A compressive sensing based parameter estimation for free space continuous variable quantum key distribution

TL;DR

This paper introduces a compressive sensing approach to efficiently estimate atmospheric channel parameters in satellite-based free-space continuous-variable quantum key distribution, reducing data and computational needs.

Contribution

The paper applies compressive sensing theory to free-space CV-QKD for low-complexity, data-efficient channel parameter estimation, a novel adaptation for quantum communication.

Findings

Effective channel parameter estimation with limited data

Reduced computational complexity in parameter estimation

Simulation results confirm method effectiveness

Abstract

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.