信道估计误差下的深度人工噪声预编码生成方法

基于人工噪声的物理层安全通信系统，传统人工噪声通常采用推导得到的闭式表达式或最优化方法数值求解生成，要求输入准确的传输信道矩阵信息，才能保证通信系统的保密性。但实际环境中存在的信道估计误差会导致人工噪声预编码误差，从而降低通信系统保密容量。为此提出一种基于深度学习的人工噪声预编码生成方法，通过将有误差的信道估计信息作为输入，与无估计误差情况下传统数值求解得到的预编码矩阵进行拟合，训练得到可适应信道估计误差的深度神经网络。仿真表明，该方法在信道估计有误差时的保密性能与鲁棒性优于传统人工噪声生成系统；相比于其他深度学习方法在物理层安全的应用，所提方法具有更快的收敛速度。

Deep artificial noise precoding generation method considering channel estimation error

For the physical layer security communication system based on artificial noise, traditional artificial noise is usually generated by using closed-form expressions derived from derivation or numerical optimization methods which both require accurate channel state information matrix to guarantee the secrecy of the communication system. However, the channel estimation error in the real scenarios causes the artificial noise precoding error to reduce the security capacity of the communication system. For this reason, this paper proposes an artificial noise precoding generation method based on deep learning. By taking the channel estimation information with estimation error as input and fitting it with the precoding matrix obtained by traditional numerical solution generated by perfect channel estimation, a well-trained deep neural network that can adapt to the channel estimation error is obtained. Simulation shows that the security performance and robustness of this method when there are errors in channel estimation are better than traditional artificial noise generation systems. Compared with other deep learning methods for physical layer security, the method proposed in this paper has faster convergence speed.