Spectrum Sensing Based on Higher Order Cumulants and Kurtosis Statistics Tests in Cognitive Radio

Bozovic, R.; Simić, Mirjana

doi:10.13164/re.2019.0464

Cited by 10 publications

(11 citation statements)

References 28 publications

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“…, solving for the algebra of P f , the new threshold value is calculated as (13) where erfc(•) is the inverse of the well-known error function. In the same way, if the detection variable is greater than the threshold, the algorithm decides for H 1 ; otherwise, the choice is for H 0 .…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Step3: Result of Step2 is used as training data to train and save the training model. Step4: Calculate the threshold using (13), if the detection function is 1, the primary user is detected; if the detection function is − 1, the primary user is not exist. Generate historical data with obtained values.…”

Section: Simulation Setting In This Section a Variety Of Montementioning

confidence: 99%

“…Following the ideas in [11], spectrum sensing and sharing based on cyclic prefix autocorrelation have been proposed based on a low-order matrix of the received signal [12]. In [13], based on higher order cumulants and kurtosis spectrum-sensing methods are proposed, in a real-world communication channel, simulation results have verified improvement of the performances. All of these algorithms aim to reduce the computational complexity and shorten the average detection time.…”

Section: Introductionmentioning

confidence: 99%

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A High-Precision Spectrum-Detection Algorithm Based on the Normalized Variance of Nonreconstruction Compression Sensing

Shen

Wang

2020

Mathematical Problems in Engineering

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The traditional energy detection algorithm has been widely used in the field of signal detection, and a variety of improved algorithms have been derived. In the case of low signal-to-noise ratio, existing methods have shortcomings on achieving fast and accurate spectrum sensing that need to be resolved. This work proposes a normalized-variance-detection method based on compression sensing measurements of received signal. The discrete cosine transform sensing matrix is used to compress the signal, whose normalized variance is then calculated before being used as the testing variable for detecting the primary user signal. Taking the detection results as historical data into consideration, the classification model is obtained after training by applying a support vector machine for classifying and predicting test signals. Simulation results show that the proposed method outperforms the current state-of-the-art approaches by achieving faster and more accurate spectrum occupancy decisions.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Simulation Setting In This Section a Variety Of Montementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A High-Precision Spectrum-Detection Algorithm Based on the Normalized Variance of Nonreconstruction Compression Sensing

Shen

Wang

2020

Mathematical Problems in Engineering

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“…Method described in [21], which uses a fundamentally different basis of detection, is a benchmark adopted in the conducted research. The research covers the most frequently referenced GoF tests, indicated as those with high efficiency and superiority to other similar, i.e., Jarque-Bera (JB) [22]- [24] and higher-order statistics (HoS) [25], [26].…”

Section: Introductionmentioning

confidence: 99%

Gaussianity Testing as an Effective Technique for Detecting Discontinuous Transmission in 5G Networks

Nikonowicz

Jessa

2021

IEEE Access

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The paper investigates statistical distribution testing-based detection methods in an intermittent signal detection scenario. The relevance of the research is driven by 5G networks based on packet transmission, incorporating the concept of cognitive radio and adapting spectrum detection methods from Long Term Evolution (LTE) Licensed-Assisted Access (LAA). The conducted study refers to the recently proposed methods based on testing goodness-of-fit (GoF) of statistical distributions, which are compared with a conventional energy detector. The authors examine the applicability of well-known GoF methods in intermittent transmission, as they require reconsideration in 5G communication systems, and investigate the behavior of the innovative energy-based GoF. The experiments are carried out for different transmitter activity factors, i.e., channel occupancy and signal-to-noise ratio (SNR), demonstrating the superiority of the GoF-based methods in general and particularly the invented GoF test over other energy-based detectors for discontinuous signals detection. INDEX TERMSGaussianity testing, energy detection, discontinuous transmission.

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“…In general, non-Gaussian signals are processed based on higher-order statistics (HOS) or fractional lower-order statistics (FLOS) [9][10][11][12]. If these signals are incorrectly assumed to satisfy Gaussian distribution, the filter will perform poorly or cease to be effective [13], because these signals obey α-stable distribution rather than Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Filtering Algorithm for Non-Gaussian Signals in Alpha-Stable Distribution

Yang¹

2020

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Currently, many adaptive filtering algorithms are available for the non-Gaussian environment, namely, least mean square (LMS) algorithm, recursive least square (RLS) algorithm, least mean fourth (LMF) algorithm, and subspace minimum norm (SMN) algorithm. Most of them can converge to the steady-state, but face various constraints in the presence of alpha (α)-stable noises. To solve the problem, this paper aims to develop an adaptive filtering algorithm for non-Gaussian signals in α-stable distribution, drawing on the merits of existing adaptive filtering algorithms. Firstly, the authors introduced the theory of α-stable distribution, the central limit theorem and fractional lower-order statistics (FLOS). Next, two classic adaptive filtering algorithms, RLS and LMS, were summarized, and compared through tests. On this basis, the FLOS-SMN algorithm was designed in the light of the features of the LMS and the SMN, which applies to the filtering of non-Gaussian signals in αstable distribution. Finally, the proposed algorithm was proved as faster, more stable and more adaptable than the traditional method.

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Spectrum Sensing Based on Higher Order Cumulants and Kurtosis Statistics Tests in Cognitive Radio

Cited by 10 publications

References 28 publications

A High-Precision Spectrum-Detection Algorithm Based on the Normalized Variance of Nonreconstruction Compression Sensing

A High-Precision Spectrum-Detection Algorithm Based on the Normalized Variance of Nonreconstruction Compression Sensing

Gaussianity Testing as an Effective Technique for Detecting Discontinuous Transmission in 5G Networks

An Adaptive Filtering Algorithm for Non-Gaussian Signals in Alpha-Stable Distribution

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