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A New Prediction Method of Rain Attenuation Along Millimeter Wave Links Based on a Bivariate Model for the Effective Path Length and Weibull Distribution

By Spiros N. Livieratos, Zisis Ioannidis, Stylianos Savaidis, Stelios Mitilineos, and Nikolaos Stathopoulos
Progress In Electromagnetics Research C, Vol. 97, 29-41, 2019


Cellular technology is moving towards its 5th generation (5G) that will employ millimeter wave (mmWave) frequencies in the attempt to offer more spectrum and multi-Gigabit-per-second (Gbps) data rates to mobile devices.Various unfavorable propagation phenomena affect mmWave communications, rain attenuation being the most severe one. Various rain attenuation prediction models can be taken into account in the design of terrestrial links based either on cumbersome statistical regression, when sufficient local experimental data are available, or on analytical models where only local rain rate measurements are provided. In this paper, a new prediction method for the rain attenuation is proposed based on a bivariate model for the numerical estimation of the effective path length of a millimeter wave terrestrial link and on Weibull distribution forthe representation of the point rainfall rate statistics. To validate the proposed prediction method, the actual data taken into account are extracted from experiments included in the databank of ITU-R SG3.The numerical results obtained show a significant improvement of the prediction accuracy compared to existing prediction models.


Spiros N. Livieratos, Zisis Ioannidis, Stylianos Savaidis, Stelios Mitilineos, and Nikolaos Stathopoulos, "A New Prediction Method of Rain Attenuation Along Millimeter Wave Links Based on a Bivariate Model for the Effective Path Length and Weibull Distribution," Progress In Electromagnetics Research C, Vol. 97, 29-41, 2019.


    1. Rappaport, T. S., et al., "Millimeter wave mobile communications for 5G cellular: It will work!," IEEE Access, Vol. 1, 335-349, May 2013.

    2. Kibria, M. G., K. Nguyen, G. P. Villardi, O. Zhao, K. Ishizu, and F. Kojima, "Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks," IEEE Access, Vol. 6, 32328-32338, May 2018.

    3. Rangan, S., T. S. Rappaport, and E. Erkip, "Millimeter-wave cellular wireless networks: Potentials and challenges," Proceedings of the IEEE, Vol. 102, No. 3, 366-385, Mar. 2014.

    4. MacCartney, G. R. and T. S. Rappaport, "Rural macrocell path loss models for millimeter wave wireless communications," IEEE Journal on Selected Areas in Communications, Vol. 35, No. 7, 1663-1677, Jul. 2017.

    5. Thomas, T. A., et al., "A prediction study of path loss models from 2–73.5 GHz in an urban-macro environment," 2016 IEEE 83rd Vehicular Technology Conference (VTC 2016-Spring), 1-5, Nanjing, China, May 2016.

    6. Shrestha, S. and D. Y. Choi, "Rain attenuation statistics over millimeter wave bands in South Korea," Journal of Atmospheric and Solar-Terrestrial Physics, Vol. 152, 1-10, Jan. 2017.

    7. Lin, S. H., "A method for calculating rain attenuation distribution on microwave paths," Bell Syst. Tech. J., Vol. 54, 1051-1086, 1975.

    8. Morita, K. and I. Higuti, "Prediction methods for rain attenuation distributions of micro and millimetre waves," Rev. Elec. Comm. Labs, Vol. 24, No. 7-8, 651-668, 1977.

    9. Fedi, F., "Prediction of attenuation due to rainfall on terrestrial links," Radio Sci., Vol. 16, No. 5, 731-743, 1981.

    10. Moupfouma, F., "Improvement of rain attenuation method for terrestrial microwave links," IEEE Trans. Ant. and Prop., Vol. 32, 1368-1372, 1984.

    11. Livieratos, S. N., V. Katsabas, and J. D. Kanellopoulos, "A global method for the prediction of the slant path rain attenuation statistics," Journal of Electromagnetic Waves and Applications, Vol. 14, No. 5, 713-724, Jan. 2000.

    12. Moupfouma, F. and L. Martin, "Modeling of the rainfall rate cumulative distribution for the design of satellite and terrestrial communication systems ," International Journal of Satellite Communications, Vol. 13, 105-115, Mar./Apr. 1995.

    13. Crane, R. K., Electromagnetic Wave Propagation through Rain, John Wiley & Sons Series, UK, 1996.

    14. Freeman, R. L., Radio System Design for Telecommunication, 3rd Ed., A Wiley Inter-science Publication, Wiley, John Wiley & Sons Inc., San Francisco, United States, 2007.

    15. ITU-R Recommendation P.838–3, Specific Attenuation Model for Rain for Use in Prediction Methods, ITU, Geneva, Switzerland, 2005.

    16. ITU-R Recommendation P.530-16, Propagation Data and Prediction Methods Required for the Design of Terrestrial Line-of-Sight Systems, ITU, Geneva, Switzerland, 2015.

    17. ITU-R Recommendation P.837-7, Characteristics of Precipitation for Propagation Modelling, ITU, Geneva, Switzerland, 2017.

    18. Da Silva Mello, L. A. R., M. S. Pontes, R. M. D. Souza, and N. A. P. Garcia, "Prediction of rain attenuation in terrestrial links using full rainfall rate distribution," IEEE Electron. Lett., Vol. 43, 1442-1443, Dec. 2007.

    19. Moupfouma, F., "Electromagnetic waves attenuation due to rain: A prediction model for terrestrial or L.O.S SHF and EHF radio communication links," J. Infrared Millim. Terahertz Waves, Vol. 30, 622-632, Mar. 2009.

    20. Lin, S. H., "National long term rain statistics and empirical calculation of 11 GHz microwave rain attenuation," Bell Syst. Tech. J., Vol. 56, 1581-1604, Nov. 1977.

    21. Patki, N., R. Wedge, and K. Veeramachaneni, "The synthetic data vault," IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2016, 399-410, Montreal, Canada, Oct. 2016.

    22. ITU-R Recommendation P.311-13, Acquisition, Presentation and Analysis of Data in Studies of Tropospheric Propagation, ITU, Geneva, Switzerland, 2009.