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Rain Attenuation Modelling Based on Symbolic Regression and Differential Evolution for 5G mmWave Wireless Communication Networks.

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Title: Rain Attenuation Modelling Based on Symbolic Regression and Differential Evolution for 5G mmWave Wireless Communication Networks.
Authors: Matondo, Sandra Bazebo1 bazebosm@gmail.com, Owolawi, Pius Adewale1
Source: Progress in Electromagnetics Research B. 2025, Vol. 111, p45-58. 14p.
Subjects: Radio transmitters & transmission, Differential evolution, Telecommunication systems, Wireless communications, Machine learning
Abstract: The microphysical structure of rain has a significant impact on the quality of radio signal transmission in the upcoming deployment of 5G millimetre-wave wireless communications in South Africa. To address this, mitigation techniques that integrate rain attenuation prediction models into network management systems are essential. This study uses a machine learning technique, symbolic regression coupled with differential evolution, to predict the rain attenuation in urban and rural 5G scenarios. Symbolic regression derives the mathematical models characterizing attenuation, while differential evolution optimizes the model coefficients. The models' accuracies are validated through predictive performance metrics, including Mean Absolute Error (MAE) and Mean Squared Error (MSE). The urban model showed excellent accuracy, and the rural model improved significantly after optimization. The interpretability of the models provides valuable insights into rain-induced attenuation and supports better design and optimization of 5G mmWave communication systems. [ABSTRACT FROM AUTHOR]
Copyright of Progress in Electromagnetics Research B is the property of Electromagnetics Academy and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Progress+in+Electromagnetics+Research+B%22">Progress in Electromagnetics Research B</searchLink>. 2025, Vol. 111, p45-58. 14p.
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  Data: The microphysical structure of rain has a significant impact on the quality of radio signal transmission in the upcoming deployment of 5G millimetre-wave wireless communications in South Africa. To address this, mitigation techniques that integrate rain attenuation prediction models into network management systems are essential. This study uses a machine learning technique, symbolic regression coupled with differential evolution, to predict the rain attenuation in urban and rural 5G scenarios. Symbolic regression derives the mathematical models characterizing attenuation, while differential evolution optimizes the model coefficients. The models' accuracies are validated through predictive performance metrics, including Mean Absolute Error (MAE) and Mean Squared Error (MSE). The urban model showed excellent accuracy, and the rural model improved significantly after optimization. The interpretability of the models provides valuable insights into rain-induced attenuation and supports better design and optimization of 5G mmWave communication systems. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Progress in Electromagnetics Research B is the property of Electromagnetics Academy and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.2528/PIERB24120204
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 45
    Subjects:
      – SubjectFull: Radio transmitters & transmission
        Type: general
      – SubjectFull: Differential evolution
        Type: general
      – SubjectFull: Telecommunication systems
        Type: general
      – SubjectFull: Wireless communications
        Type: general
      – SubjectFull: Machine learning
        Type: general
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      – TitleFull: Rain Attenuation Modelling Based on Symbolic Regression and Differential Evolution for 5G mmWave Wireless Communication Networks.
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            NameFull: Matondo, Sandra Bazebo
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              Text: 2025
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              Y: 2025
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              Value: 111
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            – TitleFull: Progress in Electromagnetics Research B
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