This paper considers a communication system whose source can learn from channel-related data, thereby making a suitable choice of system parameters for security improvement. The security of the communication system is optimized using deep neural networks (DNNs). More explicitly, the associated security vs reliability trade-off problem is characterized in terms of the symbol error probabilities and the discrete-input continuous-output memoryless channel (DCMC) capacities. A pair of loss functions were defined by relying on the Lagrangian and on the monotonic-function based techniques. These were then used for managing the learning/training process of the DNNs for finding near-optimal solutions to the associated non-convex problem. The Lagrangian technique was shown to approach the performance of the exhaustive search. We concluded by characterizing the security vs reliability trade-off in terms of the intercept probability vs the outage probability.

中文翻译：

---

深度学习辅助物理层安全：安全性与可靠性的权衡


本文考虑了一种通信系统，其源可以从信道相关数据中学习，从而做出适当的系统参数选择以提高安全性。使用深度神经网络（DNN）优化通信系统的安全性。更明确地，相关的安全性与可靠性权衡问题的特征在于符号错误概率和离散输入连续输出无记忆通道（DCMC）容量。一对损失函数是通过依赖拉格朗日和基于单调函数的技术来定义的。然后将它们用于管理 DNN 的学习/训练过程，以找到相关非凸问题的近乎最佳解决方案。拉格朗日技术被证明可以接近穷举搜索的性能。我们通过根据拦截概率与中断概率来描述安全性与可靠性的权衡来得出结论。