Supervised Machine Learning for Refractive Index Structure Parameter Modeling

The Hellenic Naval Academy (HNA) reports the latest results from an over water, free-space optical (FSO) LASER communications test facility, between the lighthouse of Psitalia Island and HNA's laboratory building. The FSO link was established in the premises of Piraeus port, with a path length...

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Bibliographic Details
Published in2023 12th International Conference on Modern Circuits and Systems Technologies (MOCAST) pp. 1 - 4
Main Authors Lionis, Antonios, Peppas, Konstantinos, Tsigkopoulos, Andreas, Nistazakis, Hector E., Cohn, Keith, Drexler, Kyle R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.06.2023
Subjects
Atmospheric modeling
free space optical communication
Integrated circuit modeling
machine learning
Meters
neural network
Optical refraction
Optical variables control
Prediction algorithms
random forest
Refractive index
refractive index structure parameter
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Summary:The Hellenic Naval Academy (HNA) reports the latest results from an over water, free-space optical (FSO) LASER communications test facility, between the lighthouse of Psitalia Island and HNA's laboratory building. The FSO link was established in the premises of Piraeus port, with a path length of 2958 meters and an average altitude of 35 meters, primarily above water. Recently, the facility has been upgraded with a BLS450 scintillometer, MRV TS5000/155 FSO system, and an Ambient Weather WS-2000 weather station. This paper presents the preliminary optical turbulence measurements, collected from 24 to 31 of May 2022, alongside with the macroscopic meteorological parameters. Four machine learning algorithms i.e. Random Forest, Single Layer Neural Network, Deep Neural Network and Gradient Boosting Regressor, were explored to predict the refractive index structure parameter via regression modeling.
DOI:10.1109/MOCAST57943.2023.10176724