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
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.
@article{title="Reconfigurable intelligent surface-aided secret key generation using an autoencoder and K-means quantization",
author="Zhenling LI, Panpan XU, Qiangqiang GAO, Chunguo LI, Weijie TAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="26",
number="8",
pages="1486-1500",
year="2025",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400799"
}
%0 Journal Article
%T Reconfigurable intelligent surface-aided secret key generation using an autoencoder and K-means quantization
%A Zhenling LI
%A Panpan XU
%A Qiangqiang GAO
%A Chunguo LI
%A Weijie TAN
%J Frontiers of Information Technology & Electronic Engineering
%V 26
%N 8
%P 1486-1500
%@ 2095-9184
%D 2025
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400799
TY - JOUR
T1 - Reconfigurable intelligent surface-aided secret key generation using an autoencoder and K-means quantization
A1 - Zhenling LI
A1 - Panpan XU
A1 - Qiangqiang GAO
A1 - Chunguo LI
A1 - Weijie TAN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 26
IS - 8
SP - 1486
EP - 1500
%@ 2095-9184
Y1 - 2025
PB - Zhejiang University Press & Springer
ER -
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, 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.
[1]Aldaghri N, Mahdavifar H, 2020. Physical layer secret key generation in static environments. IEEE Trans Inform Forens Sec, 15:2692-2705.
[2]Csiszar I, Narayan P, 2000. Common randomness and secret key generation with a helper. IEEE Trans Inform Theory, 46(2):344-366.
[3]Cui ZL, Liu J, Yang G, 2024. XL-RIS empowered near-field physical layer security against jamming and eavesdropping attacks. Front Inform Technol Electron Eng, 25(12):1750-1758.
[4]Han QQ, Liu JM, Shen ZW, et al., 2020. Vector partitioning quantization utilizing K-means clustering for physical layer secret key generation. Inform Sci, 512:137-160.
[5]Hershey JE, Hassan AA, Yarlagadda R, 1995. Unconventional cryptographic keying variable management. IEEE Trans Commun, 43(1):3-6.
[6]Ji ZJ, Yeoh PL, Zhang DY, et al., 2021. Secret key generation for intelligent reflection surface assisted wireless communication networks. IEEE Trans Veh Technol, 70(1):1030-1034.
[7]Juels A, Wattenberg M, 1999. A fuzzy commitment scheme. Proc 6th ACM Conf on Computer and Communications Security, p.28-36.
[8]Li GY, Hu AQ, Zhang JQ, et al., 2017. Security analysis of a novel artificial randomness approach for fast key generation. Proc IEEE Conf on Global Communications, p.1-6.
[9]Liu YP, Draper SC, Sayeed AM, 2012. Exploiting channel diversity in secret key generation from multipath fading randomness. IEEE Trans Inform Forens Sec, 7(5):1484-1497.
[10]Lou YM, Jin L, Zhong Z, et al., 2017. Secret key generation scheme based on MIMO received signal spaces. Sci Sin Inform, 47(3):362-373.
[11]Lu XJ, Lei J, Shi YX, et al., 2021. Intelligent reflection surface assisted secret key generation. IEEE Signal Process Lett, 28:1036-1040.
[12]Luo HF, Garg N, Ratnarajah T, 2023. A channel frequency response-based secret key generation scheme in in-band full-duplex MIMO-OFDM systems. IEEE J Sel Areas Commun, 41(9):2951-2965.
[13]Mathur S, Trappe W, Mandayam N, et al., 2008. Radio-telepathy: extracting a secret key from an unauthenticated wireless channel. Proc 14th ACM Int Conf on Mobile Computing and Networking, p.128-139.
[14]Rukhin A, Soto J, Nechvatal J, et al., 2010. A statistical test suite for random and pseudorandom number generators for cryptographic applications (NIST SP 800-22 Rev. 1a). NIST, Gaithersburg, USA.
[15]Shannon CE, 1949. Communication theory of secrecy systems. Bell Syst Tech J, 28(4):656-715.
[16]Shimizu T, Iwai H, Sasaoka H, 2011. Physical-layer secret key agreement in two-way wireless relaying systems. IEEE Trans Inform Forens Sec, 6(3):650-660.
[17]Shlezinger N, Alexandropoulos GC, Imani MF, et al., 2021. Dynamic metasurface antennas for 6G extreme massive MIMO communications. IEEE Wirel Commun, 28(2):106-113.
[18]Wan Z, Yan MY, Huang KZ, et al., 2023. Pattern-reconfigurable antenna-assisted secret key generation from multipath fading channels. Front Inform Technol Electron Eng, 24(12):1803-1814.
[19]Wang XQ, Zhu FH, Zhou QY, et al., 2024a. Energy-efficient beamforming for RISs-aided communications: gradient based meta learning. Proc IEEE Int Conf on Communications, p.3464-3469.
[20]Wang XQ, Zhu FH, Huang CW, et al., 2024b. Robust beamforming with gradient-based liquid neural network. IEEE Wirel Commun Lett, 13(11):3020-3024.
[21]Wu QQ, Zhang SW, Zheng BX, et al., 2021. Intelligent reflection surface-aided wireless communications: a tutorial. IEEE Trans Commun, 69(5):3313-3351.
[22]Wu XH, Peng YX, Hu CJ, et al., 2013. A secret key generation method based on CSI in OFDM-FDD system. Proc IEEE Conf on Globecom Workshops, p.1297-1302.
[23]Wyner AD, 1975. The wire-tap channel. Bell Syst Tech J, 54(8):1355-1387.
[24]Ye HY, Gao FF, Qian J, et al., 2020. Deep learning-based denoise network for CSI feedback in FDD massive MIMO systems. IEEE Commun Lett, 24(8):1742-1746.
[25]Yu P, Zhou FQ, Zhang X, et al., 2020. Deep learning-based resource allocation for 5G broadband TV service. IEEE Trans Broadcast, 66(4):800-813.
[26]Zeng K, 2015. Physical layer key generation in wireless networks: challenges and opportunities. IEEE Commun Mag, 53(6):33-39.
[27]Zhou G, Pan CH, Ren H, et al., 2020. Robust beamforming design for intelligent reflection surface aided MISO communication systems. IEEE Wirel Commun Lett, 9(10):1658-1662.
[28]Zhu FH, Wang XQ, Huang CW, et al., 2024. Robust beamforming for RIS-aided communications: gradient-based manifold meta learning. IEEE Trans Wirel Commun, 23(11):15945-15956.
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