English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recently, some researches are devoted to the topic of end-to-end learning aphysical layer secure communication system based on autoencoder under Gaussianwiretap channel. However, in those works, the reliability and security of theencoder model were learned through necessary decoding outputs of not onlylegitimate receiver but also the eavesdropper. In fact, the assumption of knowneavesdropper'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 thismethod is constructed only with the input and output signal samples of thelegal and eavesdropper channels and benefit that training the encoder iscompletely independent of the decoder. Moreover, since the design of securecoding does not rely on the eavesdropper's decoding results, the securityperformance would not be affected by the eavesdropper's decoding means.Numerical results show that the performance of our model is guaranteed whetherthe eavesdropper learns the decoder himself or uses the legal decoder.

Dual MINE-based Neural Secure Communications under Gaussian Wiretap Channel