DocumentCode
357803
Title
Using artificial neural network approach to predict rain attenuation on Earth-space path
Author
Hongwei Yang ; Chen He ; Wentao Song ; Hongwen Zhu
Author_Institution
Dept. of Electron. Eng., Shanghai Jiaotong Univ., China
Volume
2
fYear
2000
fDate
16-21 July 2000
Firstpage
1058
Abstract
A rain attenuation model based on artificial neural network is proposed in this paper without making many assumptions as traditional methods and therefore improves the prediction accuracy. Based on an analysis of various factors affecting rain attenuation, a rain attenuation model with artificial neural network is developed after training and verifying many different neural network topologies. The prediction results of the proposed model is also compared with that of the CCIR model. The results show that applying the artificial neural network to predict rain attenuation of a high frequency wave is a good approach and decreases the mean prediction error by 0.59 dB and the RMS error by 0.69 dB. The paper shows that this model is a new and effective way to predict rain attenuation with an artificial neural network.
Keywords
HF radio propagation; backpropagation; electromagnetic wave absorption; neural nets; rain; space communication links; telecommunication computing; tropospheric electromagnetic wave propagation; ANN; CCIR model; Earth-space path; RMS error; artificial neural network; backpropagation neural network; high frequency wave; mean prediction error reduction; neural network topologies; prediction accuracy; radiowave propagation; rain attenuation model; rain attenuation prediction; training; training data; Accuracy; Artificial neural networks; Atmospheric modeling; Attenuation measurement; Communication systems; Frequency; Helium; Meteorology; Predictive models; Rain;
fLanguage
English
Publisher
ieee
Conference_Titel
Antennas and Propagation Society International Symposium, 2000. IEEE
Conference_Location
Salt Lake City, UT, USA
Print_ISBN
0-7803-6369-8
Type
conf
DOI
10.1109/APS.2000.875404
Filename
875404
Link To Document