcome the limits of spectrum detectors, we propose in this paper, a blind detection method based on the cyclostationary features of communication signals. Our detector evaluates the level of hidden periodicity contained in the observed signal to make decision on the state of a bandwidth. In order to reduce the computational cost, we take advantage of the FFT Accumulation Method to estimate the cyclic spectrum of the observed signal. Then, we generate the Cyclic Domain Profile of the cyclic spectrum which allows us to evaluate the level of the hidden periodicity in the signal. This level of periodicity is quantified through the crest factor of Cyclic Domain Profile, which represents the decision statistic of the proposed detector. We have established the analytic expression of the optimal threshold of the detection and the probability of detection to evaluate the performance of the proposed detector. Simulation results show that the proposed detector is able to detect the presence of a communication signal on a bandwidth in a very low SNR scenario.

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“…The CFD acts in a non-cooperative context [6] [7]. It does not require a prior knowledge of the characteristics of the PU signal and remains effective in a very low SNR channel.…”

Section: Introductionmentioning

confidence: 99%

Non-Cooperative Spectrum Sensing Based on Cyclostationary Model of Digital Signals in the Context of Cognitive Radio

Kadjo¹,

Agoua²,

Bamba³

et al. 2021

ENG

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“…The method of cyclostationary feature detection proposed initially by Gardner [9,10] models communication signals as cyclostationary signals [11]. Cyclostationary processes have periodical statistics and are random in nature [10].…”

Section: Introductionmentioning

confidence: 99%

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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