# Spatial-Temporal Learning-Based Distributed Routing for Dynamic LEO Satellite Networks

arXiv: https://arxiv.org/abs/2605.02413 · HTML: https://mlsanity.com/paper/2605.02413/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 4 May 2026.

## Builds on (most cited first, 2 of 2)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [DRL-Based Beam Positioning for LEO Satellite Constellations with Weighted Least Squares](https://mlsanity.com/paper/2511.08852/) (2025)

## Abstract (arXiv)

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.