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International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)

Channel Key Extraction Scheme Using Asymmetric Intelligent Reflecting Surface Transformation

Yuwei Gao, Dengke Guo, Dongtang Ma, Jun Xiong

Published Oct 1, 2021• 0 citations
Publisher

Abstract

In physical layer secret key generation (PLSKG), the threshold-based quantization scheme is usually used to convert the channel detection value into the initial key bits. However, the data near the threshold is easily disturbed by noise fluctuation, which will lead to an increase in the key disagreement rate(KDR). We proposed an IRS-assisted key generation scheme, which uses the asymmetric change of IRS to ameliorate the downlink channel environment, to reduce the key inconsistency after quantization. Monte Carlo simulation and numerical results showed that our proposed scheme is feasible.

DOI
10.1109/ainit54228.2021.00014

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In physical layer secret key generation (PLSKG), the threshold-based quantization scheme is usually used to convert the channel detection value into the initial key bits. However, the data near the threshold is easily disturbed by noise fluctuation, which will lead to an increase in the key disagreement rate(KDR). We proposed an IRS-assisted key generation scheme, which uses the asymmetric change of IRS to ameliorate the downlink channel environment, to reduce the key inconsistency after quantization. Monte Carlo simulation and numerical results showed that our proposed scheme is feasible.
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.
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.
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.
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.
Channel secret key generation (CSKG), assisted by the new material intelligent reflecting surface (IRS), has become a new research hotspot recently. In this paper, the key extraction method in the IRS-aided low-entropy communication scenario with adjacent multi-users is investigated. Aiming at the problem of low key generation efficiency due to the high similarity of channels between users, we propose a joint user allocation and IRS reflection parameter adjustment scheme, while the reliability of information exchange during the key generation process is also considered. Specifically, the relevant key capability expressions of the IRS-aided communication system is analyzed. Then, we study how to adjust the IRS reflection matrix and allocate the corresponding users to minimize the similarity of different channels and ensure the robustness of key generation. The simulation results show that the proposed scheme can bring higher gains to the performance of key generation.