Method of Rain Attenuation Prediction Based on Long–Short Term Memory Network-Reference-Cited by-同舟云学术

Method of Rain Attenuation Prediction Based on Long–Short Term Memory Network

Published:2022-02-03 Issue:4 Volume:54 Page:2959-2995
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Cornejo Andres^ORCID,Landeros-Ayala Salvador,Matias Jose M.,Ortiz-Gomez Flor,Martinez Ramon,Salas-Natera Miguel

Funder

Consejo Nacional de Ciencia y Tecnología

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-022-10749-1.pdf

Reference64 articles.

1. Maseng T, Bakken PM (1981) A stochastic dynamic model of rain attenuation. IEEE Trans Commun 29(5):660–669

2. Arapoglou PD, Liolis KP, Panagopoulos AD (2012) Railway satellite channel at Ku band and above: composite dynamic modeling for the design of fade mitigation techniques. Int J Satell Commun Netw 30(1):1–17

3. Karagiannis GA, Panagopoulos AD, Kanellopoulos JD (2012) Multidimensional rain attenuation stochastic dynamic modeling: application to earth-space diversity systems. IEEE Trans Antennas Propag 60(11):5400–5411

4. ITU-R Recommendation P.1853-1 (2012) Tropospheric attenuation time series. Technical report. International Telecommunication Union, Geneve, Switzerland

5. Dahman I, Arbogast P, Jeannin N, Benammar B (2018) Rain attenuation prediction model for satellite communications based on the Météo-France ensemble prediction system PEARP. Nat Hazards Earth Syst Sci 18(12):3327–3341

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Forecasting Convective Storms Trajectory and Intensity by Neural Networks;Forecasting;2024-05-19

2. Harnessing Supervised Learning for Adaptive Beamforming in Multibeam Satellite Systems;2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN);2024-05-05

3. Reconstructing and Nowcasting the Rainfall Field by a CML Network;Earth and Space Science;2023-09

4. In-Excess Attenuation Detection Using Satellite Link Channel Measurements at Ka- and Q-Bands With Deep-Learning Architectures;IEEE Transactions on Antennas and Propagation;2023-08

5. Site Diversity Switching Prediction AT Q Band Using Deep Learning Techniques in Satellite Communications;2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW);2023-06-04