Reconfigurable intelligent surface (RIS) is a disruptive technology to enhance the performance of physical-layer key generation (PKG) thanks 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 base station (BS) and the RIS. However, the i.i.d. model is inaccurate for a typical RIS in an isotropic scattering environment and neglecting the existence of channel spatial correlation would possibly degrade the PKG performance. In this paper, we establish a general spatially correlated channel model and propose a new channel probing framework based on the transmit and the reflective beamforming. We derive a closed-form key generation rate (KGR) expression and formulate an optimization problem, which is solved by using the low-complexity Block Successive Upper-bound Minimization (BSUM) with Mirror-Prox method. Simulation results show that compared to the existing methods based on the i.i.d. fading model, our proposed method achieves about 5 dB transmit power gain when the spacing between two neighboring RIS elements is a quarter of the wavelength. Also, the KGR increases significantly with the number of RIS elements while that increases marginally with the number of BS and user antennas.

The artificial electromagnetic characteristics of reconfigurable intelligent surfaces (RIS) offers new opportunities to increase the secret key rate (SKR) in physical-layer key generation (PKG). However, the existing literature has primarily focused on continuous phase shift designs for RIS to enhance the SKR, while in practice the phase shifts are discrete due to hardware implementations. Hence, the extent to which practical RIS with discrete phase shifts can enhance SKR remains uncertain. Moreover, we have observed that existing optimization methods are not directly applicable to RIS with discrete phase shifts and relying solely on the approximation projection algorithm may lead to certain SKR performance degradation. To address these problems, this paper proposes a RIS-assisted PKG model considering the impact of discrete RIS phase shifts. To maximize the SKR by properly designing the RIS phase shifts, we propose an algorithm utilizing linear conic reformulation (LCR) with alternating difference-of-convex (DC) programming and successive convex approximation (SCA). Simulation results unveil that the proposed LCR-DC algorithm has the capability to achieve the SKR close to the optimal solutions. Furthermore, increasing the number of RIS elements or the number of quantization bits for RIS phase shifts enhances SKR but with diminishing returns. It is worth noting that a small number of discrete phase shifts, e.g., 2-3 quantization bits, is generally sufficient to achieve satisfactory SKR performance.

Physical-layer key generation (PKG) exploits the reciprocity and randomness of wireless channels to generate a symmetric key between two legitimate communication ends. However, in multi-cell systems, PKG suffers from severe pilot contamination due to the reuse of pilots in different cells. In this paper, we invoke multiple reconfigurable intelligent surfaces (RISs) for adaptively shaping the environment and enhancing the PKG performance. To this end, we formulate an optimization problem to maximize the weighted sum key rate (WSKR) by jointly optimizing the precoding matrices at the base stations (BSs) and the phase shifts at the RISs. To address the non-convexity of the problem, we adopt an alternating optimization (AO)-based algorithm that divides the joint optimization problem into two subproblems. For the subproblem of precoding matrices, we apply the Lagrangian dual approach based on the Karush-Kuhn-Tucker (KKT) conditions. As for the subproblem of phase shifts, we adopt a projected gradient ascent (PGA) algorithm. Simulation results validate the effectiveness of the proposed scheme, demonstrating significant gains in WSKR. Moreover, compared with a single-RIS case, deploying multiple RISs offer spatial diversity so as to improve the PKG performance of multicell systems.

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 correlation$\rho$is 0.3 and the RIS element spacing is half of the wavelength.

Physical layer encryption (PLE) has emerged as a promising technique to secure wireless communications. Different from conventional cryptography implemented at higher layers, PLE exploits the randomness of wireless channels to adjust symbol patterns at the physical layer, by which both data and modulation information can be protected. However, existing PLE schemes face challenges of security and robustness in practical usage. In a slowly varying environment, the constellation variation is negligible, which results in the vulnerability of PLE to the differential attack. Moreover, the decryption error rate of PLE is high when the channel reciprocity is not ideal. To tackle these problems, we exploit data randomness to enhance the dynamics of constellation variations between adjacent frames. Then we utilize analog-based encryption instead of digital-based encryption to dynamically rotate constellation, which reduces quantization loss and improves robustness to channel phase errors. Simulation results verify that the proposed scheme can effectively resist the differential attack and provide approximately a 4.5 dB gain when the bit error ratio (BER) is 0.001.

Reconfigurable Intelligent Surface (RIS) is a new paradigm that enables the reconfiguration of the wireless environment. Based on this feature, RIS can be employed to facilitate Physical-layer Key Generation (PKG). However, this technique could also be exploited by the attacker to destroy the key generation process via manipulating the channel features at the legitimate user side. Specifically, this paper proposes a new RIS-assisted Manipulating attack (RISM) that reduces the wireless channel reciprocity by rapidly changing the RIS reflection coefficient in the uplink and downlink channel probing step in orthogonal frequency division multiplexing (OFDM) systems. The vulnerability of traditional key generation technology based on channel frequency response (CFR) under this attack is analyzed. Then, we propose a slewing rate detection method based on path separation. The attacked path is removed from the time domain and a flexible quantization method is employed to maximize the Key Generation Rate (KGR). The simulation results show that under RISM attack, when the ratio of the attack path variance to the total path variance is 0.17, the Bit Disagreement Rate (BDR) of the CFR-based method is greater than 0.25, and the KGR is close to zero. In addition, the proposed detection method can successfully detect the attacked path for SNR above 0 dB in the case of 16 rounds of probing and the KGR is 35 bits/channel use at 23.04MHz bandwidth.

With the rapid development of wireless communication, the traditional communication encryption method is not adequate for our needs of high-speed and low latency. As a consequence, Physical Layer-Key Generation (PKG) and Reconfigurable Intelligent Surface (RIS) emerged. The existing researches on the combination of PKG and RIS mainly focus on the randomness increase of the communication environment and the key generation rate under a quasi-static environment. On the other hand, there is little research on the active attack of RIS by eavesdroppers. In this paper, the attack and defense method based on the combination of RIS and PKG is discussed. We propose a random phase attack method derived from the multiple-input single-output (MISO) model and analyze the effectiveness of the attack. To resist the random phase attack under a hypothetical scenario in which RIS is under the control of the attacker, we propose a path separation and path detection defense method according to the Multiple Signal Classification (MUSIC) algorithm. Simulation results show that the Bit Disagreement Rate (BDR) achieves a significant reduction, which proves the effectiveness of the proposed defense method based on path detection and path separation.