Full Text:   <941>

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CLC number: TN918.4

On-line Access: 2025-06-04

Received: 2024-09-13

Revision Accepted: 2024-12-17

Crosschecked: 2025-09-04

Cited: 0

Clicked: 810

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Weijie TAN

https://orcid.org/0000-0001-6590-5757

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Frontiers of Information Technology & Electronic Engineering  2025 Vol.26 No.8 P.1486-1500

http://doi.org/10.1631/FITEE.2400799


Reconfigurable intelligent surface-aided secret key generation using an autoencoder and K-means quantization


Author(s):  Zhenling LI, Panpan XU, Qiangqiang GAO, Chunguo LI, Weijie TAN

Affiliation(s):  School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China; more

Corresponding email(s):   wjtan@gzu.edu.cn

Key Words:  Reconfigurable intelligent surface (RIS), Physical layer key generation, Quantization, Autoencoder


Zhenling LI, Panpan XU, Qiangqiang GAO, Chunguo LI, Weijie TAN. Reconfigurable intelligent surface-aided secret key generation using an autoencoder and K-means quantization[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(8): 1486-1500.

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author="Zhenling LI, Panpan XU, Qiangqiang GAO, Chunguo LI, Weijie TAN",
journal="Frontiers of Information Technology & Electronic Engineering",
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doi="10.1631/FITEE.2400799"
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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, 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.

基于自编码器与K均值量化的可重构智能表面辅助密钥生成

李珍玲1,徐盼盼1,高强强2,3,李春国4,谭伟杰2,3
1贵州大学数学与统计学院,中国贵阳市,550025
2贵州大学公共大数据国家重点实验室,中国贵阳市,550025
3贵州大学计算机科学与技术学院,中国贵阳市,550025
4东南大学信息科学与工程学院,中国南京市,212013
摘要:在准静态无线信道场景下,物理层密钥的生成面临着信道特性不变带来的挑战,导致高密钥不一致率(KDR)和低密钥生成率(KGR)。为解决这些问题,提出一种新颖的可重构智能表面(RIS)辅助密钥生成方法,该方法结合了自编码器和K均值量化算法。本文提出的方法利用信道状态信息进行信道估计,动态调整RIS的反射系数,以创建快速波动的信道。该策略从动态信道参数中提取信息,从而增强信道随机性。通过集成自编码器与K均值聚类量化算法,该方法高效地从复杂、模糊且高维的信道参数中提取随机比特,显著降低了KDR。仿真实验表明,在不同信噪比条件下,该方法在KDR和KGR方面均表现出色。此外,用美国国家标准与技术研究院测试套件验证了该方法所生成密钥的随机性。

关键词:可重构智能表面;物理层密钥生成;量化;自编码器

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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