[4K1-IS-2d] Machine learning

[4K1-IS-2d-02] Prediction of radio wave attenuation in space-to-ground communications based on meteorological satellite data

〇Mai Yoshikawa¹, Yuma Abe², Dimitar Kolev², Hiroyuki Tsuji², Ikuko Eguchi YAIRI¹ (1. Graduate School of Science and Technology, Sophia University, 2. National Institute of Information and Communications Technology)

キーワード：deep learning, space communication, satellite image

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.