Low-complexity cyclostationary-based modulation classifying algorithm

Rodríguez, Pedro Manuel Gómez; Fernandez, Zaloa; Torrego, Raúl; Lizeaga, Aitor; Mendicute, Mikel; Val, Iñaki

doi:10.1016/j.aeue.2017.02.008

Cited by 14 publications

(8 citation statements)

References 7 publications

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“…Additive white Gaussian noise (AWGN) can be completely mathematically eliminated in HOC features. Cyclostationary features are based on the spectral correlation function (SCF) derived from Fourier transform of the cyclic autocorrelation function [9,10]. The highest values of SCF for different cyclic frequencies are taken by the cyclic domain profile and used to train the classifiers.…”

Section: Introductionmentioning

confidence: 99%

A robust modulation classification method using convolutional neural networks

Zhou

Yin

et al. 2019

EURASIP J. Adv. Signal Process.

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Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized.

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Section: Introductionmentioning

confidence: 99%

A robust modulation classification method using convolutional neural networks

Zhou

Yin

et al. 2019

EURASIP J. Adv. Signal Process.

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“…All labels that are modulation classes are digitized i.e., a number was assigned to every individual class. Now the labels are transformed into a one-hot encoding vector [24]- [27].…”

Section: Methods 21 Data Preprocessingmentioning

confidence: 99%

Machine learning model to classify modulation techniques using robust convolution neural network

Indira

Rao

2022

IJEECS

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<p>In wireless c<span>communications receiver plays a main role to recognize modulation techniques which were used at the transmitter. While transferring information from transmitter to receiver, the receiver must retrieve original information. In order to achieve this goal we introduced a neural network architecture that recognizes the types of modulation techniques. The applications of deep learning can be categorized into classification and detection. The CNN architecture is used to perform feature extraction based on the layers to build a model which classifies the input data. A model that classifies the radio communication signals using deep learning method. The robust c (RCNN) is used to train the modulated signals; the transformations are used to help the neural network which estimate the signal to noise ratio of each signal ranges from -20dB to 18dB with loss and accuracy of 89.57% at SNR 0dB.</span></p>

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“…Mathematically using HOC features, it is possible to eliminate additive white Gaussian noise (AWGN). From the Fourier transform of the cyclic autocorrelation system, the spectral correlation function (SCF) based cyclo‐stationary characteristics are generated 13,14 . From the cyclic domain profile, for various cyclic frequencies, the maximum SCF values are taken and used to train the classifiers.…”

Section: Introductionmentioning

confidence: 99%

A software‐defined radio testbed for deep learning‐based automatic modulation classification

Ponnaluru

Satyanarayana

2020

Int J Communication

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Summary Automatic modulation classification (AMC) is the demodulation process on the receiver side, which is a crucial protocol for current and next‐generation intelligent communication systems. This method becomes complicated, in the presence of channel noise, to identify the modulation of the transmitted signal, that is, the transmitter and receiver with its ambiguous parameters like timing information, signal strength, phase offset, and carrier frequency. Two fundamental approaches are used for the AMC, namely, the signal statistical feature‐based approach and the maximum likelihood approach. Current Feature‐Based AMC approaches typically built for a limited set of modulation; a comprehensive AMC approach utilizing convolutional neural networks (CNN) is suggested in this article to overcome this obstacle. Altogether, 11 different types of modulations considered. In this method, without an extraction function, the transmitted signal can be identified directly. Also, the features of the received signal are known directly by using this method. The classification accuracy using CNN seems to be remarkable, especially for low SNRs. In this article, a realistic AMC framework that can be quickly applied to provide reliable efficiency in numerous commercial real‐time scenarios has developed and tested. Therefore, to prove the functional viability of our proposed model, it was applied to the software‐defined radio test‐bed.

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Low-complexity cyclostationary-based modulation classifying algorithm

Cited by 14 publications

References 7 publications

A robust modulation classification method using convolutional neural networks

A robust modulation classification method using convolutional neural networks

Machine learning model to classify modulation techniques using robust convolution neural network

A software‐defined radio testbed for deep learning‐based automatic modulation classification

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