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

TL;DR

This paper demonstrates that machine learning can accurately predict free-space optical channel turbulence levels directly from raw data without additional hardware, enabling real-time turbulence mitigation in optical communication.

Contribution

It introduces a novel ML-based approach to classify turbulence levels from raw FSO data, eliminating the need for auxiliary sensors.

Findings

ML classification accuracy exceeds 98%

Effectiveness depends on turbulence change timescale

Converges when turbulence stabilizes over about one minute

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.