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

TL;DR

Starfield introduces a demand-aware topology design for LEO ISLs by embedding satellites on a spherical shell with a Riemannian metric derived from traffic vector fields. Links are oriented toward dominant traffic geodesics, enabling a κ-max-degree topology that minimizes end-to-end stretch while balancing hop count via a tunable parameter; static variants also enable fixed inter-orbital patterns. In simulations modeled on Phase 1 Starlink, Starfield achieves up to 15% reduction in stretch and 30% reduction in hop count across diverse traffic patterns, with robustness to demand perturbations and improved path smoothness compared to +Grid and Random baselines. A theoretical 2D analysis provides a bound on worst-case stretch and clarifies how field alignment governs path efficiency, pointing to future extensions to multi-shell constellations and time-varying traffic dynamics.

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.

Figures (13)

• Figure 1: +Grid topology (left) and diagonally oriented topology (right) on a grid of satellites.
• Figure 2: Geodesic flows (orange lines) between 100 highly populated cities under the distance–population demand pattern (left), with line thickness representing traffic volume. Green and red dots denote Phase 1 Starlink satellites and ground stations, respectively. The corresponding regional directional flow components are shown as a log-scaled arrow plot with weighted mean resultant length 0.72 (right).
• Figure 3: Visualization of the electric vector field (left) and the proposed vector field (right) on the spherical shell under the influence of traffic flow between between a source and destination. Vector magnitudes are ignored for clarity at each satellite.
• Figure 4: CDFs of city-to-city stretch factor for static Starfield, +Grid, and Random. Vertical lines indicate the $90^{\text{th}}$ percentile.
• Figure 5: Link usage ratio histogram of static Starfield, +Grid, and Random.

Theorems & Definitions (3)

• theorem 1
• theorem 2
• corollary 1