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<p>Satellite communication provides crucial connectivity for low-altitude aircraft to address coverage gaps in ground-based internet and aerial communication blind spots. Achieving this capability requires robust anomaly detection methods. However, existing approaches struggle to capture the complex spatio-temporal relationships in space-air communications due to challenges such as unpredictable network topology from real-time route adjustments, the absence of predefined communication link patterns, and rapid spatio-temporal relationship evolution among moving nodes. To address these challenges, this paper proposes a graph representation anomaly detection framework tailored for communication networks between low Earth orbit (LEO) satellite constellations and low-altitude aircraft. The spatio-temporal relationships between satellites and aircraft are modeled using dynamic graph structures, which capture 3D locations of each node and anomaly characteristics of each link. The proposed framework is compatible with diverse anomaly detection learning algorithms, and several static and dynamic detection algorithms are evaluated and compared in this work. Furthermore, authors present an improved variant of a novel transformer-based anomaly detection framework for dynamic graphs (TADDY), which involves temporal periodic encoding, semi-supervised comparative learning, and multiscale graph attention mechanisms. We evaluate the framework through simulated scenarios, comparing Walker and broken-chain constellations with varying air traffic densities in a specific region. The anomaly detection performances at different anomaly ratios and air traffic densities are evaluated. Experimental results demonstrate that, the performances of static anomaly detection methods and TADDY algorithm degrade significantly as air traffic density increases. Meanwhile, the proposed improved TADDY achieves an average AUC of 0.95 for Walker constellation and 0.86 for the broken-chain constellation, outperforming the original TADDY in both accuracy and reliability under high anomaly rates. Finally, sensitivity analysis and ablation studies confirm the framework’s high responsiveness to anomalies such as abrupt topological changes, offering an efficient solution for ensuring the reliability of large-scale satellite-aircraft communication systems.</p>

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Enhancing space-air communication through anomaly detection in LEO satellite constellations and low-altitude aircraft networks using graph representation learning

  • 期刊论文

  • 发表日期：

    2026年1月4日

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  • Siyang He,
  • Changhao Wu,
  • Chongbin Guo,
  • Zengshan Yin,
  • Ruyi Zhang

摘要

Satellite communication provides crucial connectivity for low-altitude aircraft to address coverage gaps in ground-based internet and aerial communication blind spots. Achieving this capability requires robust anomaly detection methods. However, existing approaches struggle to capture the complex spatio-temporal relationships in space-air communications due to challenges such as unpredictable network topology from real-time route adjustments, the absence of predefined communication link patterns, and rapid spatio-temporal relationship evolution among moving nodes. To address these challenges, this paper proposes a graph representation anomaly detection framework tailored for communication networks between low Earth orbit (LEO) satellite constellations and low-altitude aircraft. The spatio-temporal relationships between satellites and aircraft are modeled using dynamic graph structures, which capture 3D locations of each node and anomaly characteristics of each link. The proposed framework is compatible with diverse anomaly detection learning algorithms, and several static and dynamic detection algorithms are evaluated and compared in this work. Furthermore, authors present an improved variant of a novel transformer-based anomaly detection framework for dynamic graphs (TADDY), which involves temporal periodic encoding, semi-supervised comparative learning, and multiscale graph attention mechanisms. We evaluate the framework through simulated scenarios, comparing Walker and broken-chain constellations with varying air traffic densities in a specific region. The anomaly detection performances at different anomaly ratios and air traffic densities are evaluated. Experimental results demonstrate that, the performances of static anomaly detection methods and TADDY algorithm degrade significantly as air traffic density increases. Meanwhile, the proposed improved TADDY achieves an average AUC of 0.95 for Walker constellation and 0.86 for the broken-chain constellation, outperforming the original TADDY in both accuracy and reliability under high anomaly rates. Finally, sensitivity analysis and ablation studies confirm the framework’s high responsiveness to anomalies such as abrupt topological changes, offering an efficient solution for ensuring the reliability of large-scale satellite-aircraft communication systems.