Deep Learning Based Automatic Modulation Recognition Using GELU Activation Function
Altuntas, Bilge Erdem; Aksu, Omer; Celik, Melih Yigitcan; Durak, Mehmet Hakan (2024-08-27)
IEEE
27.08.2024
B. E. Altuntas, O. Aksu, M. Y. Celik and M. H. Durak, "Deep Learning Based Automatic Modulation Recognition Using GELU Activation Function," 2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA), Sana'a, Yemen, 2024, pp. 1-4, doi: 10.1109/eSmarTA62850.2024.10638998.
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202412097129
https://urn.fi/URN:NBN:fi:oulu-202412097129
Tiivistelmä
Abstract
One promising technique for detecting signal modulation schemes in cognitive radio networks is automatic modulation recognition (AMR). AMR based on high-performance deep learning (DL) techniques have been made easier recently by the growing research on DL. But, as DL is evolving daily, AMR approaches must perform better, and new approaches must be developed. This research presents a new DL based technique for AMR used in modern communication systems' cognitive radio networks. To simultaneously learn the spatio-temporal signal correlations with Gaussian Error Linear Unit (GELU) activation function, the network architecture is built with multiple distinct convolutional blocks. The suggested technique achieves an overall 6-modulation classification rate of 80% at 20 dB SNR in the simulations performed with the generated dataset.
One promising technique for detecting signal modulation schemes in cognitive radio networks is automatic modulation recognition (AMR). AMR based on high-performance deep learning (DL) techniques have been made easier recently by the growing research on DL. But, as DL is evolving daily, AMR approaches must perform better, and new approaches must be developed. This research presents a new DL based technique for AMR used in modern communication systems' cognitive radio networks. To simultaneously learn the spatio-temporal signal correlations with Gaussian Error Linear Unit (GELU) activation function, the network architecture is built with multiple distinct convolutional blocks. The suggested technique achieves an overall 6-modulation classification rate of 80% at 20 dB SNR in the simulations performed with the generated dataset.
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