Dynamic Partial Reconfigurable Demodulators In Automatic Modulation Recognition Systems for Satellite Receivers

2017 ◽  
Author(s):  
Mohamed Elhady M. Keshk ◽  
Asami Kenichi
Keyword(s):  
Modulation Recognition ◽  
Automatic Modulation Recognition ◽  
Recognition Systems

scholarly journals Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3913 ◽  
Author(s):  
Mingxuan Li ◽  
Ou Li ◽  
Guangyi Liu ◽  
Ce Zhang
Keyword(s):  
Deep Learning ◽  
Cognitive Radio ◽  
Supervised Learning ◽  
Rapid Development ◽  
Generative Adversarial Networks ◽  
Modulation Recognition ◽  
Learning Methods ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Automatic Modulation Recognition

With the recently explosive growth of deep learning, automatic modulation recognition has undergone rapid development. Most of the newly proposed methods are dependent on large numbers of labeled samples. We are committed to using fewer labeled samples to perform automatic modulation recognition in the cognitive radio domain. Here, a semi-supervised learning method based on adversarial training is proposed which is called signal classifier generative adversarial network. Most of the prior methods based on this technology involve computer vision applications. However, we improve the existing network structure of a generative adversarial network by adding the encoder network and a signal spatial transform module, allowing our framework to address radio signal processing tasks more efficiently. These two technical improvements effectively avoid nonconvergence and mode collapse problems caused by the complexity of the radio signals. The results of simulations show that compared with well-known deep learning methods, our method improves the classification accuracy on a synthetic radio frequency dataset by 0.1% to 12%. In addition, we verify the advantages of our method in a semi-supervised scenario and obtain a significant increase in accuracy compared with traditional semi-supervised learning methods.

scholarly journals IRLNet: A Short-Time and Robust Architecture for Automatic Modulation Recognition

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 143661-143676
Author(s):  
Huogen Yang ◽  
Lingzhu Zhao ◽  
Guangxue Yue ◽  
Bolin Ma ◽  
Wei Li
Keyword(s):  
Modulation Recognition ◽  
Automatic Modulation Recognition ◽  
Short Time

scholarly journals Deep Learning Aided Method for Automatic Modulation Recognition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 109063-109068 ◽  
Author(s):  
Cheng Yang ◽  
Zhimin He ◽  
Yang Peng ◽  
Yu Wang ◽  
Jie Yang
Keyword(s):  
Deep Learning ◽  
Modulation Recognition ◽  
Automatic Modulation Recognition

scholarly journals Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 42841-42847 ◽  
Author(s):  
Jie Shi ◽  
Sheng Hong ◽  
Changxin Cai ◽  
Yu Wang ◽  
Hao Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Modulation Recognition ◽  
Recognition Method ◽  
Phase Offset ◽  
Automatic Modulation Recognition
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