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Automatic modulation recognition using CNN deep learning models

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Abstract

Modulation techniques are widely required in communication applications, especially in wireless communication systems. Thus, it is necessary to classify and recognize types of modulation. Deep learning (DL) models can help precisely recognize the modulation techniques, provided that well-designed and trained DL models for high-performance recognition are developed. In this paper, two different convolutional neural network (CNN) models are proposed for recognition of modulation techniques. These models are applied to two different datasets: the first dataset consists of mathematical vectors, namely RadioML2016.10a and the second dataset contains 24,460 images. The RadioML2016.10a has eleven classes of modulation techniques, namely 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, and WBFM, while the images dataset has eight classes of digital modulation techniques, namely 16PSK, 16QAM, 2PSK, 32QAM, 64QAM, 8PSK, 8QAM, and QPSK. The two CNN models are trained and the models’ parameters are precisely tuned of for high-accuracy achievement. Experimentally, classification reports of modulation techniques and receiver operating characteristic (ROC) curves are used to analyze the performance of the proposed models. The CNN models provide high performance, with a testing accuracy of 53.65% and 94.39%. The results show that the area under the micro-average ROC curves for the CNNs is 92% and 97%.

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Data availability

The paper has two datasets, namely RadioML2016.10a dataset and images dataset available in the Kaggle repository. https://www.kaggle.com/code/wykxldz/description-on-radioml-2016-10a/data. https://www.kaggle.com/datasets/anaselmasry/dataset-cnn-train-10000?select=dataset_cnn_train_10000.

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Authors and Affiliations

  1. Department of Electronics and Communications Engineering, Al-Madinah Higher Institute for Engineering and Technology, Giza, 12947, Egypt

    Saeed Mohsen

  2. Department of Artificial Intelligence Engineering, Faculty of Computer Science and Engineering, King Salman International University (KSIU), El Tor, 46511, South Sinai, Egypt

    Saeed Mohsen & Ahmed Emam

  3. Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh, 12435, Saudi Arabia

    Anas M. Ali

  4. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Anas M. Ali

  5. Computer Science and Math Department, Faculty of Science, Menoufia University, Menoufia, Egypt

    Ahmed Emam

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  1. Saeed Mohsen
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  2. Anas M. Ali
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Correspondence to Saeed Mohsen.

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Mohsen, S., Ali, A.M. & Emam, A. Automatic modulation recognition using CNN deep learning models. Multimed Tools Appl 83, 7035–7056 (2024). https://doi.org/10.1007/s11042-023-15814-y

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