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

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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. 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. 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. 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

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

Abstract

Abstract

Objective Physical Layer Key Generation (PLKG) is an emerging key generation technique that exploits the reciprocity, time variability, and spatial decorrelation properties of wireless channels to enable real-time key generation. This technique has the potential to achieve one-time-pad encryption and demonstrates resilience against quantum attacks. PLKG typically consists of four key steps: channel probing, preprocessing and quantization, information reconciliation, and privacy amplification. Proper preprocessing can enhance channel reciprocity, remove redundancy, improve the Key Generation Rate (KGR), and reduce the Key Disagreement Rate (KDR). Reconfigurable Intelligent Surfaces (RIS) offer advantages such as low cost, low power consumption, and ease of deployment. They enable the manipulation of incident signals in terms of amplitude, phase, and polarization, thus constructing an intelligent communication environment. This provides a novel approach to mitigating the limitations of key generation imposed by the channel environment. However, existing preprocessing methods, such as Principal Component Analysis (PCA), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), and nonlinear processing, treat channel data as a whole for noise reduction and redundancy elimination. These approaches do not account for the key capacity loss caused by channel cascading in RIS-assisted communication systems, thereby limiting KGR. To address this issue, this paper proposes a novel PLKG protocol based on unfolded channels, aiming to mitigate the key capacity loss induced by channel cascading and thereby enhance KGR. Methods This paper first derives the degradation effect of channel cascading on the key generation rate through entropy theory and validates it via theoretical simulations. Next, a PLKG scheme designed for RIS-assisted communication scenarios is proposed, with improvements in both channel probing and preprocessing. In the channel probing phase, a two-stage channel estimation approach is designed. In the first stage, the PARAllel FACtor (PARAFAC) channel estimation method is employed, utilizing the inherent multidimensional information structure in Multiple Input Multiple Output (MIMO) communication systems to construct a tensor, which is then 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 used to estimate the cascaded channel, thereby introducing an additional source of randomness for key generation. In the channel preprocessing phase, the baseline unfolded channel obtained from the two-stage channel 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 then 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, thereby improving KGR while ensuring 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. Subsequently, three preprocessing methods—PCA, DCT, and WT—are applied to the cascaded channel, unfolded channel, and RIS phase shift matrix for noise reduction and decorrelation. The extracted channel features are then 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 shows that the proposed scheme achieved a 72% KGR improvement at 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). Additionally, experiments on the number of RIS configuration matrices indicates that increasing the number beyond eight yielded diminishing returns in KGR improvement. Therefore, an optimal range of 8–10 configuration matrices is recommended. Furthermore, the computational complexity of the PARAFAC channel estimation method is analyzed. 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 employs the PARAFAC channel estimation method to estimate the unfolded channel and the LS method to estimate the cascaded channel. Based on these estimations, the cascaded channel is decomposed into the unfolded channel and the RIS phase shift matrix during preprocessing. By using 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% KGR improvement at a 2dB SNR while maintaining a low KDR. However, despite its ability to enhance KGR, the proposed scheme still faces challenges such as excessive pilot overhead and computational limitations. Future work should focus on optimizing overhead reduction to further enhance its practicality.
- Physical Layer Key Generation (PLKG),
- Reconfigurable Intellectual Surface (RIS),
- Unfolded channel

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References(16)

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