A Machine Learning Framework for Satellite Data Transmission Duration Prediction: Enhancing Mission Planning Efficiency

Abstract

Satellite mission planning and optimization require precise scheduling to maximize the efficiency of communication and payload operations. This paper presents a machine learning (ML)-based framework for predicting data transmission durations for upload and download tasks during satellite passes. The framework leverages historical data from a network of two 3U satellite systems, UM5-EOSAT and UM5-RIBAT, and integrates real-world constraints including S-band antenna limitations, antenna coverage, elevation mask constraints, pass geometry, and the size of files to be downloaded. Feature engineering and model evaluation, including XGBoost, LightGBM, and Random Forest, reveal XGBoost’s superior predictive performance R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 96%, MAE= 10.25 seconds. This framework enhances scheduling efficiency and operational performance, contributing to more effective mission planning.