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V-Band LEO Satellite Channels
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  • Bassel Al Homssi ,
  • Chiu Chan ,
  • Ke (Desmond) Wang ,
  • Wayne Rowe ,
  • Ben Allen ,
  • Ben Moores ,
  • Lazlo Csurgai-Horvath ,
  • Fernando Fontan ,
  • sithamparanathan kandeepan ,
  • Akram Al-Hourani
Bassel Al Homssi
RMIT University

Corresponding Author:[email protected]

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Chiu Chan
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Ke (Desmond) Wang
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Wayne Rowe
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Ben Allen
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Ben Moores
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Lazlo Csurgai-Horvath
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Fernando Fontan
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sithamparanathan kandeepan
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Akram Al-Hourani
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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 presented in two folds; (i) a real-time forecasting method using model-based deep learning, intended for real-time operation of satellite terminals, and (ii) a statistical channel simulator that generates the path loss as a time-series random process, intended for system design and research. The provided approach capitalizes on real satellite measurements that are obtained from AlphaSat’s Q/V-band transmitter at different geographic latitudes, to model the radio channel. The results show that model-based deep learning forecasting can outperform conventional statistically derived prediction methods for varying rain and elevation angle profiles. Moreover, it can also provide more accuracy in long-term prediction in comparison to current state-of-the-art machine learning approaches for radio channel prediction. Results for the statistical channel simulator is shown to produce synthetic radio excess path loss values for varying satellite passes by capitalizing on empirical statistical models obtained from real measurements.
2023Published in IEEE Transactions on Machine Learning in Communications and Networking volume 1 on pages 78-89. 10.1109/TMLCN.2023.3286793