Deep SIMO Auto-Encoder and Radio Frequency Hardware Impairments Modeling for Physical Layer Security

TL;DR

This paper introduces a SIMO autoencoder approach that models RF hardware impairments to enhance physical layer security and robustness in wireless communication, outperforming traditional decoders and preventing eavesdropping.

Contribution

It proposes a novel end-to-end learning model that incorporates RF hardware impairments for improved security and performance in wireless systems.

Findings

AE-based SIMO model outperforms linear decoders in BER

Model enhances security against eavesdroppers

RF impairments can be leveraged for device identification

Abstract

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.