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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

A dynamic visibility aware multi orbit satellite selection framework which can determine the optimal serving satellites across orbital layers is proposed and achieves, on average, approximately 7.85% higher sum rate than the best performing baseline.

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.

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

TL;DR

A dynamic visibility aware multi orbit satellite selection framework which can determine the optimal serving satellites across orbital layers is proposed and achieves, on average, approximately 7.85% higher sum rate than the best performing baseline.

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.

Paper Structure

This paper contains 25 sections, 2 theorems, 57 equations, 9 figures, 1 table, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model and Problem Formulation
  4. Network Model
  5. Zenith Angle Rule
  6. Transmission Model
  7. Problem Formulation
  8. Problem Formulation
  9. The Proposed Solution Framework
  10. Markov Approximation
  11. Matching Game Theory
  12. UA
  13. BA
  14. Lagrangian Dual-Based Optimization for PA
  15. Complexity Analysis
  16. ...and 10 more sections

Key Result

Theorem 1

The matching $\boldsymbol{\mu}=\{{\mu_{\text{UA}},\mu_{\text{BA}}}\}$ is stable and can attain a sub-optimal solution given the transmitted power.

Figures (9)

  • Figure 1: Illustration of the zenith angle rule.
  • Figure 2: Spatial distributions of (a) satellites with UEs and (b) UEs.
  • Figure 3: Convergence of DV-MOSS with $P_{\max}=50\hbox{W}$.
  • Figure 4: DV-MOSS's CDF curve with $P_{\max}=50\hbox{W}$.
  • Figure 5: Convergence of matching process ($r_{\text{min}}=0.3$ Mbps).
  • ...and 4 more figures

Theorems & Definitions (4)

  • Theorem 1
  • proof
  • Theorem 2
  • proof