Skip to main content
Log in

A Journey from Traditional to Machine Learnig of Radio Wave Attenuation Caused by Rain: A State of Art

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Given the massive rapid growth for capacity in wireless data telecommunications every year, wireless carriers must be planned for a thousand-fold increase in mobile traffic in 2020. It compels scientists to explore for new wireless airwaves that can accommodate large data rates. Next-generation technologies must address issues such as increased spectrum allocation in millimetre wave frequency bands, the installation of directional antennas beam forming antennas, better battery life, high data transmission rates with reduced outage probability, lower capital costs, and increased capacity for multiple simultaneous users. There are two types of telecommunication links: terrestrial and satellite. Terrestrial links are also known as radio relay links. This connects the troposphere, which is located between the earth’s surface and the high atmosphere, to the propagation of radio waves. Gases, water vapour, and other weather phenomena such as rain, storms, snow, and hail all interfere with higher frequency radio signals in this region. Due to these difficulties, energy is absorbed and diffused, resulting in signal attenuation. Another form of obstruction generated by radio waves on terrestrial paths is buildings, trees, lampposts, grill, and other urban features. Under these conditions, reflection, diffraction, refraction, scattering, depolarization, and other phenomena are studied. This study discusses the effects of rain on satellite and terrestrial communications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Data Availability

All data used in this research are available. We can provide whenever ask to do so.

Code Availability

(Software application or custom code). All codes used in this research are available. We can provide whenever ask to do so.

References

  1. Houze, R. A., Jr. (1993). Cloud dynamics (p. 573). Academic. Google Scholar.

    Google Scholar 

  2. Sauvageot, H., Castanet, L., Lemorton, J. (2003) HYCELL: A new hybrid model of the rain horizontal distribution for propagation studies: Modelling of the rain cell. Radio Science 38(3).

  3. Gunn, R., & Kinzer, G. D. (1949). The terminal velocity of fall for water droplets in stagnant air. Journal of Meteorology, 6(4), 243–248.

    Article  Google Scholar 

  4. Spilhaus, A. F. (1948). Raindrop size, shape and falling speed. Journal of Meteorology, 5(3), 108–110.

    Article  Google Scholar 

  5. Brussaard, G. (1974). Rain-induced crosspolarisation and raindrop canting. Electronics Letters, 10(20), 411–412.

    Article  Google Scholar 

  6. Kathiravelu, G., Lucke, T., & Nichols, P. (2016). Rain drop measurement techniques: A review. Water, 8(1), 29.

    Article  Google Scholar 

  7. Williams, C. R., & Gage, K. S. (2009). Raindrop size distribution variability estimated using ensemble statistics. Annales Geophysicae: Atmospheres, Hydrospheres and Space Sciences, 27(2), 555–557.

    Article  Google Scholar 

  8. Marshall, J. S., & Palmer, W. M. K. (1948). The distribution of raindrops with size. Journal of Meteorology, 5, 165–166.

    Article  Google Scholar 

  9. Illingworth, A. J., & Blackman, T. M. (2002). The need to represent raindrop size spectra as normalized gamma distributions for the interpretation of polarization radar observations. Journal of Applied Meteorology, 41, 286–297.

    Article  Google Scholar 

  10. Zhang, G., Vivekanandan, J., Brandes, E., Meneghini, R., & Kozu, T. (2003). The shape-slope relation in observed gamma raindrop size distributions: Statistical error or useful information. Journal of the Seismological Society of Japan, 20, 1106–1119.

    Google Scholar 

  11. Feingold, G., & Levin, Z. (1986). The lognormal fit to raindrop spectra from frontal convective clouds in Israel. Journal of Applied Meteorology, 25, 1346–1364.

    Article  Google Scholar 

  12. Baltas, E. A., & Mimikou, M. A. (2002). The use of the Joss-type disdrometer for the derivation of ZR relationships. In Proceedings of ERAD. Vol. 291. No. 294.

  13. Panchal, P., & Joshi, R. (2016). Performance analysis and simulation of rain attenuation models at 12–40 GHz band for an earth space path over indian cities. Procedia Computer Science, 79, 801–808.

    Article  Google Scholar 

  14. Eport on Modelling of Attenuatio, (2008). Wide range propagation model, science and technologies facilities council.

  15. Paulson, K. S., & Gibbins, C. J. (2000). Rain models for the prediction of fade durations at millimetre wavelengths. IEE Proceedings-Microwaves, Antennas and Propagation, 147(6), 431–436.

    Article  Google Scholar 

  16. Singh, H., Kumar, V., Saxena, K., Boncho, B., & Prasad, R. (2020). Proposed model for radio wave attenuation due to rain (RWAR). Wireless Personal Communications, 115(1), 791–807.

