Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach

Zobaer Islam, Md [School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA] (ORCID:0000000256150273); Abele, Ethan [School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA] (ORCID:000900087742799X); Ferdous Hossain, Fahim [School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA] (ORCID:0000000182087768); Ahmad, Arsalan [Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA] (ORCID:0000000254677901); Ekin, Sabit [Department of Engineering Technology, Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA] (ORCID:0000000299577752); O’Hara, John F. [School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA] (ORCID:0000000181931867)

doi:10.1109/lcomm.2025.3555162

DOE OSTI · 2860357

Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach

Zobaer Islam, Md [School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA] (ORCID:0000000256150273)·Abele, Ethan [School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA] (ORCID:000900087742799X)·Ferdous Hossain, Fahim [School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA] (ORCID:0000000182087768)·Ahmad, Arsalan [Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA] (ORCID:0000000254677901)·Ekin, Sabit [Department of Engineering Technology, Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA] (ORCID:0000000299577752)·O’Hara, John F. [School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA] (ORCID:0000000181931867)

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Abstract

Channel turbulence is a formidable obstacle for freespace optical (FSO) communication. Anticipation of turbulence levels is highly important for mitigating disruptions but has not been demonstrated without dedicated, auxiliary hardware. We show that machine learning (ML) can be applied to raw FSO data streams to rapidly predict channel turbulence levels with no additional sensing hardware. FSO was conducted 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. Classification effectiveness was found to depend on the timescale of changes between turbulence levels but converges when turbulence stabilizes over about a one minute timescale.

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Zobaer Islam, Md [School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA] (ORCID:0000000256150273), Abele, Ethan [School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA] (ORCID:000900087742799X), Ferdous Hossain, Fahim [School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA] (ORCID:0000000182087768), Ahmad, Arsalan [Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA] (ORCID:0000000254677901), Ekin, Sabit [Department of Engineering Technology, Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA] (ORCID:0000000299577752), O’Hara, John F. [School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA] (ORCID:0000000181931867). 2025-05-01. Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach. https://doi.org/10.1109/lcomm.2025.3555162

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