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IEEE Wireless Communications and Networking Conference

RIS-Assisted Physical Layer Key Generation and Transmit Power Minimization

Liang Jin, Xiaodong Xu, Shujun Han, Jinghang Liu, Rui Meng, Hao Chen

Published Apr 10, 2022• 14 citations
Publisher

Abstract

Key generation rate and bit disagreement ratio are two main indicators to evaluate the performance of physical layer key generation (PLKG). This work explores the key generation performance in reconfigurable intelligent surface (RIS)-assisted PLKG, where a concrete scheme of key generation is proposed. Specifically, we first deduce the key generation rate expression based on estimation theory in RIS-assisted PLKG. Then we optimize the RIS reflecting coefficients with instantaneous channel state information to maximize the key generation rate. Finally, aiming at minimizing transmit power while guaranteeing key generation rate target, we formulate a power minimization problem in RIS-assisted PLKG. An alternating optimization algorithm is applied to solve the non-convex mixed-integer nonlinear programming. Simulation results demonstrate that the key generation rate of the proposed scheme is up to 197.5% higher than the existing relay-assisted scheme, and we successfully reduce the required transmit power by at least 6.6 dBm than without power minimization scheme.

DOI
10.1109/wcnc51071.2022.9771815

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arXiv:2207.11752•Globecom•Dec 4, 2022•17 citations
Key generation rate and bit disagreement ratio are two main indicators to evaluate the performance of physical layer key generation (PLKG). This work explores the key generation performance in reconfigurable intelligent surface (RIS)-assisted PLKG, where a concrete scheme of key generation is proposed. Specifically, we first deduce the key generation rate expression based on estimation theory in RIS-assisted PLKG. Then we optimize the RIS reflecting coefficients with instantaneous channel state information to maximize the key generation rate. Finally, aiming at minimizing transmit power while guaranteeing key generation rate target, we formulate a power minimization problem in RIS-assisted PLKG. An alternating optimization algorithm is applied to solve the non-convex mixed-integer nonlinear programming. Simulation results demonstrate that the key generation rate of the proposed scheme is up to 197.5% higher than the existing relay-assisted scheme, and we successfully reduce the required transmit power by at least 6.6 dBm than without power minimization scheme.
In quasi-static environments, the key generation rate (KGR) of wireless physical layer key generation (PLKG) can be greatly limited. In this paper, we aim to improve the KGR by reconfigurable intelligent surface (RIS) and jointly design the quantizer and phase shift matrix. We give a reconfiguration strategy able to quantify both real and imaginary parts at the same time and give its closed-form solution of the phase shift matrix. To reduce the bit disagreement ratio (BDR), we design a quantizer suitable for RIS-assisted PLKG which uses non-uniform quantization. To achieve high KGR while maintain low BDR, we analyze the BDR in different quantization levels and realize an adaptive quantization selection. Meanwhile, the simulation results verify that the KGR of the proposed scheme is more than twice that of the existing scheme under a certain BDR threshold.
By exploiting the entropy of wireless channels, physical layer key generation (PLKG) has gained considerable attention in recent years. Due to the characteristic of dynamic controlling of wireless channels, reconfigurable intelligent surface (RIS) can improve the key generation rate (KGR). Simultaneous transmission and reflection reconfigurable intelligent surface (STAR- RIS) can extend the half-space coverage to full-space coverage. This paper proposes a STAR-RIS-assisted physical layer key generation (SRKG) scheme in a multi-user case. The objective is to maximize the sum KGR by jointly designing the transmitting and reflecting coefficients (TARCs). We propose an iterative algorithm based on alternating optimization (AO), utilizing successive convex approximation (SCA) and semi-definite relaxation (SDR) methods, to solve the non-convex optimization problem in the presence of correlated channels. We also propose a low-complexity algorithm based on Lagrange multipliers to jointly optimize TARCs considering independent fading. Finally, the simulation results validate that, compared with the PLKG scheme assisted by traditional RIS (T-RIS), the proposed SRKG scheme can achieve higher sum KGR in a wider range under correlated and independent channel conditions.
