Measuring and modeling link quality in ground station networks for small satellite operations

We show a generic and widely applicable approach to passively measure and compare link quality that can be applied in a heterogeneous ground station and satellite configuration. Besides such a-posteriori measures of the link quality, we also develop methods to give an a-priori estimate of the expected link quality of a future pass. In combination with proper scheduling methods for ground station networks, such an a-priori link quality information has high impact on the efficient usage of ground station and satellite resources and could help to increase mission data return. In a case study, we analyze signal strength measurements of over 550 passes of AAUSAT-II, a CubeSat which transmits in the ultra high frequency band. For this purpose, we extract a link quality measure based on the signal-to-noise ratio from the digital S-meter readings of the receiving ground station radio. We then investigate influences on the link quality among pass-related data as well as surface and space weather data from publicly available Internet resources. Furthermore, we formulate the prediction of the quality of a future pass as a machine learning task and create models to solve it. We show that the surface and space weather information as well as the antenna pointing direction can enhance the precision of such prediction models.