Automatic Modulation Classification: Convolutional Deep Learning Neural Networks Approaches

Document Type : Reviews Articles.

Authors

1 Communication Engineer, International Maritime Science Academy, Red Sea, Egypt

2 communication,faculty of engineering ,Al-Azhar University , Qena

3 Electrical 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.

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