A Variational Autoencoder-Based Secure Transceiver Design Using Deep Learning

Chia Hung Lin, Chao Chin Wu, Kuan Fu Chen, Ta-Sung Lee

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

To achieve new applications for 5G communications, physical layer security has recently drawn significant attention. In a wiretap channel system, our goal is to minimize information leakage to an eavesdropper while maximizing the performance of transmission to the desired or legitimate receiver. Complicated systems or channel models make it difficult to design secrecy systems based on the information theory. In this paper, we propose a deep learning-based transceiver design for secrecy systems as an alternative. Specifically, we modify the loss function design of a variational autoencoder, which is a special type of neural network, making it possible to provide both robust data transmission and security in an unsupervised fashion. We further investigate the impact of an imperfect channel state information and use simulation results to prove that our approach can outperform the existing learning-based methods.

原文English
主出版物標題2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁數7
ISBN(電子)9781728182988
DOIs
出版狀態Published - 7 12月 2020
事件2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan
持續時間: 7 12月 202011 12月 2020

出版系列

名字2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
2020-January

Conference

Conference2020 IEEE Global Communications Conference, GLOBECOM 2020
國家/地區Taiwan
城市Virtual, Taipei
期間7/12/2011/12/20

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