Deep learning for wireless modulation classification based on discrete wavelet transform

• Published in: International journal of communication systems 2021-12, Vol.34 (18), p.n/a
• Main Authors: Al‐Makhlasawy, Rasha M., Ghanem, Hanan S., Kassem, Hossam M., Elsabrouty, Maha, Hamed, Hesham F. A., Abd El‐Samie, Fathi E., Salama, Gerges M.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Classifiers; Communications systems; Constellations; convolutional neural networks (CNNs); Deep learning; deep learning (DL); Discrete Wavelet Transform; discrete wavelet transform (DWT); Image classification; Image processing; Machine learning; Modulation; modulation classification; Noise; pretrained networks; Signal classification; Wavelet transforms
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.4980

Summary In the presence of noise in communication systems, constellation diagram points are scattered to the extent that may make the modulation classification a difficult task. With the plethora of applications of machine and deep learning, several communication systems have adopted machine and deep learning to solve some classical detection and classification problems. Casting the modulation order detection as a pattern classification of the constellation images opens the door for application of mature machine learning and image processing tools to solve the classification problem, efficiently. This paper presents a system based on a wavelet‐aided convolutional neural network (CNN) classifier to efficiently detect the modulation type and order in the presence of noise. The proposed system depends on a pretrained CNN setup, which is trained with a set of constellation diagrams for each modulation scheme and used after that for testing. In addition, discrete wavelet transform (DWT) is investigated to generate representative patterns from constellation diagrams to be used for the training and testing tasks as well. The wavelet approximation images and their corresponding wavelet sub‐bands across all predefined scales are used in the dataset. Several pretrained networks including AlexNet, VGG‐16, and VGG‐19 are used as classifiers for the modulation type from the DWTs for different constellation diagrams. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different signal‐to‐noise ratios (SNRs). This paper proposes the use of a wavelet‐aided convolutional neural network (CNN) classifier to efficiently detect the modulation order in the presence of noise. The proposed system utilizes pretrained CNN setup and train it with a training set of constellation diagrams for each modulation scheme and used for training and testing of the CNNs. In addition, discrete wavelet transform (DWT) is investigated to generate representative patterns from constellation diagrams to be used for the training and testing tasks as well. Several pretrained networks including AlexNet, VGG‐16, and VGG‐19 are used as classifiers for the modulation type from the DWTs for different constellation diagrams.