Scalable Ground Station Selection for Large LEO Constellations

TL;DR

This paper tackles the scalability challenge of GSaaS ground-station site selection for large LEO constellations by introducing a scalable framework that decomposes the global IP into single-satellite, short-time-window subproblems. Subproblem solutions are clustered to identify representative high-value locations, which are then mapped to actual GSaaS sites via Hungarian assignment to yield globally feasible placements. The approach achieves near-optimal results on synthetic Walker-Star tests (within a few percent of the IP optimum) and delivers high-quality site selections for real-world constellations (Capella Space, ICEYE, Planet Flock) where full IP solvers fail to scale, with maximum-gap increases remaining modest. Overall, the method provides a practical, scalable path to design ground networks for megaconstellations, enabling performance close to global optima and offering a foundation for future refinements like targeted post-solve local adjustments.

Abstract

Effective ground station selection is critical for low Earth orbiting (LEO) satellite constellations to minimize operational costs, maximize data downlink volume, and reduce communication gaps between access windows. Traditional ground station selection typically begins by choosing from a fixed set of locations offered by Ground Station-as-a-Service (GSaaS) providers, which helps reduce the problem scope to optimizing locations over existing infrastructure. However, finding a globally optimal solution for stations using existing mixed-integer programming methods quickly becomes intractable at scale, especially when considering multiple providers and large satellite constellations. To address this issue, we introduce a scalable, hierarchical framework that decomposes the global selection problem into single-satellite, short time-window subproblems. Optimal station choices from each subproblem are clustered to identify consistently high-value locations across all decomposed cases. Cluster-level sets are then matched back to the closest GSaaS candidate sites to produce a globally feasible solution. This approach enables scalable coordination while maintaining near-optimal performance. We evaluate our method's performance on synthetic Walker-Star test cases (1-10 satellites, 1-10 stations), achieving solutions within 95% of the global IP optimum for all test cases. Real-world evaluations on Capella Space (5 satellites), ICEYE (40), and Planet's Flock (96) show that while exact IP solutions fail to scale, our framework continues to deliver high-quality site selections.

Figures (10)

• Figure 1: Scalable GSaaS Placement via Decomposition and Clustering
• Figure 2: Optimal full-IP solution for maximum data downlink, varying $\#$ of ground stations and satellites in constellation. Benchmark “true” solution, with all original constraints enforced.
• Figure 3: Performance deviation of DBSCAN with Hungarian matching from optimal in \ref{['fig:IP_solmaxdata']}, maximum data downlink objective. Decomposition performed for shorter time windows and per-satelite subproblems. All solutions within $99\%$ of optimal.
• Figure 4:
• Figure 5: Deviation in performance of DBSCAN and Hungarian matching from optimal full-IP for minimium maximum-gap as a function of selected $\#$ of ground stations and satellites in constellation. Scalable decomposition applied along only time dimensions, with all solutions within $95\%$ of the optimal.