CyPA: A Cyclic Prefix Assisted DNN for Protocol Classification in Shared Spectrum
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2024 International Conference on Computing, Networking and Communications (ICNC), 2024, 00, pp. 629-634
- Issue Date:
- 2024-06-21
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| CyPA_A_Cyclic_Prefix_Assisted_DNN_for_Protocol_Classification_in_Shared_Spectrum.pdf | Accepted version | 366.2 kB |
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To monitor RF activity and coordinate access to a channel that is shared by heterogeneous wireless systems network administrators and or users must be able to identify observed transmissions rapidly and accurately Recent research shows that deep neural networks DNNs can identify the underlying waveform of an RF signal based on the in phase quadrature I Q samples without decoding them Such DNNs take as input a fixed size window of I Q samples To utilize the temporal features at various scales and improve the classification accuracy we propose a two stage DNN classification structure In the first stage DNN is designed to detect and classify long term periodic features such as the cyclic prefix CP The output of this classifier is then used as a latent variable for a second stage protocol technology classifier To evaluate this model we consider spectrum sharing between Wi Fi LTE License Assisted Access LAA and 5G NR unlicensed NR U over the unlicensed 5GHz bands Compared to the ResNet 18 1D the proposed two stage approach improves the classification accuracy from 71 to 90 while reducing the trainable parameters from 3 8 to 1 8 million As a result our compact design is more accurate and energy efficient than computational intensive DNNs for mobile devices
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