DL-AMC: Deep Learning for Automatic Modulation Classification
DL-AMC: Deep Learning for Automatic Modulation Classification
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
Faheem Ur Rehman、Qamar Abbas、M. Karam Shehzad
无线通信无线电设备、电信设备
Faheem Ur Rehman,Qamar Abbas,M. Karam Shehzad.DL-AMC: Deep Learning for Automatic Modulation Classification[EB/OL].(2025-04-10)[2025-05-17].https://arxiv.org/abs/2504.08011.点此复制
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