Compressive sensing based parameter estimation for free-space continuous-variable quantum key distribution

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.