Abstract
In this paper we address the deterministic, multi-satellite, multi-ground station communi- cation scheduling problem. As the number of small satellites in space increases, so does the demand for downloading their large quantities of acquired data. Given the capacity-constrained ground station network, e_cient scheduling plays a major role in the overall performance of missions. The small-satellite dynamics of collecting, storing, using, and spilling both data and energy further complicates schedule design. In this paper we extend previous work on the single-satellite scheduling problem in order to incorporate simulta- neously scheduling downloads from multiple satellites to a ground station network with the objective of maximizing the total amount of data downloaded to Earth. We assume that ground stations are restricted to receiving data from at most one satellite at a time and compare the results to those of the case where ground stations may receive data from multiple satellites concurrently in order to determine the potential download increase from such an enhanced communication capability. We create a greedy scheduling heuristic in order to compare our model's performance to a reasonable approximation of current scheduling methods. We test our model on a variety of scenarios generated from de_ned probability distributions to demonstrate how the model can be used to analyze the download performance of a satellite constellation. We also study the model's computational performance limits and sensitivity.
Presentation
Scheduling Downloads for Multi-Satellite, Multi-Ground Station Missions
In this paper we address the deterministic, multi-satellite, multi-ground station communi- cation scheduling problem. As the number of small satellites in space increases, so does the demand for downloading their large quantities of acquired data. Given the capacity-constrained ground station network, e_cient scheduling plays a major role in the overall performance of missions. The small-satellite dynamics of collecting, storing, using, and spilling both data and energy further complicates schedule design. In this paper we extend previous work on the single-satellite scheduling problem in order to incorporate simulta- neously scheduling downloads from multiple satellites to a ground station network with the objective of maximizing the total amount of data downloaded to Earth. We assume that ground stations are restricted to receiving data from at most one satellite at a time and compare the results to those of the case where ground stations may receive data from multiple satellites concurrently in order to determine the potential download increase from such an enhanced communication capability. We create a greedy scheduling heuristic in order to compare our model's performance to a reasonable approximation of current scheduling methods. We test our model on a variety of scenarios generated from de_ned probability distributions to demonstrate how the model can be used to analyze the download performance of a satellite constellation. We also study the model's computational performance limits and sensitivity.
