Type of modulation identification using Wavelet Transform and Neural Network

Walenczykowska, Marta; Kawalec, A.

doi:10.1515/bpasts-2016-0028

Cited by 25 publications

(29 citation statements)

References 16 publications

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“…The histogram peaks in WT magnitude, mean, and variance of normalized histogram are utilized for identifying the type of digital modulation [71]. In [72], the author analyzed the CWT instantaneous features (mean, variance, and central moment values) for recognizing M-ASK, M-PSK, M-FSK, M-QAM, OOK, and MSK. In [73], the histogram peaks in WT magnitude, mean, and HOM of normalized histogram were adopted as features for digital modulation classification.…”

Section: Transform Features For Mrmentioning

confidence: 99%

“…Optimization techniques for enhancing the learning process and reducing the complexity are reported to be helpful. In [72,94], principal component analysis (PCA) was used to reduce the complexity of ANNs and improve the classification accuracy at low SNR. BPNN was used in conjunction with reduced feature vectors through PCA to reduce training time and computational complexity and obtain higher-order MR at low SNR.…”

Section: Annmentioning

confidence: 99%

“…This method can remove the redundant features from the training samples, and simplifies the structure of the ANN classifier. In [72,94], the features subset selection using principal component analysis (PCA) is used to reduce the complexity of the used neural network through the selection of the best features.…”

Section: Complexity Analysis For Classifiersmentioning

confidence: 99%

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Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

et al. 2019

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The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.

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Section: Transform Features For Mrmentioning

confidence: 99%

Section: Annmentioning

confidence: 99%

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Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

et al. 2019

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“…Neural networks were often used for classification problems [53][54][55][56][57][58]. Statistical data analysis was also described [59].…”

Section: Bayes Classifiermentioning

confidence: 99%

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Głowacz¹,

Głowacz²,

Głowacz³

et al. 2017

Archives of Metallurgy and Materials

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A degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies -Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

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“…To enhance ANN classification accuracy, ANN is usually combined with the extracted features from the received signal, which allows the engine to have the capability to identify the modulation scheme at low SNR levels. Cyclic spectral analysis [17], wavelet cyclic features [21], temporal feature-based modulation [22,23], carrier frequency and baud rate [24], and continuous wavelet transform (CWT) [25] are some examples of these features. Dahap et al [26] proposed a digital recognition algorithm that uses six features extracted from instantaneous information and signal spectrum to discriminate between different modulated signals.…”

Section: Related Workmentioning

confidence: 99%

Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network

Alzaq

Ustundag

2017

J Wireless Com Network

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A pattern-based cognitive communication system (PBCCS) that optimizes non-periodic RF waveforms for security applications is proposed. PBCCS is a cross-layer approach that merges the channel encoding and modulation. The transmitter encodes sequences of bits into continuous signal patterns by selecting the proper symbol glossaries. The cognitive receiver preprocesses the received signal by extracting a limited set of wavelet features. The extracted features are fed into an artificial neural network (ANN) to recover the digital data carried by the distorted symbol. The PBCCS system offers a flexible management for robustness against a high noise level and increases the spectral efficiency. In this study, the spectral efficiency and robustness of a PBCCS scheme for an additive white Gaussian noise (AWGN) channel is investigated. The results show that at an SNR of −5 dB, a 3-bit glossary achieves a bit error rate (BER) of 10 −5 . Also, the link spectral efficiency (LSE) of the proposed system is 2.61 bps/Hz.

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Type of modulation identification using Wavelet Transform and Neural Network

Cited by 25 publications

References 16 publications

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network

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