Path-based Deep Reinforcement Learning for On-board Routing in Satellite Constellation Networks

Efficient usage of available network resources is a crucial factor for broadband services in interconnected satellite constellations. To meet required quality of service standards under heavy network loads, it is essential to optimize traffic distribution among the inter-satellite links. To address this challenge, we propose an adaptive traffic engineering framework based on deep reinforcement learning. Our approach employs a path-based decision-making strategy, using a centralized agent to distribute incoming flow requests on a set of candidate paths. This method approximates optimal solutions to the multi-commodity flow problem with relatively low computational complexity, making it suitable for in-space network control despite on-board processing limitations. The performance of the proposed scheme is evaluated against state-of-the-art rule-based benchmarks in various scenarios. We quantify the impact on performance of different candidate-path sets and traffic patterns. Overall, the proposed solution presents a viable approach for optimizing flow distribution in satellite constellation networks, suitable for the integration into the controller logic of software-defined networks. Supplementary Material File (path-based deep reinforcement learning for on-board routing in satellite constellation networks (2).pdf) - Download - 1.81 MB Information & Authors Information Version history Peer review timeline Published International Journal of Satellite Communications and Networking Version of Record20 Feb 2026Published Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 226views 168downloads Citations Download citation Manuel Roth, Thomas Jerkovits, Anupama Hegde, et al. Path-based Deep Reinforcement Learning for On-board Routing in Satellite Constellation Networks. Authorea. 01 September 2025. DOI: https://doi.org/10.22541/au.175672477.72924085/v1 DOI: https://doi.org/10.22541/au.175672477.72924085/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.