Recently, there has been a growing interest in utilizing reconfigurable intelligent surfaces (RIS) for physical layer key generation (PLKG) due to their ability to intelligently configure the wireless medium. However, to the best of our knowledge, no study has explored the potential of active RIS (ARIS) in aiding PLKG. Motivated by this gap, this letter investigates the application of ARIS in PLKG. Furthermore, most existing RIS-aided PLKG techniques are based on the idealistic assumption of independent and identically distributed (i.i.d.) fading model at the RIS. Accordingly, we further consider the intrinsic correlated fading among the RIS elements. We derive the secret key rate (SKR) of the ARIS-aided PLKG and propose a sequential convex approximation (SCA) based optimization method to jointly optimize the phase and gain of the ARIS to reach maximum SKR. Additionally, we compare the performance of passive RIS (PRIS) aided PLKG with ARIS-aided PLKG. We show that with an equal total power budget, ARIS leads to significantly higher SKR, thereby offering promisingly high energy efficiency (EE) compared to PRIS.
Reconfigurable intelligent surface (RIS) has been widely studied as a promising approach in improving secret key rate (SKR) for physical layer (PHY) secret key generation (SKG). Existing RIS-assisted PHY SKG studies mainly focus on improving the SKR by optimizing RIS phase. However, RIS can also be exploited by active attackers to contaminate the channel probing. As a result, there is a trade-off between the increased randomness and the improved correlation between legitimate channels and eavesdropping channels in a RIS-assisted SKG system under channel contamination. In this paper, we study the impact of RIS on PHY SKG by analyzing the SKR performance. A closed-form expression for the lower-bound of secret key capacity is obtained. Theoretical analysis shows that gains of legitimate channel, eavesdropping channel, and RIS-induced channels are key parameters in determining whether the assisting RIS is beneficial or harmful to the PHY SKG. Further, a SKR maximization problem under channel contamination is proposed and solved. Results show that the assisting RIS is definitely harmful to the SKG when the gain ratio of legitimate channel and eavesdropping channel is reduced with the leverage of RIS, while it can significantly improve the SKR with an optimized RIS phase when the gain ratio is improved with the help of RIS.
Physical-layer key generation (PLKG) is an information-theoretic security technique aimed at addressing key distribution challenges among resource-constrained legitimate users in the Internet of Things (IoT). However, in quasi-static or blocked environments, key generation faces significant limitations owing to its low entropy. To overcome this challenge, we propose leveraging a simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS). In this study, we introduce a STAR-RIS-aided multiuser PLKG framework and formulate an optimization problem focused on minimizing the average bit disagreement ratio through optimal phase-shift matrix adjustments. Initially, we derive closed-form solutions for optimal phase shifts, simplifying the problem to depend solely on amplitude coefficients. We then employ a Newton-Raphson algorithm under the energy-splitting (ES) protocol and a traversal method under the mode-switching (MS) and time-switching (TS) protocols to solve the optimization problem. Simulation results confirm the efficacy of our proposed schemes by comparison with existing benchmark schemes.
Reconfigurable intelligent surface (RIS) is a promising technique to enhance the performance of physical-layer key generation (PKG) due to its ability to smartly customize the radio environments. Existing RIS-assisted PKG methods are mainly based on the idealistic assumption of an independent and identically distributed (i.i.d.) channel model at both the transmitter and the RIS. However, the i.i.d. model is inaccurate for a typical RIS in an isotropic scattering environment. Also, neglecting the existence of channel spatial correlation would degrade the PKG performance. In this paper, we establish a general spatially correlated channel model in multi-antenna systems and propose a new PKG framework based on the transmit and the reflective beamforming at the base station (BS) and the RIS. Specifically, we derive a closed-form expression for characterizing the key generation rate (KGR) and obtain a globally optimal solution of the beamformers to maximize the KGR. Furthermore, we analyze the KGR performance difference between the one adopting the assumption of the i.i.d. model and that of the spatially correlated model. It is found that the beamforming designed for the correlated model outperforms that for the i.i.d. model while the KGR gain increases with the channel correlation. Simulation results show that compared to existing methods based on the i.i.d. fading model, our proposed method achieves about 5 dB performance gain when the BS antenna correlationρ\rhoρis 0.3 and the RIS element spacing is half of the wavelength.