Radio Classify Generative Adversarial Networks: A Semi-supervised Method for Modulation Recognition
| Autor: | Guangyi Liu, Yifan Wu, Shuntao Li, Mingxuan Li |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science Deep learning 020208 electrical & electronic engineering 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Adversarial system Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Artificial intelligence business computer Generative adversarial network Generative grammar |
| Zdroj: | 2018 IEEE 18th International Conference on Communication Technology (ICCT). |
| DOI: | 10.1109/icct.2018.8600032 |
| Popis: | We introduce Generative Adversarial Network (GAN) into the radio machine learning domain for the task of modulation recognition by proposing a general, scalable, end-to-end framework named Radio Classify Generative Adversarial Networks (RCGANs). This method naively learns its features through self-optimization during an extensive data-driven GPU-based training process. Several experiments are taken on a synthetic radio frequency dataset, simulation results show that, compared with some renowned deep learning methods and classic machine learning methods, the proposed method achieves higher or equivalent classification accuracy, superior data utilization, and presents robustness against noises. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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