A modulation classification method based on deformable convolutional neural networks for broadband satellite communication systems

2018 ◽  
Author(s):  
Xiangjun Xin ◽  
Ying Tao ◽  
Naijing Liu ◽  
Qian Li ◽  
Qi Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Communication Systems ◽  
Satellite Communication ◽  
Classification Method ◽  
Modulation Classification

scholarly journals Automatic Modulation Classification Method for Multiple Antenna System Based on Convolutional Neural Network

2020 ◽  
Author(s):  
Juan Wang ◽  
Yu Wang ◽  
Wenmei Li ◽  
Guan Gui ◽  
Fumiyuki Adachi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Communication Systems ◽  
Antenna System ◽  
Arithmetic Mean ◽  
Classification Method ◽  
Modulation Classification ◽  
Multiple Antenna ◽  
Modulation Recognition ◽  
Communication Signals ◽  
Multiple Antenna System

<div>In order to transmit communication signals of</div><div>different properties, quickly, effectively, and accurately, various</div><div>different modulation styles can be adopted. Accurate recognition</div><div>of signal modulation is required at the receive side. Automatic</div><div>modulation recognition (AMR) is a key technique to identify</div><div>various styles of modulation of signals received in wireless</div><div>channels. It can be used in many kinds of communication systems,</div><div>including single antenna system and multiple antenna system. In</div><div>this paper, we propose a convolutional neural networks (CNN)</div><div>aided AMR method for multiple antenna system. Compared with</div><div>the high order cumulants (HOC) and artificial neural networks</div><div>(ANN) aided traditional AMR classification method, both with</div><div>two specific combination strategies, such as relative majority</div><div>voting method and arithmetic mean method, the proposed</div><div>AMR with arithmetic mean method has the best classification</div><div>performance. The experimental results obtained verify that the</div><div>CNN, one of the representative algorithms of deep learning, has</div><div>a strong ability to exploit dominant features and classify the</div><div>modulation styles.</div>

scholarly journals Automatic Modulation Classification Method for Multiple Antenna System Based on Convolutional Neural Network

2020 ◽  
Author(s):  
Juan Wang ◽  
Yu Wang ◽  
Wenmei Li ◽  
Guan Gui ◽  
Fumiyuki Adachi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Communication Systems ◽  
Antenna System ◽  
Arithmetic Mean ◽  
Classification Method ◽  
Modulation Classification ◽  
Multiple Antenna ◽  
Modulation Recognition ◽  
Communication Signals ◽  
Multiple Antenna System

<div>In order to transmit communication signals of</div><div>different properties, quickly, effectively, and accurately, various</div><div>different modulation styles can be adopted. Accurate recognition</div><div>of signal modulation is required at the receive side. Automatic</div><div>modulation recognition (AMR) is a key technique to identify</div><div>various styles of modulation of signals received in wireless</div><div>channels. It can be used in many kinds of communication systems,</div><div>including single antenna system and multiple antenna system. In</div><div>this paper, we propose a convolutional neural networks (CNN)</div><div>aided AMR method for multiple antenna system. Compared with</div><div>the high order cumulants (HOC) and artificial neural networks</div><div>(ANN) aided traditional AMR classification method, both with</div><div>two specific combination strategies, such as relative majority</div><div>voting method and arithmetic mean method, the proposed</div><div>AMR with arithmetic mean method has the best classification</div><div>performance. The experimental results obtained verify that the</div><div>CNN, one of the representative algorithms of deep learning, has</div><div>a strong ability to exploit dominant features and classify the</div><div>modulation styles.</div>

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