Automatic Modulation Classification Using Cyclic Correntropy Spectrum in Impulsive Noise

Ma, Jitong; Qiu, Tianshuang

doi:10.1109/lwc.2018.2875001

Cited by 64 publications

(21 citation statements)

References 14 publications

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“…Nonetheless, this method required a complex computation process due to the lack of capability in handling large datasets in the SVM classifier. In another research, the cyclic correntopy spectrum-based MC approach was introduced by Ma et al [28]. Cycle frequencies (CF) were utilized to classify the modulation type of the signal.…”

Section: Automatic Modulation Classification (Amc) Without Machine Lementioning

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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Section: Automatic Modulation Classification (Amc) Without Machine Lementioning

confidence: 99%

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

et al. 2021

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“…The algorithms proposed in this paper are suitable for Gaussian noise and non-Gaussian noise. No matter what kind of noise in the input signal is closer to, the proposed algorithm has good performance and strong inclusiveness to noise [13] [14].…”

Section: Introductionmentioning

confidence: 99%

The Performance Evaluation of Big Data-Driven Modulation Classification in Complex Environment

Cai

Wang

2021

IEEE Access

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With the proliferation of frequency-using devices and the advent of the era of big data, spectrum management and control are faced with challenges of effectiveness and accuracy. Modulation classification technology is the foundation and key part of spectrum management and control. Therefore, the effectiveness of modulation classification technology in big data scenario is very important. In this paper, we consider not only the validity of the classification model under the background of big data, but also the dynamics of noise in the complex electromagnetic environment. So we construct a big dataset containing different signals under different MSNR, and use the big data to drive the deep learning model, and finally get the classification result. The proposed method can realize modulation classification only by training one model, which avoids the redundancy of model training in previous algorithms. The simulation results demonstrate the effectiveness and reliability of the proposed method. INDEX TERMS Big data, non-Gaussian noise, modulation classification, deep learning.

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“…A variety of features were extracted and employed in [6]- [18], containing amplitude with phase and carrier frequency [6], instantaneous features [7], high-order statistical features [8], [9], cyclic spectrum parameters [10], [11], bispectrum features [12], wavelet features [13], [14] and constellation diagram [15], [16]. For the choice of the training classifier, the classifiers based on machine learning like support vector machine (SVM) in [6], [13], [17], [18], decision tree in [7], [8], [14], k nearest neighbor (KNN) in [10], compressive sensing in [12], genetic algorithm in [15], and neural network (NN) in [9], [11], [16], were widely used due to their robustness, self-adaption, and nonlinear processing ability [19]. However, the performance of these PR methods largely depends on empirical feature extraction due to the limited capacity of classifiers [19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Attention Cooperative Framework for Automatic Modulation Recognition

Chen

Zhang

et al. 2020

IEEE Access

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Modulation recognition plays an indispensable role in the field of wireless communications. In this paper, a novel attention cooperative framework based on deep learning is proposed to improve the accuracy of the automatic modulation recognition (AMR). Within this framework, a convolutional neural network (CNN), a recurrent neural network (RNN), and a generative adversarial network (GAN) are constructed to cooperate in AMR. A cyclic connected CNN (CCNN) is designed to extract spatial features of the received signal, and a bidirectional RNN (BRNN) is constructed for obtaining temporal features. To take full advantage of the complementarity and relevance between the spatial and temporal features, a fusion strategy based on global average and max pooling (GAMP) is proposed. To deal with different influence levels of the signal feature maps, we present the attention mechanism in this framework to realize recalibration. Besides, modulation recognition based on deep learning requires numerous data for training purposes, which is difficult to achieve in practical AMR applications. Therefore, an auxiliary classification GAN (ACGAN) is developed as a generator to expand the training set, and we modify the loss function of ACGAN to accommodate the processing of the actual in-phase and quadrature (I/Q) signal data. Considering the difference in distribution between generated data and real data, we propose a novel auxiliary weighing loss function to achieve higher recognition accuracy. Experimental results on the dataset RML2016.10a show that the proposed framework outperforms existing deep learning-based approaches and achieves 94% accuracy at high signal to noise ratio (SNR). INDEX TERMS Automatic modulation recognition (AMR), attention mechanism, convolutional neural network (CNN), generative adversarial network (GAN), recurrent neural network (RNN).

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Automatic Modulation Classification Using Cyclic Correntropy Spectrum in Impulsive Noise

Cited by 64 publications

References 14 publications

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

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

The Performance Evaluation of Big Data-Driven Modulation Classification in Complex Environment

A Novel Attention Cooperative Framework for Automatic Modulation Recognition

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