    Article  Google Scholar 

  17. Al-Saman, A. M., Cheffena, M., Mohamed, M., Azmi, M. H., & Ai, Y. (2020). Statistical analysis of rain at millimeter waves in tropical area. IEEE Access, 8, 51044–51061.

    Article  Google Scholar 

  18. Kalaivaanan, P. M., Sali, A., Abdullah, R. S. A. R., Yaakob, S., Singh, M. J., & Al-Saegh, A. M. (2020). Evaluation of Ka-band rain attenuation for satellite communication in tropical regions through a measurement of multiple antenna sizes. IEEE Access, 8, 18007–18018.

    Article  Google Scholar 

  19. Al-Saman, A., Mohamed, M., Ai, Y., Cheffena, M., Azmi, M. H., & Rahman, T. A. (2020). Rain attenuation measurements and analysis at 73 GHz E-band link in tropical region. IEEE Communications Letters, 24(7), 1368–1372.

    Article  Google Scholar 

  20. Budalal, A. A. H., Islam, R. M., Abdullah, K., & Rahman, T. A. (2020). Modification of distance factor in rain attenuation prediction for short range millimetre-wave links. IEEE Antennas and Wireless propagation Letters, 19(6), 1027–1031.

    Article  Google Scholar 

  21. Argota, J. A. R., & Anitzine, I. F. (2020). Attenuation time series synthesizer for dynamic prediction in millimeter wave frequency bands. Synthesis, 5, 7.

    Google Scholar 

  22. Singh, H., Kumar, V., Saxena, K., & Bonev, B. (2020). An intelligent model for prediction of attenuation caused by rain based on machine learning techniques. In 2020 International Conference on Contemporary Computing and Applications (IC3A). IEEE. pp. 92–97.

  23. Rashid, M., & Din, J. (2020). Effects of reduction factor on rain attenuation predictions over millimeter-wave links for 5G applications. Bulletin of Electrical Engineering and Informatics, 9(5), 1907–1915.

    Article  Google Scholar 

  24. Usha, A., & Karunakar, G. (2021). Preliminary analysis of rain attenuation and frequency scaling method for satellite communication. Indian Journal of Physics, 95(6), 1033–1040.

    Article  Google Scholar 

  25. Han, C., Huo, J., Gao, Q., Su, G., & Wang, H. (2020). Rainfall monitoring based on next-generation millimeter-wave backhaul technologies in a dense urban environment. Remote Sensing, 12(6), 1045.

    Article  Google Scholar 

  26. Tijani, A., Yusuf, S. D., Ibrahim, U., Loko, A. Z., & Mundi, A. A. (2020). Evaluation of real time rain-rate on downlink satellite signal attenuation in Abuja, Nigeria. EDUCATUM Journal of Science, Mathematics and Technology, 7(1), 29–38.

    Article  Google Scholar 

  27. Cuervo, F., Martín-Polegre, A., Las-Heras, F., Vanhoenacker-Janvier, D., Flávio, J., & Schmidt, M. (2020). Preparation of a CubeSat LEO radio wave propagation campaign at Q and W bands. International Journal of Satellite Communications and Networking, 40(1), 39–47.

    Article  Google Scholar 

  28. Singh, H., Saxena, K., Kumar, V., Bonev, B., & Prasad, R. (2020). An empirical model for prediction of environmental attenuation of millimeter waves. Wireless Personal Communications, 115(1), 809–826.

    Article  Google Scholar 

  29. Chebil, J., Islam, M. R., Zyoud, A. H., Habaebi, M. H., & Dao, H. (2020). Rain fade slope model for terrestrial microwave links. International Journal of Microwave and Wireless Technologies, 12(5), 372–379.

    Article  Google Scholar 

  30. Elmutasim, I. E., & Mohd, I. I. (2019). Examination rain and fog attenuation for path loss prediction in millimeter wave range. In Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 Springer, pp. 935–946.

  31. Ananya, S. T., Islam, M. S., Mahmud, M. A. R., Podder, P. K., & Uddin, M. J. Atmospheric propagation impairment effects for wireless communications.

  32. Mishra, K. V., MR, B. S., & Ottersten, B. (2020). Deep Rainrate estimation from highly attenuated downlink signals of ground-based communications satellite terminals. In ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 9021–9025.

  33. Samat, F., Singh, M. S. J., & Sountharapandian, T. (2020). Rain attenuation prediction model assessment on 3-year Ka-band signal of MEASAT-5 at tropical region using 7.3-m antenna. MAPAN, 35(2), 201–212.

