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. |
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| 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] |
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| Database: | Engineering Source |
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