Optimal Ground Station Selection for Low-Earth Orbiting Satellites

2024-10-04

arXiv.org

AI Summary

To address the ground station selection challenge for low-Earth-orbit (LEO) satellite missions, this paper proposes a multi-objective optimization framework jointly constrained by downlink data volume, total cost, operational overhead, and maximum communication gap—enabling co-design of ground-segment performance and cost. Methodologically, we introduce a novel time-domain dimensionality-reduction surrogate optimization technique to overcome computational intractability inherent in full-constellation, full-lifecycle, integer-programming (IP) formulations. For the first time, six commercial Ground Station-as-a-Service (GSaaS) providers—including Atlas, AWS, and Azure—are integrated into a unified comparative evaluation framework. Validated across multiple LEO constellation scales, our approach achieves, on average, a 23% increase in downlink data volume and a 31% reduction in annual operational cost compared to conventional single- or dual-station configurations, while significantly improving mission continuity.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Optimal ground station selection for LEO satellites
• Balancing mission performance and cost using GSaaS
• Integer programming for scalable station selection optimization

Innovation

• Uses integer programming for optimal ground station selection.
• Introduces surrogate optimization to reduce computational complexity.
• Evaluates multiple GSaaS providers for cost and performance optimization.