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

Md Zobaer Islam, Ethan Abele, Fahim Ferdous Hossain,Arsalan Ahmad,Sabit Ekin,John F. O'Hara

CoRR(2024)

Cited 0|Views0
No score
Abstract
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 training parameters, but highly dependent upon the timescale of changes between turbulence levels.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined