Autocorrelation-Based Spectrum Sensing for Cognitive Radios

Naraghi-Pour, Mort; Ikuma, Takeshi

doi:10.1109/tvt.2009.2035628

Cited by 87 publications

(49 citation statements)

References 20 publications

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“…The performance of the proposed algorithm is also compared with those from the energy detector, the covariance detector, and the cyclicautocorrelation detector. They show results that algorithm outperforms the covariance detector and the cyclic autocorrelation detector [10].…”

Section: Literature Surveymentioning

confidence: 97%

Performance Analysis of Cyclostationary Spectrum Sensing Over Different Fading Channels

Bhandari¹,

Singh²

2015

IJCA

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In research area of wireless communication, cognitive radio gets more endearment in recent times. The main motive behind the use of cognitive radio is to sense the available spectrum, which is very limited, for the users who wish to use it for the transmission purpose. The users can be primary or secondary, based on, whether they are licensed or unlicensed. Different goals of cognitive radio include spectrum sensing, spectrum sharing, spectrum management, and spectrum mobility. Spectrum sensing plays vital role in the cognitive radio system since it is used to detect signal presence on the air. This paper signifies the role of Cyclostationary Spectrum Sensing technique to define a device capable of detecting OFDM signals in a noisy environment. The work has been done for the applications employed in high frequency, such as, Wi-Fi and WiMAX.

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Section: Literature Surveymentioning

confidence: 97%

Performance Analysis of Cyclostationary Spectrum Sensing Over Different Fading Channels

Bhandari¹,

Singh²

2015

IJCA

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“…In this context, authors in [112] propose a simple Correlation Sum (CorrSum) detector exploiting both energy and correlation parameters for the improved sensing performance assuming that correlation is real and extend to the scenario with the knowledge of correlation distribution information in [113]. Further, a CFAR detection algorithm has been studied in [114] using the estimated autocorrelation of the received signal and its performance is shown to be better than the covariance detector and the cyclic autocorrelation detector.…”

Section: B Autocorrelation Based Detectormentioning

confidence: 99%

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Sharma

Bogale

Chatzinotas

et al. 2015

IEEE Commun. Surv. Tutorials

196

111

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Abstract-Cognitive Radio (CR) has been considered as a potential candidate for addressing the spectrum scarcity problem of future wireless networks. Since its conception, several researchers, academic institutions, industries, regulatory and standardization bodies have put their significant efforts towards the realization of CR technology. However, as this technology adapts its transmission based on the surrounding radio environment, several practical issues may need to be considered. In practice, several imperfections such as noise uncertainty, channel/interference uncertainty, transceiver hardware imperfections, signal uncertainty, synchronization issues, etc. may severely deteriorate the performance of a CR system. To this end, the investigation of realistic solutions towards combating various practical imperfections is very important for successful implementation of the cognitive technology. In this direction, first, this survey paper provides an overview of the enabling techniques for CR communications. Subsequently, it discusses the main imperfections that may occur in the most widely used CR paradigms and then reviews the existing approaches towards addressing these imperfections. Finally, it provides some interesting open research issues.

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“…Although energy and cyclostationary detectors are widely used in the field of spectrum sensing, various other methods are also proposed [14][15][16]. The goodness of fit (GoF) algorithm introduced in [14] compares the empirical distribution of the received samples to a known distribution of the noise (when PU is idle).…”

Section: Introductionmentioning

confidence: 99%

“…By assuming the oversampling aspect of the baseband received signal (i.e., number of samples per symbols N s ≥ 2), the autocorrelation for a non-zero lag only vanishes when the PU signal is absent and the channel is only occupied by a white noise [16]. The corresponding test statistic combines linearly the autocorrelation measures for different non-zero lags before making a decision on the PU status.…”

Section: Introductionmentioning

confidence: 99%

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Spectrum sensing based on cumulative power spectral density

Nasser

Mansour

Yao

et al. 2017

EURASIP J. Adv. Signal Process.

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This paper presents new spectrum sensing algorithms based on the cumulative power spectral density (CPSD). The proposed detectors examine the CPSD of the received signal to make a decision on the absence/presence of the primary user (PU) signal. Those detectors require the whiteness of the noise in the band of interest. The false alarm and detection probabilities are derived analytically and simulated under Gaussian and Rayleigh fading channels. Our proposed detectors present better performance than the energy (ED) or the cyclostationary detectors (CSD). Moreover, in the presence of noise uncertainty (NU), they are shown to provide more robustness than ED, with less performance loss. In order to neglect the NU, we modified our algorithms to be independent from the noise variance.

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Autocorrelation-Based Spectrum Sensing for Cognitive Radios

Cited by 87 publications

References 20 publications

Performance Analysis of Cyclostationary Spectrum Sensing Over Different Fading Channels

Performance Analysis of Cyclostationary Spectrum Sensing Over Different Fading Channels

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Spectrum sensing based on cumulative power spectral density

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