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Reconfigurable intelligent surface-aided secret key generation using autoencoder and K-means quantization
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Zhenling LI†1, Panpan XU1, Qiangqiang GAO2,3, Chunguo LI4, Weijie TAN‡2,3. Reconfigurable intelligent surface-aided secret key generation using autoencoder and K-means quantization[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Reconfigurable intelligent surface-aided secret key generation using autoencoder and K-means quantization",
author="Zhenling LI†1, Panpan XU1, Qiangqiang GAO2,3, Chunguo LI4, Weijie TAN‡2,3",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400799"
}
%0 Journal Article
%T Reconfigurable intelligent surface-aided secret key generation using autoencoder and K-means quantization
%A Zhenling LI†1
%A Panpan XU1
%A Qiangqiang GAO2
%A 3
%A Chunguo LI4
%A Weijie TAN‡2
%A 3
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400799
TY - JOUR
T1 - Reconfigurable intelligent surface-aided secret key generation using autoencoder and K-means quantization
A1 - Zhenling LI†1
A1 - Panpan XU1
A1 - Qiangqiang GAO2
A1 - 3
A1 - Chunguo LI4
A1 - Weijie TAN‡2
A1 - 3
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2400799
Abstract: 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, this paper proposes a novel reconfigurable intelligent surface (RIS)-aided secret key generation approach using an autoencoder (AE) and K-means quantization algorithm. The proposed method utilizes 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, 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. Simulation experiments demonstrate that, under various signal-to-noise ratios (SNRs), the proposed method performs excellently in terms of KGR and KDR. Additionally, the randomness of the generated keys is validated through the National Institute of Standards and Technology (NIST) test suite.
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