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IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

RIS-Assisted Simultaneous Transmission and Secret Key Generation: An ICAS Paradigm

Ning Gao, Yuze Yao, Shi Jin, Cen Li, Michail Matthaiou

Published May 20, 2024• 3 citations
Publisher

Abstract

We propose an integrated communications and security (ICAS) design paradigm that is RIS-assisted simultaneous transmission and secret key generation by sharing RIS for these two tasks. Specifically, the legitimate transceivers intend to jointly optimize the data transmission rate and the key generation rate by configuring the phase-shift of RIS in the presence of a smart attacker. We first derive the key generation rate of the RIS-assisted physical layer key generation (PLKG). Then, we model the problem as a finite Markov decision process and propose a model-free reinforcement learning approach to optimize the phase-shift of RIS. Particularly, due to the channel state information (CSI) of the attacker cannot obtain in real-world conditions, we develop a deep recurrent Q-network (DRQN) based dynamic ST strategy. Simulation results show that the proposed DRQN based dynamic ST strategy has a better performance than the benchmarks even with a partial observation information, and achieves “one time pad” communication by allocating a suitable weight factor for data transmission and PLKG.

DOI
10.1109/infocomwkshps61880.2024.10620776

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We propose an integrated communications and security (ICAS) design paradigm that is RIS-assisted simultaneous transmission and secret key generation by sharing RIS for these two tasks. Specifically, the legitimate transceivers intend to jointly optimize the data transmission rate and the key generation rate by configuring the phase-shift of RIS in the presence of a smart attacker. We first derive the key generation rate of the RIS-assisted physical layer key generation (PLKG). Then, we model the problem as a finite Markov decision process and propose a model-free reinforcement learning approach to optimize the phase-shift of RIS. Particularly, due to the channel state information (CSI) of the attacker cannot obtain in real-world conditions, we develop a deep recurrent Q-network (DRQN) based dynamic ST strategy. Simulation results show that the proposed DRQN based dynamic ST strategy has a better performance than the benchmarks even with a partial observation information, and achieves “one time pad” communication by allocating a suitable weight factor for data transmission and PLKG.
We develop a new integrated communications and security (ICAS) design paradigm by leveraging the concept of reconfigurable intelligent surfaces (RISs). In particular, we propose RIS-assisted simultaneous transmission and secret key generation by sharing the RIS for these two tasks. Specifically, the legitimate transceivers intend to jointly optimize the data transmission rate and the key generation rate by configuring the phase-shift of the RIS in the presence of a smart attacker. We first derive the key generation rate of the RIS-assisted physical layer key generation (PLKG). Then, to obtain the optimal RIS configuration, we formulate the problem as a secure transmission (ST) game and prove the existence of the Nash equilibrium (NE), and then derive the NE point of the static game. For the dynamic ST game, we model the problem as a finite Markov decision process and propose a model-free reinforcement learning approach to obtain the NE point. Particularly, considering that the legitimate transceivers cannot obtain the channel state information (CSI) of the attacker in real-world conditions, we develop a deep recurrent Q-network (DRQN) based dynamic ST strategy to learn the optimal RIS configuration. The details of the algorithm are provided, and then, the system complexity is analyzed. Our simulation results show that the proposed DRQN based dynamic ST strategy has a better performance than the benchmarks even with a partial observation information, and achieves “one time pad” communication by allocating a suitable weight factor for data transmission and PLKG.
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.
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.
While reconfigurable intelligent surface (RIS) technology shows great promise for wireless communication, an adversary using such technology can threaten wireless performance. This paper explores an RIS-based attack on time-division duplex (TDD) based wireless systems that use channel reciprocity for physical layer key generation (PLKG). We demonstrate that deploying a non-reciprocal RIS with a non-symmetric "beyond diagonal" (BD) phase shift matrix can compromise channel reciprocity and thus break key consistency. The attack can be achieved without transmission of signal energy, channel state information (CSI), and synchronization with the legitimate system, and thus it is difficult to detect and counteract. We propose a physically consistent BD-RIS model and verify the impact of its attack on the secret key rate (SKR) of the legitimate system via simulations. Moreover, we provide a heuristic approach for optimizing the BD-RIS configuration to realize a more severe attack in cases where some partial knowledge of the channel state information is available. Our results demonstrate that such channel reciprocity attacks can significantly decrease the SKR of the legitimate system.
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.
To establish a secure integrated communications and sensing (ISAC) system, this paper proposes a physical layer key generation (PLKG) scheme coupled to the ISAC characteristics. Firstly, we propose a novel PLKG protocol and formulate a sum secret key rate maximization problem under the sensing accuracy by jointly optimizing the communication and sensing beamforming. To address the complex optimization problems, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm is proposed. Simulation results demonstrate the effectiveness and superiority of the proposed PPO-based RL algorithm compared to the benchmark schemes.