Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels

Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels

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 operate at higher frequencies due to severe impairments caused by varying atmospheric conditions. Hence, radio channel fore...

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Bibliographic Details
Main Authors: Bassel Al Homssi, Chiu C. Chan, Ke Wang, Wayne Rowe, Ben Allen, Ben Moores, Laszlo Csurgai-Horvath, Fernando Perez Fontan, Sithamparanathan Kandeepan, Akram Al-Hourani
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Transactions on Machine Learning in Communications and Networking
Subjects:
LEO satellites
time-series prediction
machine learning
artificial intelligence
LSTM
rain fading
Online Access:https://ieeexplore.ieee.org/document/10153617/
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