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International Conference on Microwave and Millimeter Wave Technology (ICMMT)

Reconfigurable Intelligent Surface Assisted Physical Layer Key Generation from Wireless Channels

Zheng Wan, Mengyao Yan, Wenyu Jiang, Kaizhi Huang, Liang Jin, Fan Wu

Published May 14, 2023• 0 citations
Publisher

Abstract

This paper proposes a reconfigurable intelligent surface (RIS) assisted physical layer key generation (PKG) scheme to increase the key generation rate (KGR). Firstly, we provide a high-rate key generation protocol that rapidly and randomly switches the phase of RIS elements. Then, we analyze the key information leaked to passive eavesdroppers and give the closed-form expression of KGR when the number of RIS elements is sufficiently large. Monte Carlo simulation and numerical results show that our proposed scheme can boost KGR in a static environment.

DOI
10.1109/icmmt58241.2023.10276730

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IEEE Wireless Communications Letters•Aug 16, 2024•1 citation
This paper proposes a reconfigurable intelligent surface (RIS) assisted physical layer key generation (PKG) scheme to increase the key generation rate (KGR). Firstly, we provide a high-rate key generation protocol that rapidly and randomly switches the phase of RIS elements. Then, we analyze the key information leaked to passive eavesdroppers and give the closed-form expression of KGR when the number of RIS elements is sufficiently large. Monte Carlo simulation and numerical results show that our proposed scheme can boost KGR in a static environment.
This paper presents an optimal shifting scheme using reconfigurable intelligent surfaces (RIS) to aid physicallayer key generation (PKG) and enhance communication and key generation rate (KGR) in static environments. Our approach involves designing a protocol for RIS-aided key generation and optimizing RIS coefficients to introduce phase randomization in the wireless channel while maximizing power reception. Simulation results demonstrate the effectiveness of our proposed protocol in improving both communication performance and KGR under static conditions.
Physical layer key generation (PKG) is a promising means to provide on-the-fly shared secret keys by exploiting the intrinsic randomness of the radio channel. However, the performance of PKG is highly dependent on the propagation environments. Due to its feature of controlling the wireless environment, reconfigurable intelligent surface (RIS) is appealing to be applied in PKG. In this article, in contrast to the existing literature, we investigate both the constructive and destructive effects of RIS on the PKG scheme. For the constructive aspect, we have identified static and wave-blockage environments as two RIS-empowered-PKG applications in future wireless systems. In particular, our experimental results in a static environment showed that RIS can enhance the entropy of the secret key, achieving a key generation rate (KGR) of 97.39 bit/s with a bit disagreement rate (BDR) of 0.083. In multi-user systems where some remote users are in worse channel conditions, the proposed RIS-assisted PKG algorithm improves the sum secret key rate by more than 2 dB, compared to the literature. Furthermore, we point out that RIS could be utilized by an attacker to perform new jamming and leakage attacks and give countermeasures, respectively. Finally, we outline future research directions for PKG systems in light of the RIS.
We propose a key generation protocol with the aid of a reconfigurable intelligent surface (RIS) to boost secret key rate (SKR) in quasi-static environments. Considering a passive eavesdropper, we derive the closed-form expression of the lower and upper bounds of the SKR. Our findings indicate the SKR is determined by the number of RIS elements, the correlation coefficient, the pilot length and the quality of the reflecting channel. Our protocol fully exploits the randomness from the direct channel and the reflecting channel. Monte Carlo simulations validate the analytical expression of the SKR and demonstrate our protocol outperforms existing work.
Reconfigurable intelligent surfaces (RIS) have the ability to alter the wireless environment by making changes in the impinging signal. While RIS has been extensively studied for enhancing wireless communications, its potential for facilitating group key generation (GKG) remains unexplored. In this study, we exploit the RIS to make the aggregate reflecting channels of different user terminals (UTs) as similar as possible to be able to extract common group secret keys from their channels. Specifically, the RIS will adjust its parameters to pave the way for GKG based on the physical channels of the UTs. Our method exploits the already gathered channel state information (CSI) in the RIS to beneficially design the phase shifts and does not impose additional probing burden on the network. We consider both passive RIS (PRIS) and active RIS (ARIS) to generate the group keys. The PRIS is widely adopted in physical layer key generation (PLKG) studies due to its use of passive elements, whereas the ARIS demonstrates superior capability in aligning the aggregate reflected channels among nodes in the GKG scenario, as demonstrated in this study. We will exploit various optimization methods like successive convex approximation (SCA) and semidefinite relaxation with Gaussian randomization (SDR-GR) to address the raised optimization problems. Unlike most of the studies in the literature, our scheme can achieve a high GKG rate in static environments as well. Finally, we will examine the performance of the proposed method by normalized mean squared error (NMSE), key error rate (KER), key generation rate (KGR) and key randomness metrics. Our numerical results verify that for the equal available power budget, the ARIS significantly outperforms PRIS in NMSE and KER, achieving more than four times higher KGR.
Physical-layer key generation (PLKG) can significantly enhance the security of classic encryption schemes by efficiently providing secret keys in resource-limited network like the Internet of Things (IoT). However, reaching a high key generation rate (KGR) is challenging in applications like smart home or remote area sensing with quasi-static channels. Recently, exploiting reconfigurable intelligent surface (RIS) to induce randomness in quasi-static wireless channels has received significant research interest. However, the inherent spatial correlation among the RIS elements is rarely studied, which can alter the optimum physical-layer key generation (PLKG) approach in terms of KGR and randomness in the key sequence. Specifically, for the first time, in this contribution, we take into account a spatially correlated RIS, which intends to enhance the KGR in a quasi-static medium. Novel closed-form analytical expressions for KGR are derived for the two cases of random phase shift (RPS) and our proposed equal phase shift (EPS) in the RIS elements. We also analyze the correlation between the channel samples to ensure the randomness of the generated secret key sequence. It is shown that the EPS scheme can effectively exploit the inherent spatial correlation between the RIS elements and it leads to a higher KGR compared to the widely used RPS strategy. We further formulate an optimization problem in which we determine the optimal portion of time dedicated to direct and indirect channel estimation, which has never been addressed in previous studies. We show the accuracy and the fast convergence of our sequential convex programming (SCP)-based algorithm and discuss the various parameters affecting spatially correlated RIS-assisted PLKG.
Physical layer key generation (PLKG) is a promising method to generate secret keys by harnessing the randomness of wireless channels. In this letter, a reconfigurable intelligent surface (RIS)-assisted multi-carrier PLKG framework is proposed to overcome the lack of channel randomness in static environments. Specifically, the secret key generation rate (SKGR) is analyzed based on the inter-frequency correlation among the channel coefficients across subcarriers. Building upon this analysis, a novel scheme for configuring RIS elements is proposed to enhance the SKGR by introducing correlations into the random phase shifts of RIS elements. Simulation results are presented to validate the effectiveness of the proposed scheme.