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IEEE Transactions on Consumer Electronics

Intelligent Reflective Surface-Aided Channel Phase Probing for Physical-Layer Key Generation in Transportation Systems

Xinjin Lu, Yuxin Shi, Zhi Lin, Bingyin Ren, Jinliang Li, Jing Lei

Published Jun 6, 2024• 4 citations
Publisher

Abstract

Focusing on the issue of secret key generation for transportation systems and electronic devices in possible less-dynamic channel conditions, this paper proposes an intelligent reflective surface aided channel phase probing scheme (IRS-CPP) for improving physical layer key generation. Specifically, we consider the model that both legitimate nodes first acquire the secret key from the channel phase response by employing channel probing. Then, the passive IRS are utilized to enhance the probing signal by using random phase shifts, which can effectively modify the signal phases. Furthermore, we provide the performance analysis of the proposed scheme in terms of the theoretical key capacity to estimate performance of the scheme. Finally, comprehensive results including the key capacity, key generation rate (KGR), key inconsistency rate (KIR) and key randomness are investigated. Simulation results demonstrate that the proposed scheme can effectively enhance the physical layer key generation performance in the static scenario compared with the benchmark key generation schemes.

DOI
10.1109/tce.2024.3410280

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Focusing on the issue of secret key generation for transportation systems and electronic devices in possible less-dynamic channel conditions, this paper proposes an intelligent reflective surface aided channel phase probing scheme (IRS-CPP) for improving physical layer key generation. Specifically, we consider the model that both legitimate nodes first acquire the secret key from the channel phase response by employing channel probing. Then, the passive IRS are utilized to enhance the probing signal by using random phase shifts, which can effectively modify the signal phases. Furthermore, we provide the performance analysis of the proposed scheme in terms of the theoretical key capacity to estimate performance of the scheme. Finally, comprehensive results including the key capacity, key generation rate (KGR), key inconsistency rate (KIR) and key randomness are investigated. Simulation results demonstrate that the proposed scheme can effectively enhance the physical layer key generation performance in the static scenario compared with the benchmark key generation schemes.
Abstract Aiming at the problem of secret key generation in the static wireless communication environment, this paper proposes a scheme applying intelligent reflective surface to channel phase probing (IRS-CPP) in wireless secret key generation. Firstly, both legitimate parties utilize OFDM signals for channel probing to obtain the corresponding channel phase response. Secondly, the passive elements in the IRS are used to reflect the probing signal, which can effectively change the signal phases. Finally, the secret key can be obtained after reconciliation and privacy amplification. This scheme makes full use of the reciprocity of the phase response in wireless channels, and applys the IRS elements to change the static environment indirectly. Compared with the existing key generation scheme, the proposed scheme effectively improves the performance of secret key generation in static environments.
Physical layer secret key generation is a promising candidate to achieve one-time-pad encryption approach for the wireless communication system. However, in a static environment, the secret key rate is low due to the lack of channel time-variation. To solve this problem, this letter proposes a novel secret key generation scheme assisted by an intelligent reflecting surface with discrete phase shifts. In the scheme, legitimate nodes construct the dynamic time-varying channel by rapidly and randomly switching the phase of IRS elements. Then, channel coefficients are used to generate the secret key. Based on the IRS channel model, we derive the probability distribution of the channel coefficient and the expressions of the secret key rate. Furthermore, we optimize the secret key rate by adjusting the switching time of the IRS phase. Monte Carlo simulation and numerical results show that our proposed scheme can update the secret key in a static environment.
Symbiotic radio (SR) has recently emerged as a promising technology to boost spectrum efficiency of wireless communications by allowing reflective communications underlying the active RF communications. In this paper, we leverage SR to boost physical layer security by using an array of passive reflecting elements constituting the intelligent reflecting surface (IRS), which is reconfigurable to induce diverse RF radiation patterns. In particular, by switching the IRS’s phase shifting matrices, we can proactively create dynamic channel conditions, which can be exploited by the transceivers to extract common channel features and thus used to generate secret keys for encrypted data transmissions. As such, we firstly present the design principles for IRS-assisted key generation and verify a performance improvement in terms of the secret key generation rate (KGR). Our analysis reveals that the IRS’s random phase shifting may result in a non-uniform channel distribution that limits the KGR. Therefore, to maximize the KGR, we propose both a heuristic scheme and deep reinforcement learning (DRL) to control the switching of the IRS’s phase shifting matrices. Simulation results show that the DRL approach for IRS-assisted key generation can significantly improve the KGR.
Physical layer secret key (SK) generation is known to be an efficient means to achieve high secrecy rate, on the condition that dynamic channel state information (CSI) is provided. For this reason, the secrecy performance is highly degraded in a static environment. The intelligent reflecting surface (IRS) is a promising solution to create dynamic randomness, and thus lead to enhanced secrecy performance regardless of the user environments. This paper proposes an IRS-assisted physical layer SK generation scheme, by efficiently combing phase information of the direct and reflected channel information in a hybrid way. In particular, the initial SKs are obtained by adopting efficient phase quantization method with symmetric bit allocation to complex numbered channel estimates. Simulation results show that the proposed hybrid phase quantization (PQ) can improve the SK generation rate and the key disagreement probability in a static environment.
In secret key generation of physical layer security technology, it is challenging to achieve high key capacity and low bit inconsistency rate. This paper investigates intelligent reflecting surface (IRS)-assisted secret key generation, which aims to maximize the secret key capacity by adjusting the placement of the IRS units. Specifically, we first analyze and deduce the key capacity expression of the IRS-assisted system from the perspective of information theory. Then we investigate how to use the channel state information (CSI) to place the IRS units effectively so as to maximize the secret key capacity. Simulation results show that our scheme could improve the quality of secret key generation significantly.
Physical layer secret key generation (PLKG) is a promising technology for establishing effective secret keys. Current works for PLKG mostly study key generation schemes in ideal communication environments with little or even no signal interference. In terms of this issue, exploiting the reconfigurable intelligent reflecting surface (IRS) to assist PLKG has caused an increasing interest. Most IRS-assisted PLKG schemes focus on the single-input-single-output (SISO), which is limited in future communications with multi-input-multi-output (MIMO). However, MIMO could bring a serious overhead of channel reciprocity extraction. To fill the gap, this paper proposes a novel low-overhead IRS-assisted PLKG scheme with deep learning in the MIMO communications environments. We first combine the direct channel and the reflecting channel established by the IRS to construct the channel response function, and we propose a theoretically optimal interaction matrix to approach the optimal achievable rate. Then we design a channel reciprocity-learning neural network with an IRS introduced (IRS-CRNet), which is exploited to extract the channel reciprocity in time division duplexing (TDD) systems. Moreover, a PLKG scheme based on the IRS-CRNet is proposed. Final simulation results verify the performance of the PLKG scheme based on the IRS-CRNet in terms of key generation rate, key error rate and randomness.