Performance evaluation of Welch's periodogram‐based energy detection for spectrum sensing

Martínez, Daniela M.; Andrade, Ángel G.

doi:10.1049/iet-com.2012.0640

Cited by 26 publications

(16 citation statements)

References 17 publications

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“…Welch's algorithm is a modified periodogram. The principle of the Welch algorithm is to divide the data sequence into segments to reduce the large fluctuations of the periodogram . For instance, a signal s(n) is segmented into M segments in the time domain with length L for each segment.…”

Section: Spectrum Sensing Algorithmsmentioning

confidence: 99%

“…Most conventional energy detection methods adopt a fixed decision threshold to distinguish PU signals from the noise. For example, a predefined experimental threshold was set in a previous study by measuring the noise power. However, it is difficult to guarantee the detection and false alarm probability with the fixed threshold setting method, especially when the noise power fluctuates .…”

Section: Introductionmentioning

confidence: 99%

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Energy detection–based spectrum sensing with constraint region in cognitive LTE systems

Wang

Gao

Yang

et al. 2017

Trans Emerging Tel Tech

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Cognitive LTE system, such as LTE-U, has received tremendous interest from almost all research disciplines in wireless communications. In Cognitive LTE systems, spectrum sensing is the key enabling technology to realize dynamic spectrum access. In this paper, an error decision probability is proposed to solve the trade-off problem between detection and false alarm probability with the constraint region requirement. The closed-form expression for the error decision probability, satisfied Signal-to-Noise ratio value, number of samples, and primary users' spectrum utilization ratio are derived for in both fixed and adaptive threshold setting algorithms. By implementing both Welch and wavelet-based energy detectors, the adaptive threshold setting algorithm demonstrates a more reliable and robust sensing result for both primary users and secondary users in comparison with the conventional fixed one. Furthermore, the wavelet denoising method is applied to improve the sensing performance when there are insufficient number of samples.

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Section: Spectrum Sensing Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy detection–based spectrum sensing with constraint region in cognitive LTE systems

Wang

Gao

Yang

et al. 2017

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…The Neyman-Pearson criterion states that it is possible to construct a decision rule that maximizes P D , constrained to a specific value of P FA [27]. It can be noted from Eqn (9) that the SU needs to know σ 2 s and h S to maximize the P D .…”

Section: Energy Detection With Noise Power Estimationmentioning

confidence: 99%

Reducing the effects of the noise uncertainty in energy detectors for cognitive radio networks

Martínez¹,

Andrade²

2014

Int J Communication

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SUMMARYBecause of its ease of implementation and minimum requirements about the primary signals' information, energy detection is broadly considered for signal detection in spectrum sensing algorithms. However, the noise uncertainty phenomenon, caused by the random variations in the noise power, degrades the performance of an energy detector, particularly when the signal-to-noise ratio (SNR) is low. In this work, we propose to reduce the negative effects of the noise uncertainty in the performance of an energy detector by dynamically adapting its detection threshold to the noise conditions experienced at each sensing epoch. The noise power is estimated from the received signal samples using an algorithm based on a high-pass filters bank and median filtering. With our proposal, it is possible to maintain a constant and low false alarm rate in the presence of noise uncertainty, without increasing the probability of misdetection, even in the low SNR regime, and without increasing the number of samples considered for spectrum sensing.

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“…Therefore, ED requires an accurate estimation of the noise variance; otherwise, we can face a SNR-wall problem, where the ED becomes incapable to made a robust decision about the PU status, even with a very large observation time [5,6]. However, many studies have been proposed to enhance the energy detector (ED) performance and to overcome its limitations [5,[7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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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Performance evaluation of Welch's periodogram‐based energy detection for spectrum sensing

Cited by 26 publications

References 17 publications

Energy detection–based spectrum sensing with constraint region in cognitive LTE systems

Energy detection–based spectrum sensing with constraint region in cognitive LTE systems

Reducing the effects of the noise uncertainty in energy detectors for cognitive radio networks

Spectrum sensing based on cumulative power spectral density

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