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

DevFeed: [Spatial-Temporal Learning-Based Distributed Routing for Dynamic LEO Satellite Networks](<https://devfeed.tech/articles/spatial-temporal-learning-based-distributed-routing-for-dynamic-leo-satellite-networks-113842.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2605.02413>)

Author: Po-Heng Chou, Chiapin Wang, Shou-Yu Chen, Hsiang-Ming Wang, Kuan-Fu Chen

Published: 2026-11-03T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Networking](<https://devfeed.tech/sources/arxiv-networking.md>)

Topics: [routing-system](<https://devfeed.tech/topics/routing-system.md>), [AI Networking](<https://devfeed.tech/topics/ai-networking.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>)

Tags: [ablation](<https://devfeed.tech/tags/ablation.md>), [abstract](<https://devfeed.tech/tags/abstract.md>), [arxiv](<https://devfeed.tech/tags/arxiv.md>), [attention](<https://devfeed.tech/tags/attention.md>), [based](<https://devfeed.tech/tags/based.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [cs-ni](<https://devfeed.tech/tags/cs-ni.md>), [deep](<https://devfeed.tech/tags/deep.md>), [delay](<https://devfeed.tech/tags/delay.md>), [delivery](<https://devfeed.tech/tags/delivery.md>), [distributed](<https://devfeed.tech/tags/distributed.md>), [distributed-routing](<https://devfeed.tech/tags/distributed-routing.md>), [earth](<https://devfeed.tech/tags/earth.md>), [evolution](<https://devfeed.tech/tags/evolution.md>), [features](<https://devfeed.tech/tags/features.md>), [learning](<https://devfeed.tech/tags/learning.md>), [leo](<https://devfeed.tech/tags/leo.md>), [networks](<https://devfeed.tech/tags/networks.md>), [routing](<https://devfeed.tech/tags/routing.md>), [satellite](<https://devfeed.tech/tags/satellite.md>), [spatial](<https://devfeed.tech/tags/spatial.md>), [temporal](<https://devfeed.tech/tags/temporal.md>)

## AI overview

The proposed routing framework lets each satellite choose its next hop from local network-state observations and destination information. It combines a graph attention network to represent changing topology and queue states, an LSTM to capture their evolution over time, and a deep Q-network for routing, without a separate traffic-prediction stage. In simulations of a 45-satellite constellation with normal and bursty traffic, packet delivery was comparable to a GNN-enhanced baseline, but the proposed method had higher delay and queue occupancy. Ablations indicated that spatial and temporal learning each contributed benefits.

## Source excerpt

arXiv:2605.02413v2 Announce Type: replace Abstract: This paper proposes a spatial-temporal learning-based routing framework for dynamic low Earth orbit (LEO) satellite networks. The routing problem is formulated as a partially observable Markov decision process (POMDP), where each satellite independently selects its next hop using network-state observations and destination information. A graph attention network (GAT) extracts spatial features from the dynamic satellite topology and queue states, while a long short-term memory (LSTM) network captures their temporal evolution. The resulting representation is integrated into a deep Q-network (DQN) for destination-aware next-hop selection without a separate traffic-prediction stage. Simulations in a 45-satellite LEO constellation under normal and bursty traffic demonstrate packet delivery performance comparable to a graph neural network (GNN)-enhanced routing baseline, although the proposed method incurs higher delay and queue occupancy. Ablation studies further demonstrate the complementary benefits of spatial and temporal learning.