Reinforcement Learning for Opportunistic Routing in Software-Defined LEO-Terrestrial Systems
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917211361247232 |
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| author | Krishnan, Sivaram Gu, Zhouyou Park, Jihong Oh, Sung-Min Choi, Jinho |
| author_facet | Krishnan, Sivaram Gu, Zhouyou Park, Jihong Oh, Sung-Min Choi, Jinho |
| contents | The proliferation of large-scale low Earth orbit (LEO) satellite constellations is driving the need for intelligent routing strategies that can effectively deliver data to terrestrial networks under rapidly time-varying topologies and intermittent gateway visibility. Leveraging the global control capabilities of a geostationary (GEO)-resident software-defined networking (SDN) controller, we introduce opportunistic routing, which aims to minimize delivery delay by forwarding packets to any currently available ground gateways rather than fixed destinations. This makes it a promising approach for achieving low-latency and robust data delivery in highly dynamic LEO networks. Specifically, we formulate a constrained stochastic optimization problem and employ a residual reinforcement learning framework to optimize opportunistic routing for reducing transmission delay. Simulation results over multiple days of orbital data demonstrate that our method achieves significant improvements in queue length reduction compared to classical backpressure and other well-known queueing algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13662 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Opportunistic Routing in Software-Defined LEO-Terrestrial Systems Krishnan, Sivaram Gu, Zhouyou Park, Jihong Oh, Sung-Min Choi, Jinho Networking and Internet Architecture Machine Learning The proliferation of large-scale low Earth orbit (LEO) satellite constellations is driving the need for intelligent routing strategies that can effectively deliver data to terrestrial networks under rapidly time-varying topologies and intermittent gateway visibility. Leveraging the global control capabilities of a geostationary (GEO)-resident software-defined networking (SDN) controller, we introduce opportunistic routing, which aims to minimize delivery delay by forwarding packets to any currently available ground gateways rather than fixed destinations. This makes it a promising approach for achieving low-latency and robust data delivery in highly dynamic LEO networks. Specifically, we formulate a constrained stochastic optimization problem and employ a residual reinforcement learning framework to optimize opportunistic routing for reducing transmission delay. Simulation results over multiple days of orbital data demonstrate that our method achieves significant improvements in queue length reduction compared to classical backpressure and other well-known queueing algorithms. |
| title | Reinforcement Learning for Opportunistic Routing in Software-Defined LEO-Terrestrial Systems |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2601.13662 |