Reinforcement Learning for Opportunistic Routing in Software-Defined LEO-Terrestrial Systems
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 19 references that are in the map. First posted on arXiv 20 Jan 2026.
Builds on
4 of 19 references.
References · 4
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
- 2018Residual Policy Learningcited by 164
- 2024A Tutorial on Non-Terrestrial Networks: Towards Global and Ubiquitous 6G Connectivitycited by 3
- 2024A Scalable Architecture for Future Regenerative Satellite Payloadscited by 1
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Citing papers per year: 2026: 1.
1 paper.
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- 2026Queue-Aware and Resilient Routing in LEO Satellite Networks Using Multi-Agent Reinforcement Learning ↗
- 2024An open source Multi-Agent Deep Reinforcement Learning Routing Simulator for satellite networks
- 2026iSatCR: Graph-Empowered Joint Onboard Computing and Routing for LEO Data Delivery
- 2025On the Role of AI in Managing Satellite Constellations: Insights from the ConstellAI Project
- 2024Shaping Rewards, Shaping Routes: On Multi-Agent Deep Q-Networks for Routing in Satellite Constellation Networks
- 2024Continual Deep Reinforcement Learning for Decentralized Satellite Routing
- 2024Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing
- 2024Throughput and Link Utilization Improvement in Satellite Networks: A Learning-Enabled Approach ↗