Dual MINE-based Neural Secure Communications under Gaussian Wiretap Channel
This addresses a practical limitation in secure communication systems for scenarios where eavesdropper decoder information is unavailable, though it is an incremental improvement over existing methods.
The paper tackles the problem of impractical assumptions in neural secure communications by proposing a dual MINE-based model that trains the encoder independently of the decoder, using only channel samples. Numerical results show that security performance is maintained regardless of the eavesdropper's decoding method.
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.