  Channel turbulence presents a formidable obstacle for free-space optical
(FSO) communication. Anticipation of turbulence levels is highly important for
mitigating disruptions. We study the application of machine learning (ML) to
FSO data streams to rapidly predict channel turbulence levels with no
additional sensing hardware. An optical bit stream was transmitted through a
controlled channel in the lab under six distinct turbulence levels, and the
efficacy of using ML to classify turbulence levels was examined. ML-based
turbulence level classification was found to be >98% accurate with multiple ML
training parameters, but highly dependent upon the timescale of changes between
turbulence levels.
