Free-Placement Optimization of Ground Station Locations for Low-Earth Orbit Satellites

2025-04-01 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A new two-stage free-placement method, SCORE (Sequential Cyclic Optimization via Refinement & Evaluation), optimizes ground station locations for rapidly expanding Low-Earth Orbit (LEO) satellite constellations. This approach, which combines sequential coordinate selection with cyclic refinement, addresses the limitations of traditional fixed-site selection by operating over a continuous spatial domain. Benchmarked against differential evolution (DE) and integer programming (IP) methods using commercial Earth observation constellations like Capella Space and ICEYE, SCORE demonstrates significant efficiency and performance gains. It requires up to 5x fewer function evaluations to converge than DE while improving downlink throughput by up to 13%. Compared to fixed-site methods, unconstrained SCORE achieves up to 15% greater total downlink, with infrastructure-constrained SCORE retaining over 92% of this gain, establishing a strong empirical benchmark for flexible ground network design.

Key takeaway

For AI Architects and Satellite Network Planners designing or expanding LEO ground station networks, you should evaluate free-placement optimization using methods like SCORE. This approach can yield up to 15% greater data downlink compared to fixed-site selections, even with infrastructure constraints. Prioritize infrastructure-constrained free placement to achieve substantial geometric gains within realistic deployment limits. For polar-orbit constellations, consider multi-antenna upgrades at strategic locations to significantly boost throughput.

Key insights

SCORE optimizes LEO ground station placement via sequential refinement, outperforming fixed-site and differential evolution methods.

Principles

• Continuous spatial optimization yields higher LEO downlink throughput.
• Sequential cyclic refinement manages high-dimensional, non-convex search spaces efficiently.
• Infrastructure proximity constraints balance geometric optimality with deployment feasibility.

Method

SCORE is a two-stage method combining sequential coordinate selection with cyclic refinement, using derivative-free local search (e.g., Nelder-Mead) to manage high-dimensionality and non-convexity.

In practice

• Utilize free-placement optimization for greenfield LEO ground networks to maximize data downlink.
• For polar-orbit constellations, prioritize multi-antenna stations over just expanding geographic footprint.
• Implement infrastructure proximity penalties to guide optimal placement near existing resources.

Topics

• LEO Satellite Constellations
• Ground Station Optimization
• SCORE Algorithm
• Differential Evolution
• Network Design
• Data Downlink

Code references

• sisl/loc-gsopt

Best for: AI Scientist, Research Scientist, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.