论文标题
学习BPSK受限的高斯窃听频道的端到端代码
Learning End-to-End Codes for the BPSK-constrained Gaussian Wiretap Channel
论文作者
论文摘要
假设通信方配备了深层神经网络（DNN），并且通过二进制相移键合（BPSK）调制方案进行通信，以端到端的方式学习了高斯窃听通道的有限长度代码。目的是通过DNN找到代码，该代码允许一对发射器和接收器在有旨在解码秘密消息的对手的情况下可靠，安全地通信。遵循信息理论的保密原则，使用称为Mine的深度学习工具（相互信息神经估计）来评估安全性。根据发射机的现有安全编码方案设计的不同DNN体系结构对系统性能进行了评估。数值结果表明，合法方确实可以在此设置中建立安全的传输，因为学习的代码几乎在模棱两可区域的边界上达到了点。
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.
