Automatic Modulation Classification Using a Deep Multi-Stream Neural Network
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TL;DR: It is shown that the proposed novel deep network method using a multi-stream structure can learn more features of the signal data, and it is also shown to be superior to common deep networks.
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Abstract: In wireless communication, modulation classification is an important part of the non-cooperative communication, and it is difficult to classify the various modulation schemes using conventional methods. The deep learning network has been used to handle the problem and acquire good results. In the deep convolutional neural network (CNN), the data length in the input is fixed. However, the signal length varies in communication, and it causes that the network cannot take advantage of the input signal data to improve the classification accuracy. In this paper, a novel deep network method using a multi-stream structure is proposed. The multi-stream network form increases the network width, and enriches the types of signal features extracted. The superposition convolutional unit in each stream can further improve the classification performance, while the shallower network form is easier to train for avoiding the over-fitting problem. Further, we show that the proposed method can learn more features of the signal data, and it is also shown to be superior to common deep networks.
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Citations
Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey
TL;DR: In this article, the authors provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output(MIMO) systems.
Learning the unknown: Improving modulation classification performance in unseen scenarios
Erma Perenda,Sreeraj Rajendran,Gérôme Bovet,Sofie Pollin,Mariya Zheleva +4 more
- 10 May 2021
TL;DR: It is shown that Spatial Transformer Networks (STN) and Transfer Learning (TL) embedded into a light ResNeXt-based classifier can improve average classification accuracy up to 10-30% for specific unseen scenarios with only 5% labeled data for a large dataset of 20 complex higher-order modulations.
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Deep Learning-Based Robust Automatic Modulation Classification for Cognitive Radio Networks
TL;DR: In this paper, a deep learning-based robust automatic modulation classification (AMC) method is proposed for cognitive radio networks, where the input size is extended as $4 \times N$ size by copying IQ components and concatenating in reverse order to improve the classification accuracy.
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Lightweight Deep Learning Model for Automatic Modulation Classification in Cognitive Radio Networks
TL;DR: A novel convolutional neural network architecture for AMC with bottleneck and asymmetric convolution structure are employed in the proposed model, which can reduce the computational complexity.
Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends
01 Jan 2022
TL;DR: In this paper , the authors provide an overview of the different aspects of the intelligent mMIMO (I-mMIMO) systems, including the characteristics and challenges of the massive multiple-input-multiple-output MIMO systems.
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