  Atmospheric turbulence strength (Cn2 parameter) sensing based on processing
of intensity scintillation patterns with deep neural network (DNN) is
considered. It is shown that DNN re-training with propagation distance change
can be avoided by scaling of Cn2 values obtained using a DNN trained for a
nominal distance L0 . The required Cn2 scaling factor can be obtained using
either an analytical expression derived from the Kolmogorov turbulence theory
(theory-based scaling), or through wave-optics numerical modeling and
simulations (M&S-based scaling).
