Automatic Modulation Classification Using Deep Learning Based on Sparse Autoencoders With Nonnegativity Constraints

Ali, Afan; Fan, Yangyu

doi:10.1109/lsp.2017.2752459

Cited by 99 publications

(30 citation statements)

References 21 publications

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“…The Maximum value of the power density of the normalized-centered instantaneous amplitude can reflect the spectral characteristics of different signals [21], which is defined as follow:…”

Section: ) Handcrafted Featuresmentioning

confidence: 99%

Few-Shot Modulation Classification Method Based on Feature Dimension Reduction and Pseudo-Label Training

Shi

Jiang

et al. 2020

IEEE Access

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In modulation classification domain, handcrafted feature based method can fit well from a few labeled samples, while deep learning based method require a large amount of samples to achieve the superior classification performance. In order to improve the modulation classification accuracy under the constraint of limited labeled samples, this paper proposes a few-shot modulation classification method based on feature dimension reduction and pseudo-label training (FDRPLT), which combines handcrafted feature based method with deep learning based method. First, an optimal low-dimensional feature subset is created by the combination of the handcrafted features and autoencoder-extracted features post-processed by a feature selection algorithm. Then, a fully connected network (FCN), trained on a small number of labeled signals, is designed for the automated annotating, where unlabeled samples can be annotated and used for the later convolution neural network (CNN) training. The simulation results show that the classification accuracy of eight kinds of modulation types can reach to 98.3% when the SNR is 20dB.

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“…The Maximum value of the power density of the normalized-centered instantaneous amplitude can reflect the spectral characteristics of different signals [21], which is defined as follow:…”

Section: ) Handcrafted Featuresmentioning

confidence: 99%

Few-Shot Modulation Classification Method Based on Feature Dimension Reduction and Pseudo-Label Training

Shi

Jiang

et al. 2020

IEEE Access

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“…There are various techniques such as a deep neural network (DNN) [8], a convolutional neural network (CNN) [9] and a recurrent neural network (RNN) [10] for the AMC that have been studied. The CNN algorithm is a method that shows excellent performance in image processing.…”

Section: Introductionmentioning

confidence: 99%

Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics

2020

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Recently, in order to satisfy the requirements of commercial communication systems and military communication systems, automatic modulation classification (AMC) schemes have been considered. As a result, various artificial intelligence algorithms such as a deep neural network (DNN), a convolutional neural network (CNN), and a recurrent neural network (RNN) have been studied to improve the AMC performance. However, since the AMC process should be operated in real time, the computational complexity must be considered low enough. Furthermore, there is a lack of research to consider the complexity of the AMC process using the data-mining method. In this paper, we propose a correlation coefficient-based effective feature selection method that can maintain the classification performance while reducing the computational complexity of the AMC process. The proposed method calculates the correlation coefficients of second, fourth, and sixth-order cumulants with the proposed formula and selects an effective feature according to the calculated values. In the proposed method, the deep learning-based AMC method is used to measure and compare the classification performance. From the simulation results, it is indicated that the AMC performance of the proposed method is superior to the conventional methods even though it uses a small number of features.

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“…Peng et al adopted convolutional neural network (CNN) for the task of modulation classification [16]. Similar deep learning based methods have also been proposed by Ali [21], Zheng [17] and Yang [18]. Most of these machine learning based methods claim to have low computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Modulation Classification in MIMO Systems With Distribution Test Ensemble

2020

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In classification of signal modulation types in MIMO systems, it is difficult to achieve both high accuracy and high computational efficiency at the same time. State-of-the-art likelihood based methods incur massive increase in computational complexity when the number of transmitting antennas and the order of modulation increase. To make modulation classification feasible in time critical systems, we propose a low complexity algorithm with an ensemble of distribution tests. Three goodness-of-fit and a novel variance based distribution tests are employed to examine the mismatch between unknown signal and different modulation hypotheses. The results from all tests are combined by a multilayer perceptron classifier for improved robustness under a variety of channel conditions including AWGN channel and slow fading channels. The resulting solution achieves performance close to the maximum likelihood classifier at high SNR. Yet, it requires much lower computational complexity in all cases.

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Automatic Modulation Classification Using Deep Learning Based on Sparse Autoencoders With Nonnegativity Constraints

Cited by 99 publications

References 21 publications

Few-Shot Modulation Classification Method Based on Feature Dimension Reduction and Pseudo-Label Training

Few-Shot Modulation Classification Method Based on Feature Dimension Reduction and Pseudo-Label Training

Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics

Modulation Classification in MIMO Systems With Distribution Test Ensemble

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