Development of an Improved Rain Attenuation Prediction Model for Wireless Communications Networks Operating AT 38GHz Millimeter Wave Frequency

Dapo J. Omotowa, Suleiman   Muhammad Sani

Abstract


This research focused on developing an improved rain attenuation prediction model for wireless communications at 38 GHz using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN). The study utilized rainfall data from 2011 to 2021 in the Zaria metropolis, employing the ITU-R P.838-3 model to calculate specific attenuation due to rain. Key input parameters included rain intensity, signal frequency, and polarization. MATLAB 2021a was used for calculations, converting specific attenuation into an equivalent path length. The improved model demonstrated greater accuracy than the ITU-R P.838-3 model, particularly in areas with high convective rainfall. The Mean Square Error (MSE) of 0.396085688 and Root Mean Square Error (RMSE) of 0.6293533 indicated high precision in predicting rain attenuation. This model is suitable for designing reliable wireless communication systems in the study area.


Full Text:

PDF

References


S. B. Matondo and P. A. Owolawi, "FSO

rain attenuation prediction using non-linear least

square regression," in 2019 International

Multidisciplinary Information Technology and

Engineering Conference (IMITEC), 2019, pp. 1-5:

IEEE.

I. Abdulwahab et al., "Solar Irradiance

Prediction for Zaria Town Using Different Machine

Learning Models," Pakistan Journal of Engineering

Technology, vol. 7, no. 2, pp. 66-71, 2024.

C. Capsoni, F. Fedi, and A. Paraboni, "A

comprehensive meteorologically-oriented

methodology for the prediction of wave

propagation parameters in telecommunication

applications beyond 10 GHz," NASA STI/Recon

Technical Report N, vol. 89, p. 17786, 1988.

K. Igwe, "Optimal rain attenuation

prediction models for Earth-Space communication

at Ku-band in North Central Nigeria," in

Proceedings of the 7th International

Conference on the Applications of Science

and Mathematics 2021: Sciemathic 2021,

, pp. 415-428: Springer.

F. Hossain, T. K. Geok, T. A. Rahman, M.

N. Hindia, K. Dimyati, and A. Abdaziz,

"Indoor millimeter-wave propagation

prediction by measurement and ray tracing

simulation at 38 GHz," Symmetry, vol. 10,

no. 10, p. 464, 2018.

A. Hafis, A. S. Adamu, Y. Jibril, and I.

Abdulwahab, "An Optimal Sizing of Small

Hydro/PV/Diesel Generator Hybrid System

for Sustainable Power Generation,"

Journal of Engineering Science

Technology Review, vol. 16, no. 6, 2023.

I. Abdulwahab, S. Faskari, T. Belgore, and

T. Babaita, "An improved hybrid micro-grid

load frequency control scheme for an

autonomous system," FUOYE Journal of

Engineering Technology, vol. 6, no. 4, pp.

-374, 2021.

M. Ahuna, T. Afullo, and A. Alonge,

"Rain attenuation prediction using

artificial neural network for dynamic

rain fade mitigation," SAIEE Africa

Research Journal, vol. 110, no. 1, pp.

-18, 2019.

F. Moupfouma, "Electromagnetic

waves attenuation due to rain: A

prediction model for terrestrial or LOS

SHF and EHF radio communication

links," Journal of Infrared, Millimeter,

Terahertz Waves, vol. 30, pp. 622-632, 2009.

K. K. Kolawole, E. Theophilus, and O.

Mayowa, "Development of rain

attenuation prediction in south west

Nigeria on terrestrial link using

adaptive artificial neural network."

M. A. Samad and D.-Y. Choi,

"Learning-assisted rain attenuation

prediction models," Applied Sciences,

vol. 10, no. 17, p. 6017, 2020.


Refbacks

  • There are currently no refbacks.