Feature engineering driven estimation of <i>C</i>_<i>n</i>² from deformed optical signals using neural networks

Atmospheric turbulence distorts the optical wavefront and deteriorates the bit rate in free-space optical communication or quantum key distribution. Adaptive optical methods are limited by the feedback timescales in the hardware. Here, we estimate the atmospheric refractive index structure constant <i>C</i><i>n</i>2 from distorted optical signals using a neural network that integrates physics-informed feature engineering. The advantage of feature engineering was reconfirmed by principal component analysis of the input samples. The model estimates <i>C</i><i>n</i>2 to an accuracy of up to 99%, within the coherence time relevant to the particular <i>C</i><i>n</i>2. This technique will provide ML-based augmentation in the reduction of link-error probability in free-space optical communication systems.