In this paper, we propose a spatial-temporal learning-based distributed routing framework for dynamic Low Earth Orbit (LEO) satellite networks, where graph attention networks (GAT) and long short-term memory (LSTM) are integrated within a deep Q-network (DQN)-based architecture to enable distributed and adaptive routing decisions based on local observations. The routing problem is formulated as a partially observable Markov decision process (POMDP) to address partial observability under dynamic topology and time-varying traffic. Simulation results show that the proposed method significantly outperforms conventional and learning-based routing schemes in terms of throughput, packet loss, queue length, and end-to-end delay, while achieving proactive congestion avoidance with up to 23.26% queue reduction. In addition, the proposed approach maintains low computational overhead with negligible carbon emissions, demonstrating its efficiency from a Green AI perspective.
@article{arxiv.2605.02413,
title = {Spatial-Temporal Learning-Based Distributed Routing for Dynamic LEO Satellite Networks},
author = {Po-Heng Chou and Chiapin Wang and Shou-Yu Chen and Hsiang-Ming Wang},
journal= {arXiv preprint arXiv:2605.02413},
year = {2026}
}
Comments
6 pages, 4 figures, 3 tables, and submitted to 2026 IEEE Globecom