Low-earth-orbit (LEO) satellite mega-constellations enable universal Internet access from space. To ensure mission safety, LEO satellites conduct orbital maneuvers to prevent physical collisions with other space objects. These maneuvers challenge the performance of predictive TCP congestion control, which mitigates unnecessary throughput reductions caused by handover-induced data loss/delay. Our empirical study shows that these maneuvers can downgrade predictive TCP congestion control’s throughput by 17.63% due to the maneuvering satellite’s cumulative position deviations. The root cause is that end users rely on coarse-grained Two-Line Elements (TLEs) to predict satellites’ orbits, thus suffering from inaccurate predictions. Although these negative impacts seem unsolvable, fine-grained ephemeris can help end users enhance the accuracy of predicting handover events and effectively manage the durations of freezing congestion windows to a more affordable level. We then propose MATCP, a Maneuver-Aware TCP congestion control scheme to enhance the performance of TCP transport in maneuverable satellite networks. Compared to existing predictive congestion control, MATCP leverages ephemeris to achieve more accurate predictions of satellite handovers, and freezes the congestion window at predicted handover timing with a shorter freezing duration, thus improving the throughput of predictive congestion control. Our evaluations, driven by Starlink’s space situational awareness (SSA) dataset, validate that MATCP can safely prevent 94.21% of maneuver-induced throughput reductions in predictive TCP congestion control and increase throughput by 3.83× compared to TCP CUBIC and 20.07% compared to existing predictive satellite congestion control (SATCP).

Low Earth Orbit (LEO) satellite mega-constellations promise ubiquitous network services to "unconnected" users. But their upcoming global routing for Earth will be unstable due to exhaustive topology updates between satellites and Earth, inside an orbital shell, and across heterogeneous orbital shells. In real LEO networks, these multi-dimensional dynamics are interleaved and complicated by chaotic orbital maneuvers and random failures. They are less predictable than most satellite routing proposals expect and threaten these proposals' availability, efficiency, or resiliency at scale.

Low-earth-orbit (LEO) satellite mega-constellations, such as SpaceX Starlink, are under rocket-fast deployments and promise broadband Internet to remote areas that terrestrial networks cannot reach. For mission safety and sustainable uses of space, Starlink has adopted a proprietary onboard autonomous driving system for its extremely mobile LEO satellites. This paper demystifies and diagnoses its impacts on the LEO mega-constellation and satellite networks. We design a domain-specific method to characterize key components in Starlink's autonomous driving from various public space situational awareness datasets, including continuous orbit maintenance, collision avoidance, and maneuvers between orbital shells. Our analysis shows that, these operations have mixed impacts on the stability and performance of the entire mega-constellation, inter-satellite links, topology, and upper-layer network functions. To this end, we investigate and empirically assess the potential of networking-autonomous driving co-designs for the upcoming satellite networks.