MOD3NN: A Framework for Automatic Signal Modulation Detection Using 3D CNN
In this work, we present an application of a three-dimensional convolutional neural network for the task of automatic modulation recognition from raw I/Q signal data. Raw I/Q signal data exhibits a special “helical” structure that can be exploited with three-dimensional convolutions (3D convolution...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2023-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/133383 |
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| Summary: | In this work, we present an application of a three-dimensional convolutional neural network for the task of automatic modulation recognition from raw I/Q signal data. Raw I/Q signal data exhibits a special “helical” structure that can be exploited with three-dimensional convolutions (3D convolutions) to learn spatio-temporal features from the signal for the problem of modulation recognition. By tweaking the convolutional filters to learn the helical symmetry of the data, we can design a shallow network for automatic modulation recognition (AMR). We present the results of our experiments with raw I/Q signal data collected in an uncalibrated radio frequency (RF) environment using several different modulation schemes. We show that with our methods and implementation, we can achieve around 99 % accuracy for automatic modulation recognition, for a variety of practical modulation techniques without the need for explicit feature engineering. |
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| ISSN: | 2334-0754 2334-0762 |