DL-AMC: Deep Learning for Automatic Modulation Classification

Faheem Ur Rehman; Qamar Abbas; M. Karam Shehzad

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arXiv · 2504.08011

DL-AMC: Deep Learning for Automatic Modulation Classification

Faheem Ur Rehman·Qamar Abbas·M. Karam Shehzad

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Abstract

Automatic Modulation Classification (AMC) is a signal processing technique widely used at the physical layer of wireless systems to enhance spectrum utilization efficiency. In this work, we propose a fast and accurate AMC system, termed DL-AMC, which leverages deep learning techniques. Specifically, DL-AMC is built using convolutional neural network (CNN) architectures, including ResNet-18, ResNet-50, and MobileNetv2. To evaluate its performance, we curated a comprehensive dataset containing various modulation schemes. Each modulation type was transformed into an eye diagram, with signal-to-noise ratio (SNR) values ranging from -20 dB to 30 dB. We trained the CNN models on this dataset to enable them to learn the discriminative features of each modulation class effectively. Experimental results show that the proposed DL-AMC models achieve high classification accuracy, especially in low SNR conditions. These results highlight the robustness and efficacy of DL-AMC in accurately classifying modulations in challenging wireless environments

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BibTeX RIS

Faheem Ur Rehman, Qamar Abbas, M. Karam Shehzad. 2025-04-10. DL-AMC: Deep Learning for Automatic Modulation Classification. https://arxiv.org/abs/2504.08011

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