Automatic Modulation Classification: Convolutional Deep Learning Neural Networks Approaches | ||||
| SVU-International Journal of Engineering Sciences and Applications | ||||
| Article 5, Volume 4, Issue 1, June 2023, Page 48-54 PDF (1017.58 K) | ||||
| Document Type: Reviews Articles. | ||||
| DOI: 10.21608/svusrc.2022.162662.1076 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
mona lotfy  1; Mohamed Hassan Essai2; Hany Ahmed Atallah3
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| 1Communication Engineer, International Maritime Science Academy, Red Sea, Egypt | ||||
| 2communication,faculty of engineering ,Al-Azhar University , Qena | ||||
| 3Electrical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt | ||||
| Abstract | ||||
| Automatic modulation classification (AMC), which plays critical roles in both civilian and military applications, is investigated in this paper through a deep learning approach. A lot of research has been done on feature-based (FB) AM algorithms in particular. Convolutional neural networks (CNN)-based robust AMC approach is developed in this paper to address the difficulty that current FB AMC methods are often intended for a limited set of modulation and lack of generalisation capacity. In total, 11 different modulation types are taken into consideration. Conventional AMCs can be categorized into maximum likelihood (ML)-based (ML-AMC) and feature-based AMC. This paper proposes a robust Convolutional neural network (CNN)-based automatic modulation classification (AMC) technique. The suggested technique can classify the received signals without feature extraction, and it can learn the features from them automatically. A comparison study was done for the proposed CNN-based AMCs with two different optimizers at two different signal-to-noise ratios to select the best one of them based on the performance. | ||||
| Keywords | ||||
| Modulation classification; Deep learning; Convolutional neural network Wireless signal | ||||
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