Spatial-Temporal Learning-Based Distributed Routing for Dynamic LEO Satellite Networks
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915977138012160 |
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| author | Chou, Po-Heng Wang, Chiapin Chen, Shou-Yu Wang, Hsiang-Ming |
| author_facet | Chou, Po-Heng Wang, Chiapin Chen, Shou-Yu Wang, Hsiang-Ming |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_02413 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Spatial-Temporal Learning-Based Distributed Routing for Dynamic LEO Satellite Networks Chou, Po-Heng Wang, Chiapin Chen, Shou-Yu Wang, Hsiang-Ming Networking and Internet Architecture Machine Learning 68M10, 68T05 C.2.2; I.2.6; I.2.11 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. |
| title | Spatial-Temporal Learning-Based Distributed Routing for Dynamic LEO Satellite Networks |
| topic | Networking and Internet Architecture Machine Learning 68M10, 68T05 C.2.2; I.2.6; I.2.11 |
| url | https://arxiv.org/abs/2605.02413 |