Single shot line-of-sight atmospheric turbulence profiling for laser satellite communications with STORM.

Laser satellite communication technologies are promising next-generation communications systems, offering higher data rates, more secure links, and cost-effective operations. One of the remaining challenges to tackle for ensuring sufficient link availability is atmospheric turbulence. While turbulence effects on laser links can be partly compensated for with corrective technologies and algorithms, these methods would benefit from better knowledge of turbulence profiles on the communications channel, both for system design or real-time assistance during operations. As state-of-the-art turbulence profilers are not designed to measure profiles from a laser beam on a moving satellite, this paper proposes a line-of-sight turbulence profiling tool tailored for laser satellite communications. Speckle-based turbulence observation and reconstruction via machine learning uses surrogate learning to build a model that can reconstruct optical turbulence profiles (Cn2(h)) from a single shot measurement of a speckle pattern. In this paper, the first modeling results of this flexible approach demonstrate that eight-layer turbulence profiles can be reconstructed from simulated single speckle images of a star with less than 10% error on the Fried parameter.