Performance evaluation of the correntropy coefficient in automatic modulation classification

Fontes, Aluisio I. R.; Martins, Allan de Medeiros; Silveira, Luiz F. Q.; Prı́ncipe, José C.

doi:10.1016/j.eswa.2014.07.023

Cited by 34 publications

(23 citation statements)

References 19 publications

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“…Nowadays, correntropy has been successfully used in a wide variety of applications where the signals are non-Gaussian or nonlinear, for example, automatic modulation classification [39], classification systems of pathological voices [40], and principal component analysis (PCA) [41].…”

Section: Correntropymentioning

confidence: 99%

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Linhares

Fontes

Martins

et al. 2015

Mathematical Problems in Engineering

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Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs) are an efficient tool to identify nonlinear systems. In these structures, features related to fuzzy logic, wavelet functions, and neural networks are combined in an architecture similar to the Adaptive Neurofuzzy Inference Systems (ANFIS). In practical applications, the experimental data set used in the identification task often contains unknown noise and outliers, which decrease the FWNN model reliability. In order to reduce the negative effects of these erroneous measurements, this work proposes the direct use of a similarity measure based on information theory in the FWNN learning procedure. The Mean Squared Error (MSE) cost function is replaced by the Maximum Correntropy Criterion (MCC) in the traditional error backpropagation (BP) algorithm. The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. The results demonstrate the advantages of the proposed cost function using the MCC as compared to the MSE. This work also investigates the influence of the kernel size on the performance of the MCC in the BP algorithm, since it is the only free parameter of correntropy.

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Section: Correntropymentioning

confidence: 99%

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Linhares

Fontes

Martins

et al. 2015

Mathematical Problems in Engineering

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“…The correntropy consists in a similarity measure based on Rényi entropy capable of extracting high-order statistical information from real-valued data [1]. This is why it has been widely used as a cost function in optimization problems such as adaptive filtering in an approach called maximum correntropy criterion (MCC), thus providing better performance than second-order methods in non-Gaussian noise environments [2][3][4][5][6]. Recently, the correntropy concept has been extended to complex-valued random variables using the maximum complex correntropy criterion (MCCC) [7,8].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of the maximum complex correntropy criterion with adaptive kernel width update

Aquino

Guimarães

Linhares

et al. 2019

EURASIP J. Adv. Signal Process.

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The complex correntropy is a recently defined similarity measure that extends the advantages of conventional correntropy to complex-valued data. As in the real-valued case, the maximum complex correntropy criterion (MCCC) employs a free parameter called kernel width, which affects the convergence rate, robustness, and steady-state performance of the method. However, determining the optimal value for such parameter is not always a trivial task. Within this context, several works have introduced adaptive kernel width algorithms to deal with this free parameter, but such solutions must be updated to manipulate complex-valued data. This work reviews and updates the most recent adaptive kernel width algorithms so that they become capable of dealing with complex-valued data using the complex correntropy. Besides that, a novel gradient-based solution is introduced to the Gaussian kernel and its respective convergence analysis. Simulations compare the performance of adaptive kernel width algorithms with different fixed kernel sizes in an impulsive noise environment. The results show that the iterative kernel adjustment improves the performance of the gradient solution for complex-valued data.

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“…Modulation recognition is the technology which can determine the modulation by analyzing the received signal in the premise of unknown signal modulation (or only a little known priori information). Modulation recognition technology has been widely used in the communication field [1][2][3][4][5], such as software radio platform and electronic warfare technology.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Modulation Recognition Method of QPSK Signal

Feng

Zhenyu

Meng

et al. 2015

Mathematical Problems in Engineering

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Quadrature phase shift keying (QPSK) modulation has been widely applied in communication systems. With the increasing development of QPSK modulation recognition, it is meaningful for ensuring validity and accuracy of recognition method. Therefore, the method of signal recognition has been presented based on the features of QPSK modulated signal, in which the feature parameters of QPSK signal are extracted. Besides, signal classification is fulfilled through thresholds, and modulation recognition is completed with quartic spectrum. The simulation results show that, the method can recognize QPSK modulation effectively in less sample space condition and can be more accurate.

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Performance evaluation of the correntropy coefficient in automatic modulation classification

Cited by 34 publications

References 19 publications

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Performance evaluation of the maximum complex correntropy criterion with adaptive kernel width update

An Accurate Modulation Recognition Method of QPSK Signal

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