Physics based system forecasts LEO satellite internet quality anywhere
Reading the Sky to Forecast the Ground: Physics-Informed Link-State Forecasting for LEO Networks at Any Location
Networking and Internet Architecture
Summary
Low-Earth-orbit (LEO) satellites provide internet, but their connection quality can vary with weather and location, making it hard to predict. This paper presents Gnomon, a system that uses physics and public data sources to forecast download speed, upload speed, and latency for satellite internet users, even with limited local measurements. Gnomon offers three different ways to predict network quality based on what data is available, improving accuracy on new or unseen locations. It also helps streaming video perform better by adjusting quality based on these forecasts.
What this means in practice
- •For network operators: Use Gnomon to predict satellite link quality in new geographical areas and optimize network routing without deploying hardware onsite.
- •For streaming service engineers: Improve adaptive video streaming performance over satellite links by integrating accurate forecasts of bandwidth and latency from Gnomon.
Authors
Yunxiang Chi, Zhenlin An, Longfei Shangguan, Kyle Jamieson
Abstract
In this paper, we introduce Gnomon, a physics-informed system that forecasts user-perceived low-Earth-orbit (LEO) downlink throughput, uplink throughput, and round-trip time (RTT) under different levels of trace availability. Gnomon's physics layer reconstructs the serving geometry and four-leg bent-pipe attenuation from public weather, orbital, routing, and licensing data. Based on what is available, Gnomon conditions on the target terminal's own history (Mode 1), measurements from nearby publicly reachable dishes (Mode 2), or the physical covariates alone (Mode 3) to predict the link state: Modes 1 and 2 share a fine-tuned time-series foundation model, while Mode 3 uses a compact boosted-tree estimator. All three modes expose a common output interface and can be selected without retraining. We evaluate Gnomon using 8,260 minutes of 1 Hz measurements collected at nine sites across five states in the U.S. We train on three sites and hold out the remaining six sites and their serving beams. On these unseen sites, the own-trace mode reduces downlink-throughput and RTT prediction error by 17% and 11% relative to the strongest published baseline and, to our knowledge, provides the first LEO uplink forecasts. The neighbor-trace mode requires no on-site hardware, while the covariate-only mode reduces downlink-throughput and RTT error by 24.6% and 78.8% relative to the only prior covariate-only forecaster. Moreover, experiments show that Gnomon provides calibrated quantile bands and improves adaptive-bitrate streaming driven over real TCP flows on replayed Starlink links.