Aiming at the problems of slow channel changes and low key generation rate in IoT quasi-static scenarios, a key generation method based on reconfigurable intelligent surface (RIS) was proposed.First, the agility characteristics of RIS was used to construct a fast-changing channel.Then, the base station and legitimate users extracted a consistent key from the channel information through channel estimation, conversion, and information negotiation.Finally, optimal time allocation for data transmission and key generation in the coherent time to achieve the maximum transmission rate of one-time pad.The simulation results show that the key generation rate of proposed method is higher than that of the existing relay-assisted method, random signal flow method and random number method, and as the number of RIS reflection units and frequency of channel estimation in the coherence time increase, the key generation rate will be further improve.
Aiming at the problems of slow channel changes and low key generation rate in IoT quasi-static scenarios, a key generation method based on reconfigurable intelligent surface (RIS) was proposed.First, the agility characteristics of RIS was used to construct a fast-changing channel.Then, the base station and legitimate users extracted a consistent key from the channel information through channel estimation, conversion, and information negotiation.Finally, optimal time allocation for data transmission and key generation in the coherent time to achieve the maximum transmission rate of one-time pad.The simulation results show that the key generation rate of proposed method is higher than that of the existing relay-assisted method, random signal flow method and random number method, and as the number of RIS reflection units and frequency of channel estimation in the coherence time increase, the key generation rate will be further improve.
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.
A conventional secret key generation scheme achieves a low secret key rate in static environments. To solve this problem, the reconfigurable intelligent surface (RIS) antenna is merged with the device at the base station and is firstly proposed for secret key generation. By tuning the phase shifts at the RIS, a random varying beam can be generated and combined with the original wireless channel to form a new equivalent channel so that the key capacity is not subject to the time variance of the wireless channel. Then the closed-form solution is derived. Theoretical analysis and simulation show that the performance can be increased to that of a fast varying channel with limited radiating units. Moreover, the proposed scheme is superior to the existing schemes, in which the RIS is used as a reflective surface, over performance gain and preventing information disclosure. To further develop the gain of RIS, a joint design scheme is proposed, where some units are used for key generation, and some units are used for beamforming. Finally, the joint's closed-form solution scheme is derived. Finally, on the basis of satisfying the key capacity and communication performance, an optimal resource allocation strategy is proposed. Simulation results illustrate that the key capacity and communication performance can be improved simultaneously.
Physical layer secret key generation schemes can provide lightweight encryption for resource-constrained Internet 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 makes time-varying reflection channels superimposed on the original channels. And then, the secret key capacity of all nodes within the cell coverage area can be remarkably improved. Compared with the existing methods, the scheme is loosely coupled with the existing communication system. 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 existing user-introduced randomness schemes, the proposed scheme can maintain good performance as the number of eavesdropping nodes increases. 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.
Physical layer secret key generation schemes can provide lightweight encryption for resource-constrained Internet 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 makes time-varying reflection channels superimposed on the original channels. And then, the secret key capacity of all nodes within the cell coverage area can be remarkably improved. Compared with the existing methods, the scheme is loosely coupled with the existing communication system. 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 existing user-introduced randomness schemes, the proposed scheme can maintain good performance as the number of eavesdropping nodes increases. 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.
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.
In quasi-static wireless channel scenarios, the generation of physical layer keys faces the challenge of invariant spatial and temporal channel characteristics, resulting in a high key disagreement rate (KDR) and low key generation rate (KGR). To address these issues, we propose a novel reconfigurable intelligent surface (RIS)-aided secret key generation approach using an autoencoder and K -means quantization algorithm. The proposed method uses channel state information (CSI) for channel estimation and dynamically adjusts the reflection coefficients of the RIS to create a rapidly fluctuating channel. This strategy enables the extraction of dynamic channel parameters, thereby enhancing channel randomness. Additionally, by integrating the autoencoder with the K -means clustering quantization algorithm, the method efficiently extracts random bits from complex, ambiguous, and high-dimensional channel parameters, significantly reducing KDR. Simulations demonstrate that, under various signal-to-noise ratios (SNRs), the proposed method performs excellently in terms of KGR and KDR. Furthermore, the randomness of the generated keys is validated through the National Institute of Standards and Technology (NIST) test suite.