Enabling Efficient Transmission of Satellite-to-Ground Downlinks via Throughput Prediction

Low Earth Orbit satellites play a vital role in Earth observation and remote sensing missions, with their satellite-to-ground downlinks responsible for transmitting substantial volumes of imagery and data. However, the inherent dynamism and instability of satellite-to-ground links present challenges to efficient data transmission. While existing research often concentrates on predicting physical layer parameters, our empirical measurements reveal that solely relying on physical layer predictions is insufficient to reflect application layer throughput accurately. To address this, we propose Satformer, a spatio-temporal prediction model, and based on it, the Spatio-Temporal cross-layer Adaptive Rate control (STAR) mechanism to achieve efficient satellite-to-ground downlink transmission. To bridge the gap between physical and application layers, Satformer employs tailored Sat-Embedding and a spatio-temporal attention mechanism that explicitly correlates satellite relative position data and weather data with physical layer data to predict application layer throughput. Experimental results show that Satformer improves application layer throughput prediction accuracy by up to 33.45% compared to baseline models. Building on this, the STAR mechanism increases satellite-to-ground data transmission throughput by an average of 12.12%, while reducing median end-to-end latency by up to 43.3%.