Work

A q-learning approach for enhanced routing in dynamic LEO satellite networks

Year: 2025

Type: article

Abstract: As global communication demand rises, Low Earth Orbit (LEO) satellite systems offer high-speed data transmission and extensive coverage options but face routing challenges due to dynamic topologies. This paper introduces a Q-Learning-based routing approach that converts dynamic networks into virtually static topologies at different snapshot intervals. Simulation results on a 66-satellite Starlink constellation demonstrate that Q-Learning outperforms Dijkstra’s algorithm, achieving faster convergence and reduced latency. These findings highlight the potential for Q-Learning in enhancing efficient, cost-effective satellite communications.

Source: The Egyptian Journal of Remote Sensing and Space Science

DOI: 10.1016/j.ejrs.2025.05.002

Authors: Ahmed E. Riyad, Medhat Mokhtar, Mohamed A. Belal, Mahmoud Mohamed Bahloul

Institutions: Obour Institutes, National Authority for Remote Sensing and Space Sciences, Helwan University

Language: English

Topic: Satellite Communication Systems

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $900