Deep Artificial Noise: Deep Learning-Based Precoding Optimization for Artificial Noise Scheme

Abstract

In this work, we consider a secure precoding optimization problem for the artificial noise (AN) scheme in multiple-input single-output (MISO) wiretap channels. In previous researches (Lin et al., 2013), it was proved that the generalized AN scheme which allows some portion of AN signal to be injected to the legitimate receiver's channel is the optimal precoding scheme for MISO wiretap channels. However, the optimality is valid only under some ideal assumptions such as perfect channel estimation and spatially uncorrelated channels. To break through this limitation, in this paper, we propose a novel deep neural network (DNN)-based secure precoding scheme, called the deep AN scheme. To the best of the authors' knowledge, the deep AN scheme is the first secure precoding scheme which exploits a DNN to jointly design and optimize the precoders for the information signal and the AN signal. From the numerical experiments, it is demonstrated that the proposed deep AN scheme outperforms the generalized AN scheme under various practical wireless environments.