Cyclostationary Detection Based on Non-cooperative spectrum sensing in cognitive radio network

Yawada, Prince Semba; Wei, An Jian

doi:10.1109/cyber.2016.7574819

Cited by 17 publications

(9 citation statements)

References 5 publications

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“…The state ℋ0 corresponds primary user absence and state ℋ1 corresponds primary user presence. For the sensing decision, several of the previously mentioned spectrum sensing techniques can be used, including energy detection [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], cyclostationary detection [21,22,23,24,25,26,27], matched filter detection [28,29,30,31], covariance-based detection [32,33,34,35,36,37,38,39], and machine-learning based detection [40,41,42,43,44,45,46,47,48,49,50,51] which are discussed below. These techniques are often evaluated using the probabilities of false alarm and probability of detection.…”

Section: Narrowband Spectrum Sensingmentioning

confidence: 99%

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A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

Arjoune

Kaabouch

2019

Sensors

330

196

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Cognitive radio technology has the potential to address the shortage of available radio spectrum by enabling dynamic spectrum access. Since its introduction, researchers have been working on enabling this innovative technology in managing the radio spectrum. As a result, this research field has been progressing at a rapid pace and significant advances have been made. To help researchers stay abreast of these advances, surveys and tutorial papers are strongly needed. Therefore, in this paper, we aimed to provide an in-depth survey on the most recent advances in spectrum sensing, covering its development from its inception to its current state and beyond. In addition, we highlight the efficiency and limitations of both narrowband and wideband spectrum sensing techniques as well as the challenges involved in their implementation. TV white spaces are also discussed in this paper as the first real application of cognitive radio. Last but by no means least, we discuss future research directions. This survey paper was designed in a way to help new researchers in the field to become familiar with the concepts of spectrum sensing, compressive sensing, and machine learning, all of which are the enabling technologies of the future networks, yet to help researchers further improve the efficiently of spectrum sensing.

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Section: Narrowband Spectrum Sensingmentioning

confidence: 99%

“…However, it cannot distinguish between the noise samples and the signal samples, which makes it subject to high uncertainty. In addition, it has a low detection performance for low signal-to-noise (SNR) values [21,26,28].…”

Section: Narrowband Spectrum Sensingmentioning

confidence: 99%

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

Arjoune

Kaabouch

2019

Sensors

330

196

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“…At the same time, different signals often take different features of cyclostationarity due to their different periodic parameters, or more precisely, their different cycle frequencies [2]. On the other hand, the cycle frequencies of a cyclostationary signal can be used to detect the presence of the signal, or to discriminate the signal from noise or other interfering signal for the purpose of signal identification and classification as used in cognitive radio [3][4][5] or cognitive positioning system [6], [7], and so on. Moreover, in the applications of frequency-shift (FRESH) filtering for spatial-time or time-frequency overlapped signals [8][9][10], the difference in cycle frequencies of different signals can also be used to separate the overlapped signal of interest (SOI) from other signals.…”

Section: Introductionmentioning

confidence: 99%

Efficient Cycle Frequency Acquisition of a Cyclostationary Signal with the FACA Method

Hu¹,

Yu²,

Deng³

et al. 2019

RADIOENGINEERING

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The cycle frequencies of a cyclostationary signal can be used for the signal identification and classification, separation of the overlapped signals in cycle domain, and so on. Efficient cycle frequency acquisition depends on the fast measurement of cyclic autocorrelation function (CAF) or spectral correlation function (SCF) of the signal. Presently the relative efficient CAF and SCF measuring methods mainly include the cyclic correlogram, the well-known fast Fourier transform accumulation method (FAM), and so on. Motivated by these methods, a new efficient cycle frequency acquisition method which integrates the fast Fourier transform (FFT) algorithm with the autocorrelated cyclic autocorrelation function, named FACA, is presented. With the presented method, we can acquire the cycle frequencies of a cyclostationary signal more efficiently with a given level of reliability. Meanwhile, by enlarging the FFT window width of the FACA method we can get the same cycle frequency resolution as the benchmarked method FAM, but the computation cost still can be spared at this case.

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“…The energy detection method does not depend on the prior information of the received signal. At a low SNR, the performance of the energy detection method is poor [19].…”

Section: Energy Detectormentioning

confidence: 99%

“…In some cases, the PU signal includes data redundancy to get more protection against noise ambiguity at the receiver [21]. The method of detecting PU using the cyclic redundancy in the received signal is called cyclostationary detection [19]. As shown in Fig.…”

Section: Cyclostationary Detectormentioning

confidence: 99%

A Framework to Analyze Energy Efficiency of Multi-Band Spectrum Sensing Algorithms

Patel¹

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A FRAMEWORK TO ANALYZE ENERGY EFFICIENCY OF MULTI-BAND SPECTRUM SENSING ALGORITHMS by Jay B. Patel Cognitive radio (CR) is a device which can detect wireless communication channels that are not in use and adapt its parameters intelligently. Networks with CRs use the available frequency bands much more efficiently and hence have higher data rates compare to traditional radios. Spectrum sensing is the class of techniques used by CRs to understand its wireless environment. Recent research on evaluating multi-band spectrum sensing algorithms is limited to only algorithm complexity and optimization; therefore, the primary goal of the study is to devise a novel framework that analyzes a multi-band spectrum sensing algorithm in terms of energy consumption and algorithm efficiency. The proposed structure leads to a comparison and evaluation of a large class of multi-band spectrum sensing algorithms. Multi-band spectrum sensing search methods such as linear, random and binary are evaluated for energy loss and detection performance using the proposed framework. ACKNOWLEDGMENTS I want to thank my advisor, Dr. Birsen Sirkeci, for her outstanding support, dedication, and patience throughout the research.

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Cyclostationary Detection Based on Non-cooperative spectrum sensing in cognitive radio network

Cited by 17 publications

References 5 publications

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

Efficient Cycle Frequency Acquisition of a Cyclostationary Signal with the FACA Method

A Framework to Analyze Energy Efficiency of Multi-Band Spectrum Sensing Algorithms

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