Research article

Modulation classification analysis of CNN model for wireless communication systems

  • Received: 27 June 2023 Revised: 28 August 2023 Accepted: 21 September 2023 Published: 23 October 2023
  • Modulation classification (MC) is a critical task in wireless communication systems, enabling the identification of the modulation class in the received signals. In this paper, we analyzed a novel multi-layer convolutional neural network (CNN) to extract hierarchical features directly from the raw baseband samples. Moreover, we compared the training and testing accuracy of the CNN model for various decimation rates, input sample size and the number of convolutional layers. The results showed that the three-layer CNN model provided better classification accuracy with less computation cost. Furthermore, we observed that the MC performance of the proposed CNN model was better than the other deep learning (DL) and cumulant-based models.

    Citation: Tamizhelakkiya K, Sabitha Gauni, Prabhu Chandhar. Modulation classification analysis of CNN model for wireless communication systems[J]. AIMS Electronics and Electrical Engineering, 2023, 7(4): 337-353. doi: 10.3934/electreng.2023018

    Related Papers:

  • Modulation classification (MC) is a critical task in wireless communication systems, enabling the identification of the modulation class in the received signals. In this paper, we analyzed a novel multi-layer convolutional neural network (CNN) to extract hierarchical features directly from the raw baseband samples. Moreover, we compared the training and testing accuracy of the CNN model for various decimation rates, input sample size and the number of convolutional layers. The results showed that the three-layer CNN model provided better classification accuracy with less computation cost. Furthermore, we observed that the MC performance of the proposed CNN model was better than the other deep learning (DL) and cumulant-based models.



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