Assessment of adaptive optics‐corrected optical links statistics from integrated turbulence parameters through a Gaussian process metamodel

Abstract

SummaryWith the development of free space optical links (FSO) for space‐ground communications comes the need to mitigate the effects of the atmospheric turbulence to guarantee a lossless connection. By having a network of addressable ground stations, we want to guarantee to always target a point where the link is available. Assuming atmospheric transmission is managed thanks to site diversity, we focus only on the influence of atmospheric turbulence on the signal injected into a single mode fiber on the downlink. The use of adaptive optics (AO) is assumed to avoid turbulence‐induced signal disruptions and enable a sufficiently high level of received signal for data transmission. Up to now, AO performance adequate assessment required the knowledge of high‐resolution and wind profiles. With the advent of integrated atmospheric parameters measurement instruments, we investigate here the possibility to estimate AO‐corrected performance from a limited number of integrated parameters. In this paper, we propose to use a Gaussian process metamodel to assess the statistics of the received optical power after an AO correction. Taking as input only four integrated parameters of the turbulence profile and associated wind profile, which can be measured with simple instruments, the estimation error on the value of the 1% quantile of the received optical power is inferior to . We also demonstrate the possibility to estimate the half correlation time of the received optical power using the same integrated parameters.

Forecasting of turbulence impact on optical link from geostationary satellite

“…As inputs of our model we added 5 moments of the PSD of scintillation measured by a 5 cm pupil instrument, these results were simulated using the formulas of Shen 26 . As seen on figure 12, adding information on the PSD to our model enabled much better results with coefficient of determination of 0.99 for the phase and 0.98 for the scintillation 25 .…”

“…Once again, best results were obtained with the use of a Gaussian process metamodel. On figure 11 where the estimated value is compared to the expected one, we can see that the coefficient of determination is 0.88 for the phase demi-correlation and 0.84 for the scintillation 25 . This low score is due to the lack of temporal information found in r0, ℎand 6̅ .…”

“…Different machine learning metamodels have been tested of which we can cite gradient boosting, multilayer perceptron, support vector machine regression and Gaussian process 24 . Best results were obtained for Gaussian process 25 . 15% of our database was used for a training set and the 85% remaining for a test set.…”