  A novel precoding method based on supervised deep neural networks is
introduced for the multiple-input multiple-output Gaussian wiretap channel. The
proposed deep learning (DL)-based precoding learns the input covariance matrix
through offline training over a large set of input channels and their
corresponding covariance matrices for efficient, reliable, and secure
transmission of information. Furthermore, by spending time in offline training,
this method remarkably reduces the computation complexity in real-time
applications. Compared to traditional precoding methods, the proposed DL-based
precoding is significantly faster and reaches near-capacity secrecy rates.
DL-based precoding is also more robust than transitional precoding approaches
to the number of antennas at the eavesdropper. This new approach to precoding
is promising in applications in which delay and complexity are critical.
