Probability density function based data augmentation for deep neural network automatic modulation classification with limited training data

Hao, Chongzheng; Dang, Xiaoyu; Yu, Xianxiang; Li, Sai; Wang, Chenghua

doi:10.1049/cmu2.12588

Cited by 4 publications

(1 citation statement)

References 31 publications

(73 reference statements)

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“…Modulation is categorized as a technology for processing signals, that mainly converts an information signal into a form that is suitable for transmission [ 18 ]. In communication, modulation usually involves changing some attributes of a carrier signal, such as the amplitude, frequency, or phase of the carrier signal.…”

Section: Design Of Signal Amc Model With Neural Network Fusionmentioning

confidence: 99%

Signal automatic modulation based on AMC neural network fusion

Yin,

Diao

2024

PLoS ONE

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With the rapid development of modern communication technology, it has become a core problem in the field of communication to find new ways to effectively modulate signals and to classify and recognize the results of automatic modulation. To further improve the communication quality and system processing efficiency, this study combines two different neural network algorithms to optimize the traditional signal automatic modulation classification method. In this paper, the basic technology involved in the communication process, including automatic signal modulation technology and signal classification technology, is discussed. Then, combining parallel convolution and simple cyclic unit network, three different connection paths of automatic signal modulation classification model are constructed. The performance test results show that the classification model can achieve a stable training and verification state when the two networks are connected. After 20 and 29 iterations, the loss values are 0.13 and 0.18, respectively. In addition, when the signal-to-noise ratio (SNR) is 25dB, the classification accuracy of parallel convolutional neural network and simple cyclic unit network model is as high as 0.99. Finally, the classification models of parallel convolutional neural networks and simple cyclic unit networks have stable correct classification probabilities when Doppler shift conditions are introduced as interference in practical application environment. In summary, the neural network fusion classification model designed can significantly improve the shortcomings of traditional automatic modulation classification methods, and further improve the classification accuracy of modulated signals.

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Section: Design Of Signal Amc Model With Neural Network Fusionmentioning

confidence: 99%

Signal automatic modulation based on AMC neural network fusion

Yin,

Diao

2024

PLoS ONE

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MAE‐SigNet: An effective network for automatic modulation recognition

Zhang,

Song,

Wang

2024

IET Communications

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The rapid development of the Internet of Things has exacerbated issues such as spectrum resource scarcity, poor communication quality, and high communication energy consumption. Automatic modulation recognition (AMR), a key technology in cognitive radio, has emerged as a crucial solution to these challenges. Deep neural networks have been recently applied in AMR tasks and have achieved remarkable success. However, existing deep learning‐based AMR methods often need to consider the sensitivity of models to noise fully. This study proposes a masked autoencoder multi‐scale attention feature fusion model (MAE‐SigNet). This model integrates a MAE, multi‐scale feature extraction module, bidirectional long short‐term memory module, and MAM to accomplish the AMR task under low signal‐to‐noise ratio. Additionally, we optimize the cross‐entropy loss of the MAE‐SigNet model by introducing MAE decoder reconstruction error, which enhances the model's sensitivity to noise while achieving more accurate feature representation. Experimental results demonstrate that the MAE‐SigNet model achieves average recognition rates of 63.77%, 65.28%, and 75.26% on the RML2016.10a, RML2016.10b, and RML2016.04c datasets. Mainly, MAE‐SigNet exhibits outstanding performance at various levels of low signal‐to‐noise ratios from −6 to 4 dB.

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A transfer learning-based GAN for data augmentation in automatic modulation recognition

Gao,

Ke,

et al. 2024

Eng. Res. Express

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The automatic modulation recognition (AMR) algorithms based on deep learning (DL) have achieved high classification accuracy by automatically extracting deep features from massive data. However, in real-world scenarios, sufficient training data is always difficult to collect, which affects the performance of DL-based models. As a type of data augmentation algorithm, Generative Adversarial Networks (GANs) can generate artificial data similar to the given real data and thus solve the problem of insufficient data, whereas the training process of GANs is also affected by limited number of data samples. Inspired by the successful application of transferring GANs in the field of image generation, this paper employs transfer learning-based GANs to enlarge the training data by generating the constellation diagram images of radio signals, which can effectively solve the problems of divergence and model collapse. We feed the augmented dataset into a CNN model for modulation recognition and the experimental results demonstrate that our proposed method achieves a performance improvement ranging from 1% to 5.1% compared with the result of the original limited training data.

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Probability density function based data augmentation for deep neural network automatic modulation classification with limited training data

Cited by 4 publications

References 31 publications

Signal automatic modulation based on AMC neural network fusion

Signal automatic modulation based on AMC neural network fusion

MAE‐SigNet: An effective network for automatic modulation recognition

A transfer learning-based GAN for data augmentation in automatic modulation recognition

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