Satellite Connectivity Prediction for Fast-Moving Platforms

TL;DR

This paper demonstrates that machine learning models can accurately predict satellite signal quality for fast-moving platforms, enabling proactive switching and seamless connectivity in dynamic environments.

Contribution

The paper introduces a machine learning approach to predict satellite signal quality for fast-moving objects, improving connectivity management and reducing switching times.

Findings

ML model achieved an F1 score of 0.97 on test data

Proactive predictions enable seamless satellite handovers

Applicable to various moving platforms like aircraft and trains

Abstract

Satellite connectivity is gaining increased attention as the demand for seamless internet access, especially in transportation and remote areas, continues to grow. For fast-moving objects such as aircraft, vehicles, or trains, satellite connectivity is critical due to their mobility and frequent presence in areas without terrestrial coverage. Maintaining reliable connectivity in these cases requires frequent switching between satellite beams, constellations, or orbits. To enhance user experience and address challenges like long switching times, Machine Learning (ML) algorithms can analyze historical connectivity data and predict network quality at specific locations. This allows for proactive measures, such as network switching before connectivity issues arise. In this paper, we analyze a real dataset of communication between a Geostationary Orbit (GEO) satellite and aircraft over multiple flights, using ML to predict signal quality. Our prediction model achieved an F1 score of 0.97 on the test data, demonstrating the accuracy of machine learning in predicting signal quality during flight. By enabling seamless broadband service, including roaming between different satellite constellations and providers, our model addresses the need for real-time predictions of signal quality. This approach can further be adapted to automate satellite and beam-switching mechanisms to improve overall communication efficiency. The model can also be retrained and applied to any moving object with satellite connectivity, using customized datasets, including connected vehicles and trains.