An expert Discrete Wavelet Adaptive Network Based Fuzzy Inference System for digital modulation recognition

Avcı, Engin; Hanbay, Davut; Varol, Asaf

doi:10.1016/j.eswa.2006.06.001

Cited by 79 publications

(40 citation statements)

References 35 publications

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“…Pattern recognition approaches, however, do not need such careful treatment [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. They are easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Blind signal-type classification using a novel robust feature subset selection method and neural network classifier

Shermeh

Azimi

2010

Ann. Telecommun.

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Automatic modulation recognition plays an important role for many novel computer and communication technologies. Most of the proposed systems can only identify a few kinds of digital signal and/or low order of them. They usually require high levels of signal-to-noise ratio. In this paper, we present a novel hybrid intelligent system that automatically recognizes a variety of digital signals. In this recognizer, a multilayer perceptron neural network with resilient back propagation learning algorithm is proposed as the classifier. For the first time, a combination set of spectral features and higher order moments up to eighth and higher order cumulants up to eighth are proposed as the effective features. Then we have optimized the classifier design by bees algorithm (BA) for selection of the best features that are fed to the classifier. This optimization method is new for this area. Simulation results show that the proposed technique has very high recognition accuracy with seven features selected by BA.

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“…Pattern recognition approaches, however, do not need such careful treatment [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. They are easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Blind signal-type classification using a novel robust feature subset selection method and neural network classifier

Shermeh

Azimi

2010

Ann. Telecommun.

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“…This method reaches the great the recognition effect. But with the increasingly complex signal environment, it is a huge challenge in how to make innovation in the basis of current recognition technology, and promote classification ability under the situation of the serious interference of white Gaussian noise and phase-frequency distortion and time delay et al [9]. Therefore, stationary characteristic of first-order cyclic is applied and the algorithm of mean value of the first-order cyclic in this paper to be used in the modulation classification recognition of band signal of VHF.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for VHF Signal Modulation Classification Based on Algorithm of First-Order Cyclic Moment

Yang¹,

Luo²,

Li³

et al. 2015

Proceedings of the International Conference on Computer Information Systems and Industrial Applications

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Abstract--In view of the problem of modulation recognition algorithm existing about low recognition rate under environment condition of low SNR, the algorithm of first-order cyclic moment are first presented used in band signal of VHF modulation classification recognition in this paper and the recognition rate increased significantly. The first-order cycle frequencies at which estimated first-order cyclic moment magnitudes exceed the cutoff value were selected as candidate cycle frequencies based on estimation first-order cyclic moment at first-order cycle frequency series, then decided to choose the number of candidate cycle frequency by a cyclostationarity test to realize signal modulation classification recognition, the classification recognition rate can reach more than 98% under condition of SNR is greater than -6 dB.

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“…This block is followed by an AMC which contains a feature extractor and a classifier.An overwhelming number of proposed features exist in AMC literature. Some of the most popular features include instantaneous amplitude, phase, and frequency [7][8][9], statistical features such as higher order moments and cumulants [10][11][12][13], wavelets [14][15][16][17][18], spectral peaks [19], etc. The classifier block makes use of extracted features to identify signal modulation by applying a fixed threshold, or alternatively using a pattern recognition technique, such as artificial neural networks [20][21][22] or support vector machines [18].…”

Section: Introductionmentioning

confidence: 99%

Automatic modulation classification of digital modulations in presence of HF noise

Hazza

Shoaib

Alshebeili

et al. 2012

EURASIP J. Adv. Signal Process.

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Designing an automatic modulation classifier (AMC) for high frequency (HF) band is a research challenge. This is due to the recent observation that noise distribution in HF band is changing over time. Existing AMCs are often designed for one type of noise distribution, e.g., additive white Gaussian noise. This means their performance is severely compromised in the presence of HF noise. Therefore, an AMC capable of mitigating the time-varying nature of HF noise is required. This article presents a robust AMC method for the classification of FSK, PSK, OQPSK, QAM, and amplitude-phase shift keying modulations in presence of HF noise using feature-based methods. Here, extracted features are insensitive to symbol synchronization and carrier frequency and phase offsets. The proposed AMC method is simple to implement as it uses decision-tree approach with pre-computed thresholds for signal classification. In addition, it is capable to classify type and order of modulation in both Gaussian and non-Gaussian environments.

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An expert Discrete Wavelet Adaptive Network Based Fuzzy Inference System for digital modulation recognition

Cited by 79 publications

References 35 publications

Blind signal-type classification using a novel robust feature subset selection method and neural network classifier

Blind signal-type classification using a novel robust feature subset selection method and neural network classifier

A Novel Method for VHF Signal Modulation Classification Based on Algorithm of First-Order Cyclic Moment

Automatic modulation classification of digital modulations in presence of HF noise

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