This study tackles rain attenuation in millimeter-wave (mm-wave) communication, a critical concern for the advancement of 5G wireless technology. It examines the variability of raindrop size distribution (DSD) and its impact on specific attenuation, with a focus on tropical environments such as Malaysia. Using the Joss-Waldvogel disdrometer, this study collected and analyzed extensive DSD data over three years, revealing that the highest DSD concentrations do not necessarily result in the greatest specific attenuation. This study adopted a machine learning approach, specifically supervised learning with linear regression, to enhance the accuracy of attenuation prediction models. A new set of coefficients for the power-law model of specific attenuation was derived and benchmarked against the ITU-R P.838-3 standard and similar studies in comparable climates. The findings emphasize the importance of developing region-specific models that consider local meteorological variations, potentially offering significant improvements to the reliability and design of mm-wave communication systems in the future.
Collaboration network of the authors
9 authors ·
15 co-authorship links
· 6 across different organisations
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University of Technology Malaysia
Tun Hussein Onn University of Malaysia
National Technical University of Athens
Affiliation unknown
Different organisations
Same organisation
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