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Lecture notes in electrical engineering

Compressed Sensing Method for Secret Key Generation Based on MIMO Channel Estimation

Yuqi Li, Ting Jiang, Jingjing Huang

Published Jan 1, 2015• 1 citation
Publisher
DOI
10.1007/978-3-319-08991-1_44

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In satellite-based free-space continuous-variable QKD (CV-QKD), the parameter estimation for the atmospheric channel fluctuations due to the turbulence effects and attenuation is crucial for analyzing and improving the protocol performance. However, the partial key data usually need to be sacrificed for the parameter estimation leading to the secret key reduction and the possible information leakage, especially when the channel is varying. In this paper, compressive sensing (CS) theory is applied to free-space CV-QKD to achieve the channel parameter estimation with small amount of key data sacrifice and low computational complexity. According to CS theory, the possibility of the sparse representation for free-space channel is analyzed and the two types of sparse reconstruction models for the channel parameters are constructed combining with the stability of the sub-channels. The most part of key data for parameter estimation is saved by using the model constructed by the variables in the quantum signals, while all the key data can be saved and be used to generate the secret key by using the model constructed by the second-order statistics of the variables. Thus, the methods can generate more secret key, improve the secret key rate, and be well adapted for the cases with the limited communication time since fewer or no key data (variables) is sacrificed for parameter estimation. Finally, simulation results are given to verify the effectiveness of the proposed methods.
This paper investigates a physical layer key generation (PLKG) scheme for reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple-input multipleoutput (MIMO) systems. Unlike traditional PLKG which relies on channel state information (CSI), we exploit the virtual angles of departure (AoDs). To accurately estimate these angles, we propose a redundant transforming matrix-based compressive sampling matching pursuit (RTMCoSa) method. We then derive the secret key rate (SKR) of the RIS-aided mmWave system. Simulation results demonstrate that the RTMCoSa method outperforms existing orthogonal matching pursuit (OMP) methods in channel probing for angle information. What is more, the proposed key generation scheme surpasses traditional CSI-based methods in SKR when the SNR is low. And the SKR of our method maintains robust when the SNR decreases.
In satellite-based free-space continuous-variable QKD (CV-QKD), the parameter estimation for the atmospheric channel fluctuations due to the turbulence effects and attenuation is crucial for analyzing and improving the protocol performance. In this paper, compressive sensing (CS) theory is applied to free-space CV-QKD to achieve the channel parameter estimation with low computational complexity and small amount of data. According to CS theory, the possibility of the sparse representation for free-space channel is analyzed and the two types of sparse reconstruction models for the channel parameters are constructed combining with the stability of the sub-channels. The most part of variable for parameter estimation is saved by using the model relying on the variables in the quantum signals, while all the variables can be used to generate the secret key by using the model relying on the second-order statistics of the variables. The methods are well adapted for the cases with the limited communication time since a little or no variable is sacrificed for parameter estimation. Finally, simulation results are given to verify the effectiveness of the proposed methods.