Rain Attenuation Prediction Models of 60GHz Based on Neural Network and Least Squares-Support Vector Machine

  • Lina Zhao
  • Long Zhao
  • Qizhu Song
  • Chenglin Zhao
  • Bin Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)

Abstract

Although 60 GHz mmWave (millimeter-wave) has attractive features and promising applications, it is affected seriously by rain attenuation. Based on the neural networks and SVM (support vector machine), two novel rain attenuation prediction models for 60 GHz millimeter-wave are proposed in this paper. We respectively applied the BP (back-propagation) neural network and LS-SVM (least squares-support vector machine) to simulate the non-linear relationship between rainfall intensity and rain attenuation, then the two models are compared with general ITU-R model. Experimental results showed that both of the proposed prediction models are indeed superior to the existing ITU-R model for rain attenuation prediction in the sense of both accuracy and stability while LS-SVM is the most promising model for the prediction of rain attenuation.

Keywords

60 GHz mmWave Back-propagation neural network Least squares-support vector machine Rain attenuation 

Notes

Acknowledgements

This work was supported by Nation Natural Science Foundation of China(61271180).

References

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lina Zhao
    • 1
  • Long Zhao
    • 1
  • Qizhu Song
    • 1
  • Chenglin Zhao
    • 1
  • Bin Li
    • 1
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

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