Impact of Rain Attenuation on Short-Range Frequency Operations in 5G Networks

Attenuation caused by rain poses significant challenges for millimeter wave communication systems, especially in regions with heavy rainfall. Accurate attenuation prediction is required to set realistic performance expectations and ensure robust millimeter-wave communications links for 5G networks even in challenging environments. The aim of this work is to model and optimize rain attenuation in terrestrial 5G using evolutionary algorithms. The attenuation is modelled using symbolic regression that takes rain rate, path length, and frequencies between 24.25 GHz and 86 GHz as input. Next, differential evolution is used to determine the optimal parameters that best fit the model. The model is found to predict the attenuation with minimal errors. The proposed model’s performance is evaluated against the ITU-R, Crane, Da Silva and Melo models. The proposed model’s accuracy and performance are further validated based on the obtained MSE, MAE and R-squared values.