An Unfolded Channel-based Physical Layer Key Generation Method For Reconfigurable Intelligent Surface-Assisted Communication Systems

YANG Lijun; CHEN Zishuo; LU Haitao; GUO Lin

doi:10.11999/JEIT240988

Volume 47 Issue 2

Feb. 2025

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YANG Lijun, CHEN Zishuo, LU Haitao, GUO Lin. An Unfolded Channel-based Physical Layer Key Generation Method For Reconfigurable Intelligent Surface-Assisted Communication Systems[J]. Journal of Electronics & Information Technology, 2025, 47(2): 449-457. doi: 10.11999/JEIT240988

Citation:

YANG Lijun, CHEN Zishuo, LU Haitao, GUO Lin. An Unfolded Channel-based Physical Layer Key Generation Method For Reconfigurable Intelligent Surface-Assisted Communication Systems[J]. Journal of Electronics & Information Technology, 2025, 47(2): 449-457. doi: 10.11999/JEIT240988

Citation:

YANG Lijun, CHEN Zishuo, LU Haitao, GUO Lin. An Unfolded Channel-based Physical Layer Key Generation Method For Reconfigurable Intelligent Surface-Assisted Communication Systems[J]. Journal of Electronics & Information Technology, 2025, 47(2): 449-457. doi: 10.11999/JEIT240988

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An Unfolded Channel-based Physical Layer Key Generation Method For Reconfigurable Intelligent Surface-Assisted Communication Systems

doi: 10.11999/JEIT240988 cstr: 32379.14.JEIT240988

YANG Lijun¹,
CHEN Zishuo¹,
LU Haitao^{2, 3},
GUO Lin^{4
,
,}

1.
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
ZTE Corporation, Shenzhen 518057, China
3.
Shenzhen Key Laboratory of 5G RAN Security Technology Research and Application, Shenzhen 518055, China
4.
School of Modern Post, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Funds: The National Natural Science Foundation of China(62372244, 62172235), The ZTE Industry-university-Research Fund (2023ZTE08-02), The National Key Research and Development Program of China(2021YFB3101100), The Primary Research & Development Plan of Jiangsu Province (BE2023025), The Natural Science Foundation of Nanjing University of Posts and Telecommunications (NY222132), The Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX23_1057)

Received Date: 2024-11-05
Rev Recd Date: 2025-02-20

Available Online: 2025-02-24

Publish Date: 2025-02-28

Abstract

Abstract

Objective Physical Layer Key Generation (PLKG) is an emerging technique that leverages the reciprocity, time variability, and spatial decorrelation properties of wireless channels to enable real-time key generation. This method offers potential for one-time-pad encryption and resilience against quantum attacks. PLKG typically includes four key steps: channel probing, preprocessing and quantization, information reconciliation, and privacy amplification. Proper preprocessing can improve channel reciprocity, eliminate redundancy, increase the Key Generation Rate (KGR), and reduce the Key Disagreement Rate (KDR). Reconfigurable Intelligent Surfaces (RIS) present advantages such as low cost, low power consumption, and ease of deployment. By manipulating incident signals in terms of amplitude, phase, and polarization, RIS enables the creation of intelligent communication environments, offering a novel approach to mitigating channel limitations in key generation. However, current preprocessing methods like Principal Component Analysis (PCA), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), and nonlinear processing typically treat channel data as a whole for noise reduction and redundancy removal. These methods overlook the key capacity loss induced by channel cascading in RIS-assisted systems, limiting KGR. To address this challenge, this paper proposes a novel PLKG protocol based on unfolded channels, aimed at mitigating key capacity loss due to channel cascading, thereby enhancing KGR. Methods This paper first derives the degradation effect of channel cascading on the KGR using entropy theory and validates it through theoretical simulations. A PLKG scheme tailored for RIS-assisted communication scenarios is then proposed, with enhancements in both channel probing and preprocessing. In the channel probing phase, a two-stage channel estimation approach is introduced. The first stage employs the PARAllel FACtor (PARAFAC) method for channel estimation, utilizing the multidimensional information structure inherent in Multiple Input Multiple Output (MIMO) communication systems to construct a tensor. This tensor is used to estimate the baseline unfolded channel via the Alternating Least Squares (ALS) algorithm. In the second stage, the RIS phase shift matrix is randomized, and the Least Squares (LS) method is applied to estimate the cascaded channel, introducing an additional source of randomness for key generation. In the channel preprocessing phase, the baseline unfolded channel derived from the two-stage estimation is used to separate the cascaded channel into the unfolded channel and the RIS phase shift matrix. Conventional methods such as PCA, DCT, and Wavelet Transform (WT) are applied to remove noise and redundancy from the obtained data. By utilizing both the unfolded channel and the RIS phase shift matrix as joint key sources, the proposed scheme mitigates the KGR degradation caused by channel cascading, enhancing KGR while maintaining a low KDR. Results and Discussions A Rayleigh channel MIMO communication system model is established for experimentation. The proposed two-stage channel estimation method is used to separate the cascaded channel into the unfolded channel and the RIS phase shift matrix. Three preprocessing methods—PCA, DCT, and WT—are then applied to the cascaded channel, unfolded channel, and RIS phase shift matrix for noise reduction and decorrelation. The extracted channel features are quantized, followed by information reconciliation and privacy amplification. The experiment compares two key generation approaches: one using the cascaded channel as the key source and the other using the unfolded channel and RIS phase shift matrix as joint key sources. Simulation results show that the proposed scheme achieves a 72% improvement in KGR at a 2 dB Signal-to-Noise Ratio (SNR) (Fig. 8). Among the preprocessing methods, DCT demonstrates the highest KGR and the lowest KDR (Fig. 9, Fig. 10, Fig. 11, Fig. 11). Additionally, experiments on the number of RIS configuration matrices indicate that increasing the number beyond eight yields diminishing returns in KGR improvement. Thus, an optimal range of 8–10 configuration matrices is recommended. Furthermore, the computational complexity of the PARAFAC channel estimation method is analyzed, and the feasibility of real-time key generation is validated by considering channel coherence time, algorithm complexity, and communication protocol frame intervals. Conclusions This paper proposes a PLKG scheme that utilizes the PARAFAC channel estimation method to estimate the unfolded channel and the LS method to estimate the cascaded channel. During preprocessing, the cascaded channel is decomposed into the unfolded channel and the RIS phase shift matrix. By using both the unfolded channel and the RIS phase shift matrix as joint key sources, the proposed method mitigates the degradation of KGR caused by channel cascading. Compared with conventional PLKG schemes that use the cascaded channel as the key source, the proposed method achieves a 72% improvement in KGR at a 2 dB SNR, while maintaining a low KDR. However, despite enhancing KGR, the proposed scheme still faces challenges such as excessive pilot overhead and computational limitations. Future work should focus on optimizing overhead reduction to improve its practicality.
- Physical Layer Key Generation (PLKG),
- Reconfigurable Intellectual Surface (RIS),
- Unfolded channel

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References(16)

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