Visibility-aware Satellite Selection and Resource Allocation in Multi-Orbit LEO Networks
Yingzhuo Sun, Yulan Gao, Ming Xiao, Zhu Han, Octavia A. Dobre

TL;DR
This paper introduces a dynamic, visibility-aware satellite selection and resource allocation framework for multi-orbit LEO networks, optimizing coverage and sum rate amid complex orbital dynamics.
Contribution
It proposes a novel optimization framework combining Markov approximation and matching game theory for satellite selection and resource management in multi-orbit LEO networks.
Findings
Achieves approximately 7.85% higher sum rate than baselines.
Converges to a suboptimal solution across various scenarios.
Effectively manages dynamic visibility and heterogeneous coverage patterns.
Abstract
Multi orbit low earth orbit (LEO) satellites communication is envisioned as a key infrastructure to deliver global coverage, enabling future services from space air ground integrated networks.However, the optimized design of LEO which jointly addresses satellite selection, association control, and resource scheduling while accounting for dynamic visibility in multi orbit constellations still remains open. Satellites moving along distinct orbital planes yield phase shifted ground tracks and heterogeneous, time varying coverage patterns that significantly complicate the optimization.To bridge the gap, we propose a dynamic visibility aware multi orbit satellite selection framework which can determine the optimal serving satellites across orbital layers. The framework is built upon Markov approximation and matching game theory. Specifically, we formulate a combinatorial optimization problem…
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Taxonomy
TopicsSatellite Communication Systems · Spacecraft Dynamics and Control · Spacecraft Design and Technology
