Intelligent Reflecting Surface for Wireless Communication Security and Privacy

TL;DR

This paper explores how intelligent reflecting surfaces (IRS) can enhance wireless security and privacy by improving secrecy rates, enabling covert communications, and creating transmission randomness, while addressing challenges like optimal amplitude design and CSI estimation.

Contribution

It introduces new insights into IRS-based physical layer security and covert communications, including amplitude optimization, tradeoffs with CSI accuracy, and a deep learning-based channel estimation method.

Findings

Optimal IRS element amplitudes vary and are not always 1.

Tradeoff exists between secrecy performance and CSI estimation accuracy.

Deep neural network-based channel estimation can improve covert communication rates.

Abstract

Intelligent reflection surface (IRS) is emerging as a promising technique for future wireless communications. Considering its excellent capability in customizing the channel conditions via energy-focusing and energy-nulling, it is an ideal technique for enhancing wireless communication security and privacy, through the theories of physical layer security and covert communications, respectively. In this article, we first present some results on applying IRS to improve the average secrecy rate in wiretap channels, to enable perfect communication covertness, and to deliberately create extra randomness in wireless propagations for hiding active wireless transmissions. Then, we identify multiple challenges for future research to fully unlock the benefits offered by IRS in the context of physical layer security and covert communications. With the aid of extensive numerical studies, we demonstrate the necessity of designing the amplitudes of the IRS elements in wireless communications with the consideration of security and privacy, where the optimal values are not always $1$ as commonly adopted in the literature. Furthermore, we reveal the tradeoff between the achievable secrecy performance and the estimation accuracy of the IRS's channel state information (CSI) at both the legitimate and malicious users, which presents the fundamental resource allocation challenge in the context of IRS-aided physical layer security. Finally, a passive channel estimation methodology exploiting deep neural networks and scene images is discussed as a potential solution to enabling CSI availability without utilizing resource-hungry pilots. This methodology serves as a visible pathway to significantly improving the covert communication rate in IRS-aided wireless networks.