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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Bibliografiske detaljer

Principais autores:	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:	Artigo
Sprog:	Inglês
Udgivet:	IEEE 2023-01-01
Serier:	IEEE Transactions on Machine Learning in Communications and Networking
Fag:	LEO satellites time-series prediction machine learning artificial intelligence LSTM rain fading
Online adgang:	https://ieeexplore.ieee.org/document/10153617/
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