Modulation classification analysis of CNN model for wireless communication systems

Tamizhelakkiya K; Sabitha Gauni; Prabhu Chandhar; Tamizhelakkiya K; Sabitha Gauni; Prabhu Chandhar

doi:10.3934/electreng.2023018

AIMS Electronics and Electrical Engineering

2023, Volume 7, Issue 4: 337-353. doi: 10.3934/electreng.2023018

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Research article

Modulation classification analysis of CNN model for wireless communication systems

1.
Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, Tamil Nadu, India
2.
Autosys Control Systems India Pvt Ltd, Chennai, Tamil Nadu, India
3.
Chandhar Research Labs Pvt Ltd, Chennai, Tamil Nadu, India

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.
- modulation classification (MC),
- CNN,
- deep learning (DL),
- feature map,
- neural network
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

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Abstract

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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