Starfield: Demand-Aware Satellite Topology Design for Low-Earth Orbit Mega Constellations

2026-01-15

AI Summary

This work addresses the limitation of existing low Earth orbit (LEO) satellite constellation designs, which often neglect the interplay among terrestrial traffic distribution, ground station locations, and orbital geometry, thereby failing to meet heterogeneous communication demands. To bridge this gap, we propose Starfield—a traffic-aware heuristic topology design method that, for the first time, integrates real-world ground traffic patterns into LEO constellation planning. By constructing a vector field and a Riemannian metric on the spherical manifold, Starfield guides satellites in selecting optimal inter-satellite links. Experimental results on a Starlink Phase 1 simulation show that Starfield reduces hop count by up to 30% and improves stretch factor by 15% compared to +Grid and random topologies. Under real traffic conditions, even with static deployment, it achieves a 20% improvement in stretch factor and demonstrates robustness against traffic perturbations.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• 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
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Low-Earth orbit (LEO) mega-constellations are emerging as high-capacity backbones for next-generation Internet. Deployment of laser terminals enables high-bandwidth, low-latency inter-satellite links (ISLs); however, their limited number, slow acquisition, and instability make forming a stable satellite topology difficult. Existing patterns like +Grid and Motif ignore regional traffic, ground station placement, and constellation geometry. Given sparse population distribution on Earth and the isolation of rural areas, traffic patterns are inherently non-uniform, providing an opportunity to orient inter-satellite links (ISLs) according to these traffic patterns. In this paper, we propose Starfield, a novel demand-aware satellite topology design heuristic algorithm supported by mathematical analysis. We first formulate a vector field on the constellation's shell according to traffic flows and define a corresponding Riemannian metric on the spherical manifold of the shell. The metric, combined with the spatial geometry, is used to assign a distance to each potential ISL, which we then aggregate over all demand flows to generate a heuristic for each satellite's link selection. Inspired by +Grid, each satellite selects the link with the minimum Riemannian heuristic along with its corresponding angular links. To evaluate Starfield, we developed a custom, link-aware, and link-configurable packet-level simulator, comparing it against +Grid and Random topologies. For the Phase 1 Starlink, simulation results show up to a 30% reduction in hop count and a 15% improvement in stretch factor across multiple traffic distributions. Moreover, static Starfield, an inter-orbital link matching modification of Starfield, achieves a 20% improvement in stretch factor under realistic traffic patterns compared to +Grid. Experiments further demonstrate Starfield's robustness under traffic demand perturbations.

Problem

• LEO mega-constellations
• inter-satellite links
• traffic-aware topology
• non-uniform traffic
• satellite network design

Innovation

• demand-aware topology
• Riemannian metric
• inter-satellite links
• LEO mega-constellations
• vector field