Automatic modulation classification by exploiting cyclostationary features in wavelet domain

Kumar, Sushant; Darak, Sumit J.

doi:10.1109/ncc.2017.8077145

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…From the scalograms we can also extracts the detail coefficients at the coarsest scale from the wavelet decomposition (Park et al, 2008;Kumar et al, 2017) of the three signals. Figure 21 shows these coefficients for three levels of the signals.…”

Section: Figure 13mentioning

confidence: 99%

Spread spectrum modulation recognition based on phase diagram entropy

Stanescu

Digulescu

Ioana

et al. 2023

Front. Signal Process.

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Wireless communication technologies are undergoing intensive study and are experiencing accelerated progress which leads to a large increase in the number of end-users. Because of this, the radio spectrum has become more crowded than ever. These previously mentioned aspects lead to the urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency. Specifically, modulation scheme recognition occupies a crucial position in the civil and military application, especially with the emergence of Software Defined Radio (SDR). The modulation recognition is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Spread spectrum (SS) techniques represent the foundation for the design of Cognitive Radio systems. In this work, we propose a new method of characterization of Spread spectrum modulations capable of providing relevant information for the process of recognition of this type of modulations. Using the proposed approach, results higher than 90% are obtained in the modulation classification process, thus bringing an advantage over the classical methods, whose performance is below 75%.

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Section: Figure 13mentioning

confidence: 99%

Spread spectrum modulation recognition based on phase diagram entropy

Stanescu

Digulescu

Ioana

et al. 2023

Front. Signal Process.

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“…On the other hand, FB methods are simpler to implement. Different types of features such as time domain, transform domain (frequency domain, wavelet transform [8], spectral correlation function (SCF)...), statistical and constellation can be used for this category [9]. Over the years, various techniques have been attempted for automated modulation classification using FB with both conventional and machine learning (ML) techniques, including the decision tree approach, support vector machines (SVM) [10][11][12], K-nearest neighbor (KNN) [13], artificial neural networks (ANNs) [14], and others.…”

Section: Related Workmentioning

confidence: 99%

SDR implementation of a light deep learning model based CNN for joint spectrum sensing and AMC

Nesraoui,

Teguig,

Sadoudi

2024

Phys. Scr.

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Automatic modulation classification (AMC) aims to blindly recognize themodulation type of a received signal in wireless systems. It is also a critical componentof non-cooperative communication systems after the detection of the presence of asignal. In this paper, we introduce a robust approach, termed DET-AMC (jointDetection and Automatic Modulation Classification), employing Convolutional NeuralNetworks (CNNs) trained via transfer learning methodology. The main advantage ofour approach is its ability to handle a wide range of modulation types, including 10different schemes generated in Gnuradio and their detection using the same model.Through extensive experimentation, we evaluate the performance of our light CNNbased DET-AMC method across varying signal-to-noise ratio (SNR) levels, as well asin the presence of phase noise and frequency offset. We find that the CNN’s learnedfeatures, obtained through transfer learning, exhibit robustness, particularly in lowSNR and various challenging conditions, leading to accurate modulation classification.In general, our approach outperforms existing methods by using the effectivenessof deep learning in capturing relevant discriminative features. Additionally, ourmodel offers a robust solution for join detection and AMC by achieving an accurateprobability of detection and modulation classification without the need for manualfeature engineering or the consideration of frequency offset, phase noise or noiseestimation. Our model achieves 100% accuracy for synthetic and real data at anSNR equal to -10 dB for detection, and 100% and 98% for classification of syntheticand real signals at -4 dB, respectively.

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“…Quantization is a significant process in modulation recognition [48]. It produces a received signal that has a range of discrete finite values.…”

Section: Quantizationmentioning

confidence: 99%

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

et al. 2021

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The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

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Automatic modulation classification by exploiting cyclostationary features in wavelet domain

Cited by 8 publications

References 14 publications

Spread spectrum modulation recognition based on phase diagram entropy

Spread spectrum modulation recognition based on phase diagram entropy

SDR implementation of a light deep learning model based CNN for joint spectrum sensing and AMC

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

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