Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks
Automatic modulation classification (AMC) can play an important role in the timely identification of suspicious and unwanted signal activities to enable secure communication in future next-generation cellular networks. Moreover, AMC can detect the modulation scheme without even adding additional ove...
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/617d8ea16aaf4b29b305399a4fd1ac5a |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Automatic modulation classification (AMC) can play an important role in the timely identification of suspicious and unwanted signal activities to enable secure communication in future next-generation cellular networks. Moreover, AMC can detect the modulation scheme without even adding additional overhead in the signal. In this paper, we developed a universal software radio peripheral (USRP) based intelligent AMC system to detect and classify various digital modulation schemes in real-time. For each modulation scheme, we extracted different spectral features for different values of signal-to-noise ratio (SNR) values. Based on the extracted features, we train the neural network to classify the modulation schemes. Experimental results show that we achieve around 97% classification accuracy in real-time as compared to the existing offline classification schemes. Moreover, we also compare the performance of the proposed model with HisarMod2019.1 model in terms of various metrics such as cross-entropy and mean square error. Results clearly demonstrates the efficiency of the proposal for real-time implementation and classification. |
|---|