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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| Format: | Article |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10153617/ |
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| author | 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 |
| author_facet | 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 |
| author_sort | Bassel Al Homssi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c6b7b5246d8a4c2ab8df8833a94e25a2 |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-c6b7b5246d8a4c2ab8df8833a94e25a22025-08-20T02:57:19ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2023-01-011788910.1109/TMLCN.2023.328679310153617Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite ChannelsBassel Al Homssi0https://orcid.org/0000-0002-7125-6738Chiu C. Chan1Ke Wang2https://orcid.org/0000-0001-5788-1396Wayne Rowe3https://orcid.org/0000-0002-0947-2341Ben Allen4Ben Moores5Laszlo Csurgai-Horvath6https://orcid.org/0000-0002-6460-3500Fernando Perez Fontan7https://orcid.org/0000-0002-0783-7562Sithamparanathan Kandeepan8Akram Al-Hourani9https://orcid.org/0000-0003-0652-8626Department of Electrical and Electronic Engineering, RMIT University, Melbourne, VIC, AustraliaDepartment of Electrical and Electronic Engineering, RMIT University, Melbourne, VIC, AustraliaDepartment of Electrical and Electronic Engineering, RMIT University, Melbourne, VIC, AustraliaDepartment of Electrical and Electronic Engineering, RMIT University, Melbourne, VIC, AustraliaOneWeb, London, U.KOneWeb, London, U.KDepartment of Broadband Infocommunications and Electromagnetic Theory, Budapest University of Technology and Economics, Budapest, HungaryTelecommunications Engineering School, The University of Vigo, Vigo, SpainDepartment of Electrical and Electronic Engineering, RMIT University, Melbourne, VIC, AustraliaDepartment of Electrical and Electronic Engineering, RMIT University, Melbourne, VIC, AustraliaAs 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.https://ieeexplore.ieee.org/document/10153617/LEO satellitestime-series predictionmachine learningartificial intelligenceLSTMrain fading |
| spellingShingle | 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 Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels IEEE Transactions on Machine Learning in Communications and Networking LEO satellites time-series prediction machine learning artificial intelligence LSTM rain fading |
| title | Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels |
| title_full | Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels |
| title_fullStr | Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels |
| title_full_unstemmed | Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels |
| title_short | Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels |
| title_sort | deep learning forecasting and statistical modeling for q v band leo satellite channels |
| topic | LEO satellites time-series prediction machine learning artificial intelligence LSTM rain fading |
| url | https://ieeexplore.ieee.org/document/10153617/ |
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