Time-Dependent Network Topology Optimization for LEO Satellite Constellations

2025-01-23

AI Summary

To address the challenges of time-varying topology, constrained inter-satellite links, and multi-objective conflicts in dynamic low Earth orbit (LEO) satellite constellation networking, this paper proposes the Dynamic Optimal Topology Design (DoTD) algorithm based on a Dynamic Time-Expanded Graph (DTEG). The method introduces a time-dependent connection quality scoring function, rigorously proves its boundedness, and thereby overcomes scalability and long-term robustness limitations inherent in static and heuristic approaches. It integrates multi-objective constraint modeling with a distributed real-time link evaluation mechanism, validated on a TLE-driven Mininet simulation platform. Experiments using real Starlink orbital data demonstrate that DoTD outperforms Greedy and +Grid baselines by achieving a 23.6% increase in throughput, a 31.4% reduction in end-to-end latency, and a 47.2% decrease in link handover frequency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Algorithm Configuration

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Today's Low Earth Orbit (LEO) satellite networks, exemplified by SpaceX's Starlink, play a crucial role in delivering global internet access to millions of users. However, managing the dynamic and expansive nature of these networks poses significant challenges in designing optimal satellite topologies over time. In this paper, we introduce the underline{D}ynamic Time-Expanded Graph (DTEG)-based underline{O}ptimal underline{T}opology underline{D}esign (DoTD) algorithm to tackle these challenges effectively. We first formulate a novel space network topology optimization problem encompassing a multi-objective function -- maximize network capacity, minimize latency, and mitigate link churn -- under key inter-satellite link constraints. Our proposed approach addresses this optimization problem by transforming the objective functions and constraints into a time-dependent scoring function. This empowers each LEO satellite to assess potential connections based on their dynamic performance scores, ensuring robust network performance over time without scalability issues. Additionally, we provide proof of the score function's boundary to prove that it will not approach infinity, thus allowing each satellite to consistently evaluate others over time. For evaluation purposes, we utilize a realistic Mininet-based LEO satellite emulation tool that leverages Starlink's Two-Line Element (TLE) data. Comparative evaluation against two baseline methods -- Greedy and $+$Grid, demonstrates the superior performance of our algorithm in optimizing network efficiency and resilience.

Problem

• Low Earth Orbit (LEO) Satellite Constellations
• Time-Varying Network Structure Optimization
• Network Capacity Maximization and Delay Minimization

Innovation

• DoTD Algorithm
• LEO Satellite Networks
• Network Optimization