Deep learning forecasting and statistical modeling for Q/V-band LEO satellite channels

B Al Homssi, CC Chan, K Wang… - … Machine Learning in …, 2023‏ - ieeexplore.ieee.org
As the number of satellite networks increases, the radio spectrum is becoming more
congested, prompting the need to explore higher frequencies. However, it is more difficult to …

Satellite to ground station, attenuation prediction for 2.4–72 GHz using LTSM, an artificial recurrent neural network technology

MM Domb Alon, G Leshem - Electronics, 2022‏ - mdpi.com
Satellite communication links suffer from arbitrary weather phenomena such as clouds, rain,
snow, fog, and dust. Furthermore, when signals approach the ground station, they have to …

Survey of Satellite-ground Channel Models for Low Earth Orbit Satellites

Z SU, LIU Liu, B AI, T ZHOU, Z HAN, X DUAN… - 电子与信息学报, 2024‏ - jeit.ac.cn
Abstract Low Earth Orbit (LEO) satellite has the characteristics of low communication delay,
low deployment cost and wide coverage, and has become an important part of the …

Rain Attenuation Prediction Modeling for Microwave and Millimeter Wave Band Using LSTM

HE Legesse, L **ong, D **g**g - 2024 4th International …, 2024‏ - ieeexplore.ieee.org
This study introduces a Long-Short Term Memory (LSTM) network prediction model that can
accurately forecast rain attenuation in tropical regions, where weather-related interference is …

[PDF][PDF] 面向低轨卫星的星地信道模型综述

苏昭阳, 刘留, 艾渤, 周涛, **紫杰, 段相龙… - Journal of Electronics & …, 2022‏ - jeit.ac.cn
低轨卫星(LEO) 具备通信时延低, 部署成本低, 覆盖范围广的特点, 已经成为了建设未来空天地
一体化网络的重要组成部分. 然而卫星通信中端到端传播距离长, 经历衰落复杂 …

[PDF][PDF] Satellite to Ground Station, Attenuation Prediction for 2.4–72 GHz Using LTSM, an Artificial Recurrent Neural Network Technology. Electronics 2022, 11, 541

MM Domb Alon, G Leshem - 2022‏ - academia.edu
Satellite communication links suffer from arbitrary weather phenomena such as clouds, rain,
snow, fog, and dust. Furthermore, when signals approach the ground station, they have to …