Multi‐Source Fusion Deep Learning for Predicting Satellite Communication Link Quality in Environmental Monitoring Cyber‐Physical Systems

Remote environmental monitoring covers large and heterogeneous regions, yet large target areas lack communication stations, creating a persistent backhaul constraint. To provide connectivity, monitoring‐oriented cyber–physical systems adopt low earth orbit satellite links. However, a LEO satellite offers only short, intermittent contact windows, and both window availability and in‐window link quality vary with satellite pass geometry and weather driven disturbances. In this paper, a multimodal fusion model, Residual Multi‐Modal Temporal Fusion Network (RMM‐TFNet) that integrates numerical meteorological variables, historical communication statistics, and sky images is proposed to predict access window availability and signal quality. The architecture employs two modality specific encoders and combines their representations through two complementary fusion modules. A series of controlled data transmission experiments were conducted, yielding a corpus of communication link measurements in practical situations. The proposed model is found to achieve higher predictive accuracy than competitive deep learning baselines by evaluating on these measurements.