  This paper presents a novel approach to achieving secure wireless
communication by leveraging the inherent characteristics of wireless channels
through end-to-end learning using a single-input-multiple-output (SIMO)
autoencoder (AE). To ensure a more realistic signal transmission, we derive the
signal model that captures all radio frequency (RF) hardware impairments to
provide reliable and secure communication. Performance evaluations against
traditional linear decoders, such as zero-forcing (ZR) and linear minimum mean
square error (LMMSE), and the optimal nonlinear decoder, maximum likelihood
(ML), demonstrate that the AE-based SIMO model exhibits superior bit error rate
(BER) performance, but with a substantial gap even in the presence of RF
hardware impairments. Additionally, the proposed model offers enhanced security
features, preventing potential eavesdroppers from intercepting transmitted
information and leveraging RF impairments for augmented physical layer security
and device identification. These findings underscore the efficacy of the
proposed end-to-end learning approach in achieving secure and robust wireless
communication.
