RT Journal Article
T1 Deep learning forecasting and statistical modeling for Q/V-band LEO satellite channels
A1 Homssi, Bassel Al
A1 Chan, Chiu C.
A1 Wang, Ke
A1 Rowe, Wayne
A1 Allen, Ben
A1 Moores, Ben
A1 Csurgai-Horváth, László
A1 Pérez Fontán, Fernando
A1 Kandeepan, Sithamparanathan
A1 Al-Hourani, Akram
K1 3325.06 Comunicaciones Por Satélite
K1 2202.09 Propagación de Ondas Electromagnéticas
K1 1203.04 Inteligencia Artificial
AB 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 forecasting is crucial for operators to adjust and maintain the link’s quality. This paper presents a practical approach for Q/V-band modeling for low Earth orbit satellite channels based on tools from machine learning and statistical modeling. The developed Q/V-band LEO satellite channel model is composed of: 1) forecasting method using model-based deep learning, intended for real-time operation of satellite terminals; and 2) statistical channel simulator that generates a time-series path-loss random process, intended for system design and research. Both approaches capitalize on real-measurements obtained from AlphaSat’s Q/V-band transmitter at different geographic latitudes. The results show that model-based deep learning can outperform simple statistical and deep learning methods by at least 50%. Moreover, the model is capable of incorporating varying rain and elevation angle profiles
PB IEEE Transactions on Machine Learning in Communications and Networking
SN 2831316X
YR 2023
FD 2023-06-15
LK http://hdl.handle.net/11093/6731
UL http://hdl.handle.net/11093/6731
LA eng
NO IEEE Transactions on Machine Learning in Communications and Networking, 1: 78-89 (2023)
NO United Kingdom (U.K.)-Australia Space Bridge, | Ref. Grant P4-22
DS Investigo
RD 08-sep-2024