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arXiv:2010.06613 · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

Intelligent Reflecting Surface-Assisted Wireless Key Generation for Low-Entropy Environments

Paul Staat, Harald Elders-Boll, Markus Heinrichs, Rainer Kronberger, Christian Zenger, Christof Paar

Published Sep 13, 2021• 65 citations
arXivHTMLPDF

Abstract

Physical layer key generation is a promising candidate for cryptographic key establishment between two wireless communication parties. It offers information-theoretic security and is an attractive alternative to public-key techniques. Here, the inherent randomness of wireless radio channels is used as a shared entropy source to generate cryptographic key material. However, practical implementations often suffer from static channel conditions which exhibit a limited amount of randomness. In the past, considerable research efforts have been made to address this fundamental limitation. However, current solutions are not generic or require dedicated hardware extensions such as reconfigurable antennas. In this paper, we propose a novel wireless key generation architecture based on randomized channel responses from an intelligent reflecting surface (IRS). Due to its passive nature, a cooperative IRS is well-suited to provide randomness for conventional resource-constrained radios. We conduct the first practical studies to successfully demonstrate IRS-based physical-layer key generation with an OFDM system. In a static environment, using a single subcarrier only, our IRS-assisted prototype system achieves a key generation rate (KGR) of 97.39 bps with 6.5% key disagreement rate (KDR) after quantization, while passing standard randomness tests.

DOI
10.1109/pimrc50174.2021.9569556

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Physical layer key generation is a promising candidate for cryptographic key establishment between two wireless communication parties. It offers information-theoretic security and is an attractive alternative to public-key techniques. Here, the inherent randomness of wireless radio channels is used as a shared entropy source to generate cryptographic key material. However, practical implementations often suffer from static channel conditions which exhibit a limited amount of randomness. In the past, considerable research efforts have been made to address this fundamental limitation. However, current solutions are not generic or require dedicated hardware extensions such as reconfigurable antennas. In this paper, we propose a novel wireless key generation architecture based on randomized channel responses from an intelligent reflecting surface (IRS). Due to its passive nature, a cooperative IRS is well-suited to provide randomness for conventional resource-constrained radios. We conduct the first practical studies to successfully demonstrate IRS-based physical-layer key generation with an OFDM system. In a static environment, using a single subcarrier only, our IRS-assisted prototype system achieves a key generation rate (KGR) of 97.39 bps with 6.5% key disagreement rate (KDR) after quantization, while passing standard randomness tests.
Physical layer secret key generation (SKG) is a promising supplement for high-level encryption, which leverages the channel reciprocity and the natural randomness of wireless channel. However, practical implementations often exhibit static environment. To solve this problem, this paper proposes a novel secret key generation scheme assisted by an intelligent reflecting surface (IRS) which can smartly reconfigure the radio signal propagation to improve the secret key rate of SKG. Specially, we formulate the minimum achievable secret key capacity in the IRS-assisted multiple-input single-output (MISO) system with multiple eavesdroppers. Then, an optimization framework for designing IRS reflecting coefficients based on the secret key capacity lower bound is proposed. Numerical results show that our optimal IRS-assisted SKG scheme can achieve much higher achievable secret key capacity when compared with two benchmark schemes.
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 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.
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.
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.