Deep Learning for Rain Fade Prediction in Satellite Communications
Author:
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9681915/9681831/09682090.pdf?arnumber=9682090
Reference22 articles.
1. Prediction method of fade dynamics on earth-space paths;union;Recommendation ITU-R,2005
2. Time series prediction of rain attenuation from rain rate measurement using synthetic storm technique for a tropical location
3. Rain Fade Slope Model in Satellite Path Based on Data Measured in Heavy Rain Zone
4. Attenuation prediction for fade mitigation using neural network with in situ learning algorithm
5. Rain Attenuation Along Terrestrial Millimeter Wave Links: A New Prediction Method Based on Supervised Machine Learning
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1. 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
2. Excess Attenuation Detection in Satellite Communication Channel Measurements with Deep Learning Architectures;2023 17th European Conference on Antennas and Propagation (EuCAP);2023-03-26
3. Experimental Modeling of Short-Term Effects of Rain on Satellite Link Using Machine Learning;IEEE Transactions on Instrumentation and Measurement;2023
4. A Review on Rain Signal Attenuation Modeling, Analysis and Validation Techniques: Advances, Challenges and Future Direction;Sustainability;2022-09-19
5. High-Capacity, Weather-Resilient MIMO Feeder Links in Multibeam Satellite Systems;IEEE Transactions on Communications;2022-07
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