Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels-Reference-Cited by-同舟云学术

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

Published:2023 Issue: Volume:1 Page:78-89
ISSN:2831-316X
Container-title:IEEE Transactions on Machine Learning in Communications and Networking
language:
Short-container-title:Trans. Mach. Learn. Comm. Netw.

Author:

Homssi Bassel Al¹^ORCID,Chan Chiu C.¹,Wang Ke¹^ORCID,Rowe Wayne¹^ORCID,Allen Ben²,Moores Ben²,Csurgai-Horváth László³^ORCID,Fontán Fernando Pérez⁴^ORCID,Kandeepan Sithamparanathan¹,Al-Hourani Akram¹^ORCID

Affiliation:

1. Department of Electrical and Electronic Engineering, RMIT University, Melbourne, VIC, Australia

2. OneWeb, London, U.K

3. Department of Broadband Infocommunications and Electromagnetic Theory, Budapest University of Technology and Economics, Budapest, Hungary

4. Telecommunications Engineering School, The University of Vigo, Vigo, Spain

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9882533/9895461/10153617.pdf?arnumber=10153617

Reference45 articles.

1. Fade Slope Analysis of Alphasat Satellite Measurements at Ka and Q Bands

2. Long-term statistics of tropospheric attenuation from the Ka/U band ITALSAT satellite experiment in the United Kingdom

3. Short-Term Prediction of the Attenuation in a Commercial Microwave Link Using LSTM-based RNN

4. A Survey of Rain Fade Models for Earth–Space Telecommunication Links—Taxonomy, Methods, and Comparative Study

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