New Approach of Automatic Modulation Classification based on in Phase-Quadrature Diagram Combined with Artificial Neural Network

Gouho, Jean Baptiste Bi; Melèdje, Désiré; Aka, Boko; Babri, Michel

doi:10.14569/ijacsa.2019.0100721

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…We choose four modulations (BPSK, 8 PSK, 4 QAM, 32 QAM), which are frequently used in several studies. As presented in the table, our method shows an improvement in terms of recognizing different modulations used at low SNRs, particularly at 4 dB 53 …”

Section: Simulation Results and Discussionmentioning

confidence: 80%

An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding

Abdel‐Moneim

Al‐Makhlasawy

Bauomy

et al. 2022

Trans Emerging Tel Tech

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Recently, automatic modulation classification (AMC) has extensively and commonly been utilized in several modern wireless communication systems as a significant tool of signal detection for civilian and military applications and cognitive radio systems. Although several methods have been established to identify the modulation scheme of a received signal, they show a difficulty of learning radio characteristics for most conventional machine learning algorithms. This article focuses on the deep learning (DL) classification technique to solve these problems. To improve the classification accuracy of a communication signal modulation type, we apply a new model that combines Gabor filtering and thresholding with the help of convolution filters implemented in DL. A basic convolutional neural network, AlexNet, and a residual neural network are used for being compatible with constellation diagrams in order to achieve a superior classification performance. Moreover, the Gabor filter can effectively extract spatial information, including edges and textures. In terms of classification accuracy, the proposed AMC system improves the signal modulation classification accuracy significantly, and achieves competitive results. We use seven modulation types over the range of signal-noise ratio (SNR) values from −10 to 30 dB. The performed experiments reveal that the proposal guarantees a remarkable classification accuracy of approximately 100% at a 10 dB SNR over AWGN and Rayleigh fading channels. Therefore, to prove the functional viability of our proposed method, it can be applied in adaptive modulators that can be used in many devices in applications such as Internet-of-Things (IoT). INTRODUCTIONDue to the increasingly growing demand for wireless radio spectrum bandwidth, better approaches for utilization of the radio spectrum are essential. 1 The automatic modulation classification (AMC) is one of the most common techniques for identifying frequency spectra, spectrum management, electronic warfare, cognitive radio networks, interference

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Section: Simulation Results and Discussionmentioning

confidence: 80%

An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding

Abdel‐Moneim

Al‐Makhlasawy

Bauomy

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…To compare our results with those which exist in the literature, we will do it with four modulations because it is these modulations that several authors have used [3][4][5]14]. But the comparison with other results of the literature is difficult as was mentioned in papers [7,13] because there are no standard databases of modulations that everyone should use to facilitate comparison and the parameters of experiments differ from one author to another.…”

Section: Comparisons With Other Resultsmentioning

confidence: 99%

“…In [3] and [4] have demonstrated that the application of a clustering algorithm (fuzzy c-means, k-means, k-center) to normalize data polluted by Gaussian noise only cannot be used to recognize the modulation used. It will be necessary to couple given results to a Neural Network to fulfill the task of classification [3,14]. Our proposed algorithm is a supervised learning case where all modulations are known by receiver and recognition is done through the use of the I-Q constellation diagram which will be considered as a pixel-coded picture.…”

Section: Coding Of the Constellation Diagrammentioning

confidence: 99%

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Automatic Modulation Classification Based on In-Phase Quadrature Diagram Constellation Combined with a Deep Learning Model

Gouho¹,

Karim²,

Aka³

et al. 2020

IJST

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Objectives: This study aims to present a framework for Automatic Modulation recognition using Deep learning without feature extraction. Methods: We study seven modulations using the In-Phase Quadrature constellation polluted by Additive White Gaussian Noise. We apply the K-means algorithm to normalize data transmitted and polluted by noise; the new diagram obtained is considered as an image and coded in pixel before entering in a Deep Neural Network where we apply 20% dropout on hidden layers to avoid overfitting. The simulation is carried out in Matlab. Findings: Experiment performed on selected modulations following the proposed framework gives a good percentage of recognition equal to 96.12%. Our algorithm Deep Neural Network imaGe gives the best performance results at epoch equal to 2,000,000. Applications: The outcome will be beneficial for researchers in Software-Defined Radio for civilian and military applications like electronic attacks and electronic protection.

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New Approach of Automatic Modulation Classification based on in Phase-Quadrature Diagram Combined with Artificial Neural Network

Cited by 2 publications

References 6 publications

An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding

An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding

Automatic Modulation Classification Based on In-Phase Quadrature Diagram Constellation Combined with a Deep Learning Model

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