Signal automatic modulation based on AMC neural network fusion

Yin, Haoran; Diao, Junqin

Abstract

With the rapid development of modern communication technology, it has become a core problem in the field of communication to find new ways to effectively modulate signals and to classify and recognize the results of automatic modulation. To further improve the communication quality and system processing efficiency, this study combines two different neural network algorithms to optimize the traditional signal automatic modulation classification method. In this paper, the basic technology involved in the communication process, including automatic signal modulation technology and signal classification technology, is discussed. Then, combining parallel convolution and simple cyclic unit network, three different connection paths of automatic signal modulation classification model are constructed. The performance test results show that the classification model can achieve a stable training and verification state when the two networks are connected. After 20 and 29 iterations, the loss values are 0.13 and 0.18, respectively. In addition, when the signal-to-noise ratio (SNR) is 25dB, the classification accuracy of parallel convolutional neural network and simple cyclic unit network model is as high as 0.99. Finally, the classification models of parallel convolutional neural networks and simple cyclic unit networks have stable correct classification probabilities when Doppler shift conditions are introduced as interference in practical application environment. In summary, the neural network fusion classification model designed can significantly improve the shortcomings of traditional automatic modulation classification methods, and further improve the classification accuracy of modulated signals.

Citation

Yin, H., & Diao, J. (2024). Signal automatic modulation based on AMC neural network fusion. PLoS ONE, 19(6), Article e0304531. https://doi.org/10.1371/journal.pone.0304531

• Journal Article Type: Article
• Acceptance Date: May 14, 2024
• Online Publication Date: Jun 6, 2024
• Publication Date: Jun 6, 2024
• Deposit Date: Jun 10, 2024
• Publicly Available Date: Jan 9, 2025
• Journal: PLoS ONE
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 6
• Article Number: e0304531
• DOI: https://doi.org/10.1371/journal.pone.0304531
• Public URL: https://durham-repository.worktribe.com/output/2480456