  Wireless communications are particularly vulnerable to eavesdropping attacks
due to their broadcast nature. To effectively deal with eavesdroppers, existing
security techniques usually require accurate channel state information (CSI),
e.g., for friendly jamming (FJ), and/or additional computing resources at
transceivers, e.g., cryptography-based solutions, which unfortunately may not
be feasible in practice. This challenge is even more acute in low-end IoT
devices. We thus introduce a novel deep learning-based FJ framework that can
effectively defeat eavesdropping attacks with imperfect CSI and even without
CSI of legitimate channels. In particular, we first develop an
autoencoder-based communication architecture with FJ, namely AEFJ, to jointly
maximize the secrecy rate and minimize the block error rate at the receiver
without requiring perfect CSI of the legitimate channels. In addition, to deal
with the case without CSI, we leverage the mutual information neural estimation
(MINE) concept and design a MINE-based FJ scheme that can achieve comparable
security performance to the conventional FJ methods that require perfect CSI.
Extensive simulations in a multiple-input multiple-output (MIMO) system
demonstrate that our proposed solution can effectively deal with eavesdropping
attacks in various settings. Moreover, the proposed framework can seamlessly
integrate MIMO security and detection tasks into a unified end-to-end learning
process. This integrated approach can significantly maximize the throughput and
minimize the block error rate, offering a good solution for enhancing
communication security in wireless communication systems.
