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Learning end-to-end codes for the BPSK-constrained Gaussian wiretap channel
Physical Communication ( IF 2.2 ) Pub Date : 2021-02-10 , DOI: 10.1016/j.phycom.2021.101282
Alireza Nooraiepour , Sina Rezaei Aghdam

Finite-length codes are learned for the Gaussian wiretap channel in an end-to-end manner assuming that the communication parties are equipped with deep neural networks (DNNs), and communicate through binary phase-shift keying (BPSK) modulation scheme. The goal is to find codes via DNNs which allow a pair of transmitter and receiver to communicate reliably and securely in the presence of an adversary aiming at decoding the secret messages. Following the information-theoretic secrecy principles, the security is evaluated in terms of mutual information utilizing a deep learning tool called MINE (mutual information neural estimation). System performance is evaluated for different DNN architectures, designed based on the existing secure coding schemes, at the transmitter. Numerical results demonstrate that the legitimate parties can indeed establish a secure transmission in this setting as the learned codes achieve points on almost the boundary of the equivocation region.



中文翻译:

学习BPSK约束的高斯窃听通道的端到端代码

假设通信双方都配备了深度神经网络(DNN),并通过二进制相移键控(BPSK)调制方案进行通信,则以端对端的方式为高斯窃听通道学习有限长度代码。目的是通过DNN查找代码,该代码允许一对发送器和接收器在存在旨在解密机密消息的对手的情况下可靠而安全地进行通信。遵循信息理论的保密原则,使用称为MINE(相互信息神经估计)的深度学习工具,根据相互信息对安全性进行评估。在发射机处,针对基于现有安全编码方案设计的不同DNN架构,评估了系统性能。

更新日期:2021-02-19
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