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iSatCR: Graph-Empowered Joint Onboard Computing and Routing for LEO Data Delivery

arXiv 2603.18539 ↗First posted 19 Mar 2026cs.NI cs.LGcited by 0

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Abstract (arXiv)

Sending massive Earth observation data produced by low Earth orbit (LEO) satellites back to the ground for processing consumes a large amount of on-orbit bandwidth and exacerbates the space-to-ground link bottleneck. Most prior work has concentrated on optimizing the routing of raw data within the constellation, yet cannot cope with the surge in data volume. Recently, advances in onboard computing have made it possible to process data in situ, thus significantly reducing the data volume to be transmitted. In this paper, we present iSatCR, a distributed graph-based approach that jointly optimizes onboard computing and routing to boost transmission efficiency. Within iSatCR, we design a novel graph embedding utilizing shifted feature aggregation and distributed message passing to capture satellite states, and then propose a distributed graph-based deep reinforcement learning algorithm that derives joint computing-routing strategies under constrained on-board storage to handle the complexity and dynamics of LEO networks. Extensive experiments show iSatCR outperforms baselines, particularly under high load.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 52 references that are in the map. First posted on arXiv 19 Mar 2026.

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References · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
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  11. 2024Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communicationscited by 18
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3 more
  1. 2022ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networkscited by 5
  2. 2022Computing-Aware Routing for LEO Satellite Networks: A Transmission and Computation Integration Approachcited by 3
  3. 2025Travel Time and Weather-Aware Traffic Forecasting in a Conformal Graph Neural Network Frameworkcited by 3

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