Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Jiangtao Luo, Bingbing Xu, Shaohua Xia, Yongyi Ran

Abstract

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.

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

Abstract

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.
Paper Structure (42 sections, 16 equations, 9 figures, 3 tables, 1 algorithm)

This paper contains 42 sections, 16 equations, 9 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. Routing Strategies for LEO Satellite Networks
  4. Joint Optimization of Computing and Transmission
  5. Graph Based DRL Methods in Communication
  6. System Model and Problem Formulation
  7. Network Model
  8. Task Model
  9. Delay Model
  10. Problem Formulation
  11. Proposed Approach of Integrated Computing and Routing
  12. Distributed Framework of Joint Decision for Computing and Routing
  13. Resource Awareness
  14. Decision Making
  15. Graph Embedding For Resource Awareness
  16. ...and 27 more sections

Figures (9)

  • Figure 1: Scenario of an observation task to be computed onboard and results transmitted back to the ground.
  • Figure 2: Distributed Framework of joint decision for computing and routing.
  • Figure 3: Proposed distributed graph embedding method. (a) Different regions of the graph representation correspond to the current satellite feature, neighboring satellites, and 2-hop neighboring satellites, respectively. (b) An example of the distributed resource awareness mechanism, in which satellites can perceive resource information within a 3-hop range by integrating the graph representations of neighboring satellites. (c) Message passing and feature aggregating of graph representations in distributed graph embedding mechanism.
  • Figure 4: D3QN-based joint optimization of computing and routing for LEO satellites in training.
  • Figure 5: Average reward changing curve during DRL training.
  • ...and 4 more figures