Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach
Abstract
Channel turbulence is a formidable obstacle for free-space 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.
Keywords
Cite
@article{arxiv.2405.16729,
title = {Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach},
author = {Md Zobaer Islam and Ethan Abele and Fahim Ferdous Hossain and Arsalan Ahmad and Sabit Ekin and John F. O'Hara},
journal= {arXiv preprint arXiv:2405.16729},
year = {2025}
}
Comments
5 pages, 4 figures, 3 tables, accepted for publication in IEEE Communications Letters