Topology-Compressed Data Delivery in Large-Scale Heterogeneous Satellite Networks: An Age-Driven Spatial-Temporal Graph Neural Network Approach

In Large-Scale Heterogeneous Satellite Networks (LSHSNs) integrating Low Earth Orbit (LEO) and Medium Earth Orbit (MEO) satellites, high-timeliness data delivery confronts dynamical connectivity and obvious latency, which heavily challenges existing graph-dependable transmission strategies requiring...

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Bibliographic Details
Published in:IEEE transactions on mobile computing Vol. 24; no. 7; pp. 6673 - 6687
Main Authors: Gao, Ronghao, Zhang, Bo, Zhang, Qinyu, Yang, Zhihua
Format: Magazine Article
Language:English
Published: IEEE 01.07.2025
Subjects:
age of information (AoI)
Computational modeling
data delivery
Feature extraction
graph neural network (GNN)
Heuristic algorithms
Large-scale heterogeneous satellite network (LSHSN)
Low earth orbit satellites
Measurement
Network topology
Routing
Satellite broadcasting
Satellites
Topology
ISSN:1536-1233, 1558-0660
Online Access:Get full text
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Summary:In Large-Scale Heterogeneous Satellite Networks (LSHSNs) integrating Low Earth Orbit (LEO) and Medium Earth Orbit (MEO) satellites, high-timeliness data delivery confronts dynamical connectivity and obvious latency, which heavily challenges existing graph-dependable transmission strategies requiring to obtain global topological information with huge computational cost and signaling overhead. To address this issue, in this paper, we propose an Age-predicting Local Information Dependable Transmission (ALIDT) mechanism for the LSHSN by considering the impact of time-varying topology on the timeliness of data, in which a novel metric of data freshness called Forwarding-aware Age of Information (FAoI) is well-designed to evaluate the timeliness in data forwarding at node. In particular, we develop a satellite Coverage-based Local Information Sharing (CLIS)-assisted Spatial-Temporal Graph Neural Network (STGNN) to extract the topological features in both temporal and spatial dimensions and a Graph Matching Network (GMN)-based topology compression algorithm to improve computation efficiency. The simulation results indicate that the proposed mechanism performs better in improving the storage overhead, throughput and average FAoI compared with the conventional Open Shortest Path First (OSPF) routing algorithm with Time-Varying Graph (TVG) model, GNN-based Multipath Routing (GMR) algorithm, and Gated Recurrent Units (GRU) based metric prediction algorithm in hybrid satellite networks, respectively.
ISSN:1536-1233
1558-0660
DOI:10.1109/TMC.2025.3544574