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arXiv 2102.12918cs.ITcs.CRcs.LGmath.ITstat.ML
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Dual MINE-based Neural Secure Communications under Gaussian Wiretap Channel

Beijing University of Posts and Telecommunications(北京邮电大学);State Grid Fujian Electric Power Co.LTD(国网福建省电力有限公司);State Grid Hunan Electric Power Co.LTD(国网湖南省电力有限公司)

Jingjing Li, Zhuo Sun, Lei Zhang, Hongyu Zhu

英文摘要:

Recently, some researches are devoted to the topic of end-to-end learning a physical layer secure communication system based on autoencoder under Gaussian wiretap channel. However, in those works, the reliability and security of the encoder model were learned through necessary decoding outputs of not only legitimate receiver but also the eavesdropper. In fact, the assumption of known eavesdropper's decoder or its output is not practical. To address this issue, in this paper we propose a dual mutual information neural estimation (MINE) based neural secure communications model. The security constraints of this method is constructed only with the input and output signal samples of the legal and eavesdropper channels and benefit that training the encoder is completely independent of the decoder. Moreover, since the design of secure coding does not rely on the eavesdropper's decoding results, the security performance would not be affected by the eavesdropper's decoding means. Numerical results show that the performance of our model is guaranteed whether the eavesdropper learns the decoder himself or uses the legal decoder.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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