Scalable Ground Station Selection for Large LEO Constellations

2025-10-03

AI Summary

Ground station (GS) placement for large-scale low-Earth orbit (LEO) satellite constellations faces conflicting objectives—minimizing operational cost, maximizing downlink capacity, and ensuring communication continuity—while constrained by heterogeneous ground station-as-a-service (GSaaS) provider portfolios and prohibitive computational complexity of global optimization. Method: We propose a scalable hierarchical optimization framework that decomposes the global placement problem into single-satellite, short-time-window subproblems. Leveraging a divide-and-conquer strategy integrated with clustering analysis, the framework extracts high-value site patterns from locally optimal solutions to achieve near-global-optimal, coordinated GS placement. It combines mixed-integer programming, unsupervised clustering, and GSaaS provider matching. Contribution/Results: Evaluated on real and synthetic constellations—including Capella, ICEYE, and Planet—the framework achieves >95% of the globally optimal solution while scaling efficiently to constellations comprising nearly 100 satellites.

Technology Category

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

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements

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.

Problem

• Optimizing ground station selection for LEO satellite constellations
• Solving scalable station selection with hierarchical decomposition approach
• Achieving near-optimal performance while maintaining computational tractability

Innovation

• Hierarchical framework decomposes global problem into subproblems
• Clusters optimal station choices to identify high-value locations
• Matches cluster sets to closest GSaaS candidate sites