# Optimal Ground Station Selection for Low-Earth Orbiting Satellites

arXiv: https://arxiv.org/abs/2410.16282 · HTML: https://mlsanity.com/paper/2410.16282/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [A Maximum Independent Set Method for Scheduling Earth Observing Satellite Constellations](https://mlsanity.com/paper/2008.08446/) (2020)
- [Markov Decision Processes For Multi-Objective Satellite Task Planning](https://mlsanity.com/paper/1910.08419/) (2019)
- [Globally Optimal Selection of Ground Stations in Satellite Systems with Site Diversity](https://mlsanity.com/paper/1912.12911/) (2019)
- [Minimizing the Installation Cost of Ground Stations in Satellite Networks: Complexity, Dynamic Programming and Approximation Algorithm](https://mlsanity.com/paper/2005.07498/) (2020)

## Built on by (most cited first, 2 of 2)

- [Scalable Ground Station Selection for Large LEO Constellations](https://mlsanity.com/paper/2510.03438/) (2025)
- [Free-Placement Optimization of Ground Station Locations for Low-Earth Orbit Satellites](https://mlsanity.com/paper/2606.12667/) (2026)

## Abstract (arXiv)

This paper presents a solution to the problem of optimal ground station selection for low-Earth orbiting (LEO) space missions that enables mission operators to precisely design their ground segment performance and costs. Space mission operators are increasingly turning to Ground-Station-as-a-Service (GSaaS) providers to supply the terrestrial communications segment to reduce costs and increase network size. However, this approach leads to a new challenge of selecting the optimal service providers and station locations for a given mission. We consider the problem of ground station selection as an optimization problem and present a general solution framework that allows mission designers to set their overall optimization objective and constrain key mission performance variables such as total data downlink, total mission cost, recurring operational cost, and maximum communications time-gap. We solve the problem using integer programming (IP). To address computational scaling challenges, we introduce a surrogate optimization approach where the optimal station selection is determined based on solving the problem over a reduced time domain. Two different IP formulations are evaluated using randomized selections of LEO satellites of varying constellation sizes. We consider the networks of the commercial GSaaS providers Atlas Space Operations, Amazon Web Services (AWS) Ground Station, Azure Orbital Ground Station, Kongsberg Satellite Services (KSAT), Leaf Space, and Viasat Real-Time Earth. We compare our results against standard operational practices of integrating with one or two primary ground station providers.