ASAP: Adversarial Learning Based Secure Autoprecoder Design for MIMO Wiretap Channels

To avoid the non-convex and complicated secrecy rate optimization in the multiple-input and multiple-output (MIMO) system, deep learning (DL) is introduced in the secure precoder design recently. However, existing works neglect the eavesdropper with learning capacity, which is constructed by neural networks (NNs) and capable of enhancing its receiver NN according to the optimal secure transmitter. In this letter, we propose an adversarial learning-based secure autoprecoder (ASAP) for the MIMO wiretap channels, where the modulation and the precoder are jointly optimized and adversarially trained with the eavesdropping receiver. Numerical results verify the convergence of the ASAP method, which enables an optimal trade-off between the information secrecy and reliability. Moreover, joint optimization is especially important against the evolved eavesdropper.