Machine Learning-Based Mobility-Aware Classification for Satellite Communication Systems

Authors

  • Navya Lalithya, Peddireddy Madhuri, Thallapalli Namitha Reddy, Puligundla Venkata Sriharshini, Ojja Bhargavi

Keywords:

Satellite Communication, Machine Learning, Mobility Pattern Classification, Orbital Trajectory Prediction, Signal Optimization, Decision Trees, Support Vector Machines, Deep Learning, Adaptive Algorithms, Satellite Network Optimization.

Abstract

Satellite communication plays a pivotal role in enabling global connectivity by transmitting data, voice,and multimedia content through satellites positioned in various orbital paths. These satellites, travelingat velocities between 7,000 and 28,000 km/h depending on their altitude, follow specific trajectoriessuch as geostationary, polar, and elliptical orbits. Each orbit allows the satellite

References

Zhan, X.; Ling, Z.; Xu, Z.; Guo, L.; Zhuang, S. Driving efficiency and risk management in finance through AI and RPA. Unique Endeavor Bus. Soc. Sci. 2024, 3, 189–197.

Manoharan, G.; Kumar, V.; Karthik, A.; Asha, V.; Mohan, C.R.; Nijhawan, G. IoT-Based Smart Inventory Management System Using Machine Learning Techniques. In Proceedings of the 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Chennai, India, 9–10 May 2024; IEEE: Piscataway, NJ, USA, 2024; pp. 1–5

Downloads

Published

2025-04-15

How to Cite

Navya Lalithya, Peddireddy Madhuri, Thallapalli Namitha Reddy, Puligundla Venkata Sriharshini, Ojja Bhargavi. (2025). Machine Learning-Based Mobility-Aware Classification for Satellite Communication Systems. Journal of Computational Analysis and Applications (JoCAAA), 34(4), 1249–1255. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3120

Issue

Section

Articles