基于压缩感知的自由空间连续变量量子密钥分发参数估计
A compressive sensing based parameter estimation for free space continuous variable quantum key distribution
摘要：
在星载自由空间连续变量量子密钥分配（CV-QKD）中，由于大气湍流效应和衰减引起的大气信道波动的参数估计对于分析和改善协议性能至关重要。本文将压缩感知（CS）理论应用于自由空间CV-QKD中，以实现低计算复杂度和小数据量的信道参数估计。根据压缩感知理论，分析了自由空间信道稀疏表示的可能性，并结合子信道的稳定性，构造了两类信道参数稀疏重构模型。基于量子信号中变量的模型节省了大部分用于参数估计的变量，而基于变量的二阶统计量的模型可以利用所有变量来生成密钥。该方法很好地适用于通信时间有限的情况下，因为很少或没有变量被牺牲的参数估计。最后通过仿真验证了所提方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
