Visibility-aware Satellite Selection and Resource Allocation in Multi-Orbit LEO Networks

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 that maximizes the sum rate under per satellite power budgets. The problem is NP hard , combining discrete user association (UA) decisions with continuous power allocation, and an inherently non convex sum rate maximization objective. We address it through a problem specific Markov approximation. Moreover, we alternately solve UA or bandwidth allocation via a matching game and power allocation via a Lagrangian dual program, which together form a block coordinate descent method tailored to this problem. Simulation results show that the proposed algorithm converges to a suboptimal solution across all scenarios. Extensive experiments against four state of the art baselines further demonstrate that our algorithm achieves, on average, approximately 7.85% higher sum rate than the best performing baseline.