Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/201750
Authors: 
Mossad, Omar S.
ElNainay, Mustafa
Torki, Marwan
Year of Publication: 
2019
Series/Report no.: 
2nd Europe - Middle East - North African Regional Conference of the International Telecommunications Society (ITS): "Leveraging Technologies For Growth", Aswan, Egypt, 18th-21st February, 2019
Abstract: 
Automatic modulations recognition is one of the most important aspects in cognitive radios (CRs). Unlicensed users or secondary users (SUs) tend to classify the incoming signals to recognize the type of users in the system. Once the available users are detected and classified accurately, the CR can modify his transmission parameters to avoid any interference with the licensed users or primary users (PUs). In this paper, we propose a deep learning technique to detect the modulations schemes used in a number of sampled transmissions. This approach uses a deep neural network that consists of a large number of convolutional filters to extract the distinct features that separate the various modulation classes. The training is performed to improve the overall classification accuracy with a major focus on the misclassified classes. The results demonstrate that our approach outperforms the recently proposed Convolutional, Long Short Term Memory (LSTM), Deep Neural Network (CLDNN) in terms of overall classification accuracy. Moreover, the classification accuracy obtained by the proposed approach is greater than the CLDNN algorithm at the highest signal-to-noise ratio used.
Subjects: 
modulation recognition
deep learning
convolutional neural networks
Document Type: 
Conference Paper

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