scholarly journals 3D convolutional neural networks based automatic modulation classification in the presence of channel noise

2021 ◽  
Author(s):  
Rahim Khan ◽  
Qiang Yang ◽  
Inam Ullah ◽  
Ateeq Ur Rehman ◽  
Ahsan Bin Tufail ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Channel Noise ◽  
Modulation Classification ◽  
Automatic Modulation Classification

scholarly journals Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 27182-27188
Author(s):  
Kaisheng Liao ◽  
Yaodong Zhao ◽  
Jie Gu ◽  
Yaping Zhang ◽  
Yi Zhong
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Modulation Classification ◽  
Automatic Modulation Classification

scholarly journals Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6410
Author(s):  
Ke Zang ◽  
Wenqi Wu ◽  
Wei Luo
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
Model Performance ◽  
Sparse Learning ◽  
Modulation Classification ◽  
Learning Models ◽  
Automatic Modulation Classification

Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently. However, deep neural networks are usually overparameterized, i.e., most of the connections between neurons are redundant. The large model size hinders the deployment of deep neural networks in applications such as Internet-of-Things (IoT) networks. Therefore, reducing parameters without compromising the network performance via sparse learning is often desirable since it can alleviates the computational and storage burdens of deep learning models. In this paper, we propose a sparse learning algorithm that can directly train a sparsely connected neural network based on the statistics of weight magnitude and gradient momentum. We first used the MNIST and CIFAR10 datasets to demonstrate the effectiveness of this method. Subsequently, we applied it to RNNs with different pruning strategies on recurrent and non-recurrent connections for AMC problems. Experimental results demonstrated that the proposed method can effectively reduce the parameters of the neural networks while maintaining model performance. Moreover, we show that appropriate sparsity can further improve network generalization ability.

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