Autocorrelation Convolution Networks Based on Deep Learning for Automatic Modulation Classification

2020 ◽  
Author(s):  
Duona Zhang ◽  
Wenrui Ding ◽  
Hongyu Wang ◽  
Baochang Zhang
Keyword(s):  
Deep Learning ◽  
Modulation Classification ◽  
Automatic Modulation Classification

scholarly journals Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 924 ◽  
Author(s):  
Duona Zhang ◽  
Wenrui Ding ◽  
Baochang Zhang ◽  
Chunyu Xie ◽  
Hongguang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Aerial Vehicles

scholarly journals LightAMC: Lightweight Automatic Modulation Classification via Deep Learning and Compressive Sensing

2020 ◽  
Author(s):  
Yu Wang ◽  
Jie Yang ◽  
Miao Liu ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Compressive Sensing ◽  
Communication Systems ◽  
Scaling Factor ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Performance Loss ◽  
Computational Speed ◽  
Aerial Vehicle ◽  
Large Model

Automatic modulation classification (AMC) is an promising technology for non-cooperative communication systems in both military and civilian scenarios. Recently, deep learning (DL) based AMC methods have been proposed with outstanding performances. However, both high computing cost and large model sizes are the biggest hinders for deployment of the conventional DL based methods, particularly in the application of internet-of-things (IoT) networks and unmanned aerial vehicle (UAV)-aided systems. In this correspondence, a novel DL based lightweight AMC (LightAMC) method is proposed with smaller model sizes and faster computational speed. We first introduce a scaling factor for each neuron in convolutional neural network (CNN) and enforce scaling factors sparsity via compressive sensing. It can give an assist to screen out redundant neurons and then these neurons are pruned. Experimental results show that the proposed LightAMC method can effectively reduce model sizes and accelerate computation with the slight performance loss.

scholarly journals Dive Into Deep Learning Based Automatic Modulation Classification: A Disentangled Approach

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 113271-113284 ◽  
Author(s):  
Xiaolei Shang ◽  
Honglin Hu ◽  
Xiaoqiang Li ◽  
Tianheng Xu ◽  
Ting Zhou
Keyword(s):  
Deep Learning ◽  
Modulation Classification ◽  
Automatic Modulation Classification
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