Scalable Ground Station Selection for Large LEO Constellations
This addresses the operational efficiency challenge for satellite operators by providing a scalable solution to a previously intractable optimization problem, though it is incremental as it builds on existing mixed-integer programming methods.
The paper tackles the problem of selecting ground stations for large LEO satellite constellations to minimize costs and maximize data downlink, introducing a scalable hierarchical framework that decomposes the global problem into subproblems. It achieves solutions within 95% of the global optimum in synthetic tests and scales effectively to real-world cases with up to 96 satellites where exact methods fail.
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.