Spectrum Sensing Based on Nonparametric Autocorrelation in Wireless Communication Systems under Alpha Stable Noise

Chen, Riqing; Wang, Jun; Lin, Rong; Zhao, Xiangmo

doi:10.1155/2016/6753830

Cited by 4 publications

(4 citation statements)

References 12 publications

(16 reference statements)

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“…In other words, the farthest values from the center of the distribution will occur with a much higher probability in the non-Gaussian case than in the Gaussian one. Due to this heavy-tailed behavior, non-Gaussian alpha-stable distributions are commonly used in the modeling of channels subjected to impulsive noise of undetermined variance [26], [29], [52].…”

Section: Alpha-stable Distributionsmentioning

confidence: 99%

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Automatic Modulation Classification Architectures Based on Cyclostationary Features in Impulsive Environments

Camara

Lima

et al. 2019

IEEE Access

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Cyclostationary analysis has several applications in communications, e.g., spectral sensing, signal parameter estimation, and modulation classification. Most of them consider the additive white Gaussian noise (AWGN) channel model, although wireless communication systems may also be subject to non-Gaussian interference and impulsive noise. In this context, the communication channel can be better modeled by heavy-tailed distributions, such as the non-Gaussian alpha-stable one. Some applications of the cyclostationary approach based on the spatial sign cyclic correlation function (SSCCF), fractional lower-order cyclic autocorrelation function (FLOCAF), and cyclic correntropy function (CCF) demonstrate that these are promising solutions for the analysis of signals in the presence of impulsive non-Gaussian noise. However, the investigation of functions above applied to digital modulation recognition in impulsive environments, and the comparison among them are topics that did not adequately explore yet. This work demonstrates that SSCCF is a particular case of the FLOCAF. Besides, a detailed analysis of the use of the FLOCAF and CCF is presented to obtain cyclostationary descriptors for the recognition of digital modulations BPSK, QPSK, 8-QAM, 16-QAM, and 32-QAM. Automatic modulation classification (AMC) architectures, based on the functions mentioned above, are also proposed. Besides, another contribution showed is that both the FLOCAF and CCF allow the symbol rate parameter estimation. The performances of AMC architectures were evaluated in the scenario with modulated signals contaminated with additive non-Gaussian alpha-stable noise. The results demonstrate that both architectures can classify signals in different contamination scenarios. However, the architecture based on the CCF is more efficient than the FLOCAF-based one.

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Section: Alpha-stable Distributionsmentioning

confidence: 99%

“…Cyclostationary analysis allows the extraction of cyclic spectral features from communication signals, also known as cyclostationary signatures, and can be efficiently used in Gaussian environments for spectral sensing [22], [23], automatic modulation recognition [23]- [25], and estimation of signal parameters [20], [23], [26].…”

Section: Introductionmentioning

confidence: 99%

Automatic Modulation Classification Architectures Based on Cyclostationary Features in Impulsive Environments

Camara

Lima

et al. 2019

IEEE Access

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“…For example, variance and high-order statistics of signal components are unbounded under alpha-stable noise conditions. Based on this finite statistics, the statistics mentioned above would show degradation performance of state description [16,17].…”

Section: Introductionmentioning

confidence: 99%

A New Feature Extraction Method for Bearing Faults in Impulsive Noise Using Fractional Lower‐Order Statistics

2019

Shock and Vibration

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According to the performance degradation problem of feature extraction from higher-order statistics in the context of alpha-stable noise, a new feature extraction method is proposed. Firstly, the nonstationary vibration signal of rolling bearings is decomposed into several product functions by LMD to realize signal stability. Then, the distribution properties of product functions in the time domain are discussed by the comparison of heavy tails and characteristic exponent estimation. Fractional lower-order p-function optimization is obtained by the calculation of the distance ratio based on K-means algorithms. Finally, a fault feature dataset is established by the optimal FLOS and lower-dimensional mapping matrix of covariation to accurately and intuitively describe various bearing faults. Since the alpha-stable noise is effectively suppressed and state described precisely, the presented method has shown better performance than the traditional methods in bearing experiments via fractional lower-order feature extraction.

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“…To date, new methods of BOC signal recognition and parameter estimation have been proposed [7][8][9][10][11]. The detection methods are based on spectral correlation [7][8][9] and the methods for parameter estimation are based on autocorrelation [10,11]. The basis of the spectral correlation methods is based on the cyclostationary characteristic of the BOC signal, so that the parameters of the carrier, square wave, and pseudo random sequence can be estimated.…”

Section: Introductionmentioning

confidence: 99%

A Detection Algorithm for the BOC Signal Based on Quadrature Channel Correlation

Qian

Zheng

Feng

2018

Journal of Electrical and Computer Engineering

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In order to solve the problem of detecting a BOC signal, which uses a long-period pseudo random sequence, an algorithm is presented based on quadrature channel correlation. The quadrature channel correlation method eliminates the autocorrelation component of the carrier wave, allowing for the extraction of the absolute autocorrelation peaks of the BOC sequence. If the same lag difference and height difference exist for the adjacent peaks, the BOC signal can be detected effectively using a statistical analysis of the multiple autocorrelation peaks. The simulation results show that the interference of the carrier wave component is eliminated and the autocorrelation peaks of the BOC sequence are obtained effectively without demodulation. The BOC signal can be detected effectively when the SNR is greater than −12 dB. The detection ability can be improved further by increasing the number of sampling points. The higher the ratio of the square wave subcarrier speed to the pseudo random sequence speed is, the greater the detection ability is with a lower SNR. The algorithm presented in this paper is superior to the algorithm based on the spectral correlation.

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Spectrum Sensing Based on Nonparametric Autocorrelation in Wireless Communication Systems under Alpha Stable Noise

Cited by 4 publications

References 12 publications

Automatic Modulation Classification Architectures Based on Cyclostationary Features in Impulsive Environments

Automatic Modulation Classification Architectures Based on Cyclostationary Features in Impulsive Environments

A New Feature Extraction Method for Bearing Faults in Impulsive Noise Using Fractional Lower‐Order Statistics

A Detection Algorithm for the BOC Signal Based on Quadrature Channel Correlation

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