Automatic Modulation Classification for Enhanced Cognitive Radio for IoT Systems based on Deep Learning

Ali, Engy M.; Salama, Gerges M.; A., Khalil A.; Ezz-Eldin, Mai

doi:10.21608/jaet.2024.318588.1331

	Automatic Modulation Classification for Enhanced Cognitive Radio for IoT Systems based on Deep Learning
Journal of Advanced Engineering Trends
Volume 44, Issue 1, January 2025, Page 106-113 PDF (1017.27 K)
Document Type: Original Article
DOI: 10.21608/jaet.2024.318588.1331
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Authors
Engy M. Ali ¹; Gerges M. Salama ²; Khalil A. A.¹; Mai Ezz-Eldin ³
¹Electrical Engineering Dep., Faculty of Engineering, Minia University, Egypt.
²Electrical Engineering Dep., Faculty of Engineering, Minia University, Egypt
³Electrical and Communication Engineering Dep., Future High Institute of Engineering, Fayoum.
Abstract
Automatic Modulation Classification (AMC) plays a crucial role in Cognitive Radio (CR) systems, especially within Internet of Things (IoT) devices where spectrum efficiency and flexibility are paramount. Traditional modulation classification methods often rely on feature extraction and machine learning (ML) algorithms, which need a lot of complex calculations and may struggle with complex modulation schemes and noisy channels. Deep Learning (DL), particularly Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), has a positive impact on AMC due to their ability to automatically learn and extract discriminative features from the sequences of raw I/Q received signals. So, this paper proposes DL AMC model is built and investigated using Radioml2016 data for enhancing spectrum management for CR in IoT systems. The proposed model has reduce model parameters by 35% by using depthwise separable convolutional and traditional convolution to create the model architecture while increasing the accuracy of the model to 84 % at high SNR . The reduction in the model parameters led to a reduction in the prediction time to achieve the requirement in cognitive radio systems for IoT devices.
Keywords
Automatic Modulation Classification; Cognitive Radio; Deep Learning; Depthwise Separable Convolution


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