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.
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.
To address the problem of low secret key rate (SKR) for quasi-static environments, a secret key generation protocol assisted by Reconfigurable Intelligent Surface (RIS) and artificial noise is proposed. On the one hand, RIS is used to construct the fast-changing channel, and on the other hand, artificial noise is designed to disturb the eavesdropper, both of which boost the SKR. Then, the closed-form expressions for the lower and upper bounds of the secret key capacity of the protocol are derived. Based on this expression, the impact of pilot and artificial noise power allocation on the key capacity is analyzed and the pilot power ratio is optimized. In addition, the influences of RIS elements number, channel correlation coefficient and pilot length on the SKR are further analyzed. Finally, the simulation analysis shows that compared to existing works, the proposed protocol reduces information leakage and further increases the SKR.
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.
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.
Abstract In this paper, we introduce a new method to solve the low-entropy problem in a quasi-static environment. We utilize a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) that transmits and reflects incoming signals to users located on different sides of the surface. Specifically, we design the flow of multi-user channel probing when STAR-RIS is assisted, analyze and derive the analytical expression of the secret key capacity (SKC) of STAR-RIS-assisted system from the perspective of information theory, and finally analyze how to maximize the SKC and sum of all users under three different working protocols of STAR-RIS. The simulation results show that: (1) STAR-RIS can achieve better SKC performance than the conventional reflection/transmission RIS; and (2) the SKC is determined by the number of STAR-RIS elements, correlation coefficient, pilot length and RIS-reflected channel quality, and the time switching protocol can obtain higher SKC than the energy splitting protocol and mode switching protocol.
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 secret key generation schemes can provide lightweight encryption for resource constrained Inter?net of Things (IoT) devices. However, many IoT devices are stationary and the environment is usually static, which leads to ultra-low/zero secret key rates. To circumvent this problem, a novel secret key generation scheme based on Reconfigurable Intelligent Surface (RIS) is proposed for Multiple-Input Single?Output IoT communications. The RIS is introduced into the network, and the phase shifts at RIS are tuned randomly in each frame, which superimpose time-varying reflection channels on the orginal channels. And then the secret key capacity of all nodes within the cell coverage area can be remarkably improved. The closed-form expressions of secret key capacity taking into account eavesdropping are first derived for the case studies of independent and correlated channels. We can conclude that sharing the same phase shifts randomness will not bring key information leakage. Compared with three benchmark schemes, the proposed scheme can both maintain good performance whenever eavesdropping nodes exist. Further, the impact of parameters, such as the number of reflecting units, the transmit antennas and the Rician factor, on secret key performance are analyzed respectively, which can offer guidelines for system design. Finally, simulation results verify the theoretical analysis.