Determination of Optimal Maximum Usable Frequency Prediction Periods for Ionospheric Radio Channels Using the XGBoost Machine Learning Algorithm

An algorithm has been developed for applying the XGBoost machine learning method to solve the problem of predicting the maximum usable frequencies of shortwave communication channels with and without hyperparameter tuning. The optimal (minimum and maximum possible) periods for predicting the maximum...

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Bibliographic Details
Published inSystems of Signals Generating and Processing in the Field of on Board Communications (Online) pp. 1 - 4
Main Author Konkin, N. A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.03.2023
Subjects
Communication channels
Heuristic algorithms
HF communication
hyperopt
hyperparameter
ionosphere
Machine learning
Machine learning algorithms
maximum usable frequency
Predictive models
sounding
time series
Training
XGBoost
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ISSN2768-0118
DOI10.1109/IEEECONF56737.2023.10092186

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Summary:An algorithm has been developed for applying the XGBoost machine learning method to solve the problem of predicting the maximum usable frequencies of shortwave communication channels with and without hyperparameter tuning. The optimal (minimum and maximum possible) periods for predicting the maximum usable frequencies are determined based on training samples compiled from radio sounding data of short-wave communication channels obtained using Ionosonde VSUT equipment. A database of machine learning models XGBoost was obtained for various combinations of hypermeters and forecast periods. The effectiveness of the development was verified for a mid-latitude radio path with a length of 2600 km using the MUF (Maximum usable frequency) dataset for thirty days.
ISSN:2768-0118
DOI:10.1109/IEEECONF56737.2023.10092186