    Article  Google Scholar 

  34. Sanyaolu, M. E., Dairo, O. F., Willoughby, A. A., & Kolawole, L. B. (2020). 1-Minute rain rate distribution for communication link design based on ground and satellite measurements in west AFRICA. Telecommunications and Radio Engineering, 79(6), 533–543.

    Article  Google Scholar 

  35. Mishra, M. K., Renju, R., Mathew, N., Suresh Raju, C., Sujimol, M. R., & Shahana, K. (2020). Characterization of GSAT-14 satellite Ka-band microwave signal attenuation due to precipitation over a tropical coastal station in the southern peninsular region of the indian subcontinent. Radio Science, 55(2), e2019RS006910.

    Article  Google Scholar 

  36. Acharya, R. (2020). A simple real-time frequency scaling technique for rain attenuation and its performance. International Journal of Satellite Communications and Networking, 38(4), 329–340.

    Article  Google Scholar 

  37. Sanyaolu, M. E., Dairo, O. F., Willoughby, A. A., & Kolawole, L. B. (2020). Estimation of rain fade durations on communication links at Ka band in equatorial and tropical regions. Telecommunications and Radio Engineering, 79(2), 129–141.

    Article  Google Scholar 

  38. Jeon, J., Muhammad, K., Cho, J., Xu, G., Na, I., & Zhang, J. (2020). Design considerations for terahertz wireless communication systems. In 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW). IEEE. pp. 1–5.

  39. Kelmendi, A., Švigelj, A., & Hrovat, A. (2020). Statistical analysis of satellite communication experimental time diversity in Slovenia. In 2020 14th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1–5.

  40. Alencar, G. A. (2004). Low statistical data processing for applications in Earth-space paths rain attenuation prediction by an artificial neural network. In 2004 Asia-Pacific Radio Science Conference, 2004. Proceedings, IEEE, pp. 344–346.

  41. Thiennviboon, P., & Wisutimateekorn, S. (2019). Rain attenuation prediction modeling for Earth-space links using artificial neural networks. In 2019 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), IEEE, pp. 29–32.

  42. Mpoporo, L. J., Owolawi, P. A., & Ayo, A. O. (2019, November). Utilization of artificial neural networks for estimation of slant-path rain attenuation. In 2019 International Multidisciplinary Information Technology and Engineering Conference (IMITEC) (pp. 1–7). IEEE.

  43. Livieratos, S. N., & Cottis, P. G. (2019). Rain attenuation along terrestrial millimeter wave links: A new prediction method based on supervised machine learning. IEEE Access, 7, 138745–138756.

    Article  Google Scholar 

  44. Li, T., Suzuki, K., Nishioka, J., Mizukoshi, Y., & Hasegawa, Y. (2015). Short-term rainfall attenuation prediction for wireless communication. In 2015 IEEE 16th International Conference on Communication Technology (ICCT), IEEE, pp. 615–619.

  45. Ahuna, M. N., Afullo, T. J., & Alonge, A. A. (2019). Rain attenuation prediction using artificial neural network for dynamic rain fade mitigation. SAIEE Africa Research Journal, 110(1), 11–18.

    Article  Google Scholar 

  46. Amarjit, & Gangwar, R. P. S. (2008). Implementation of artificial neural network for prediction of rain attenuation in microwave and millimeter wave frequencies. IETE Journal of Research, 54(5), 346–352.

    Article  Google Scholar 

  47. Zhao, L., Zhao, L., Song, Q., Zhao, C., & Li, B. (2014). Rain attenuation prediction models of 60 GHz based on neural network and least squares-support vector machine. In The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems, pp. 413–421. Springer.

  48. Roy, B., Acharya, R., & Sivaraman, M. R. (2012). Attenuation prediction for fade mitigation using neural network within situ learning algorithm. Advances in Space Research, 49(2), 336–350.

    Article  Google Scholar 

Download references

Funding

This work was not supported by the financial Grant from any organization.

Author information

Authors and Affiliations

Authors

Contributions

(Optional: please review the submission guidelines from the journal whether statements are mandatory): Did work for state of Art for rain attenuation. Add reviews for machine and AI based propagation (Attenuation due to rain).

Corresponding author

Correspondence to Vivek Kumar.

Ethics declarations

Conflict of interest

(Include appropriate disclosures): There is no any conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, V., Singh, H., Saxena, K. et al. A Journey from Traditional to Machine Learnig of Radio Wave Attenuation Caused by Rain: A State of Art. Wireless Pers Commun 125, 3261–3285 (2022). https://doi.org/10.1007/s11277-022-09709-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09709-8

Keywords

Navigation