人工知能学会

In pursuing an intelligent society, the deployment of Beyond 5G/6G is anticipated. A crucial aspect of realizing this vision lies in establishing a robust non-terrestrial network encompassing satellite-based communication systems. However, space-based communication faces challenges from atmospheric disturbances. For instance, Ka-band, a crucial frequency range for satellite communication, is attenuated by rain. Similarly, optical satellite communication links are disrupted by clouds. To ensure reliable and high-quality communication, it is imperative to accurately predict the impact of weather on signal propagation, enabling the selection of ground stations and modulation methods. This research focuses on developing a predictive model for radio wave attenuation during space-to-ground communication, leveraging data from meteorological satellites. The model's core is a deep learning architecture that integrates CNNs, renowned for their proficiency in image feature extraction. The rain attenuation prediction with this model achieved a high coefficient of determination. In addition, to improve the prediction accuracy, we analyzed the complex relationship between the radio wave reception strength and Himawari standard data, a comprehensive dataset acquired from the Himawari meteorological satellite.