Data-Limited Modulation Classification with a CVAE-Enhanced Learning Model

Xuefei Ji, Jue Wang, Ye Li, Qiang Sun, Shi Jin, Tony Q.S. Quek

研究成果: Article同行評審

摘要

The modulation classification problem can be effectively handled by modern machine learning techniques, however, the available data for training is usually limited due to costly measurements in practice. Concerning the problem of insufficient training data, we propose a conditional variational auto-encoder (CVAE)-enhanced learning model for modulation classification. Different from the conventional data augmentation approaches where the data is independently generated, we introduce a feedback unit from the classifier to the CVAE generative network, to ensure that the generated data helps improving the classification accuracy. A two-stage generating-training algorithm is proposed. Via both simulation and practical implementation on a universal software radio peripheral (USRP) platform, it is shown that the proposed method effectively improves the classification accuracy in realistic propagation environment.

原文English
文章編號9125974
頁(從 - 到)2191-2195
頁數5
期刊IEEE Communications Letters
24
發行號10
DOIs
出版狀態Published - 2020 十月

All Science Journal Classification (ASJC) codes

  • 建模與模擬
  • 電腦科學應用
  • 電氣與電子工程

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