Examining the Predictability of Starlink Downlink Throughput

Starlink satellite internet has emerged as a viable solution for extending internet connectivity, particularly in remote or underserved areas, complementing terrestrial networks. With the increasing use of Starlink for latency-critical realtime multimedia communication such as live streaming, video conferencing, virtual reality, and online gaming, understanding and predicting Starlink performance becomes increasingly relevant. However, the dynamic nature of satellite constellations, atmospheric conditions, bandwidth availability, and network congestion present hurdles to maintaining steady network performance, which is crucial for real-time multimedia applications. This study investigates the predictability of Starlink downlink throughput, which can aid in proactive scheduling decisions and intelligent traffic management across multiple paths, thereby optimizing Starlink for real-time multimedia applications. The research develops a comprehensive approach for throughput prediction by leveraging historical time series data and a diverse array of machine learning and deep learning models over time slot sizes of 1 s, 100 ms, 50 ms, 20 ms, 10 ms, and 5 ms. Furthermore, the work includes three baseline models to assess the performance enhancement that the machine learning and deep learning models provide. The study proposes a prediction scheme that allows a set of predictors to perform single-step predictions over these prediction intervals. Model evaluation results show that predicting downlink throughput at short time slot sizes of 5 ms and 10 ms for different history sizes offers substantial benefits relative to the baseline models.