###### Abstract  
Electromagnetic radiation measurement and management emerge as crucial factors in the economical deployment of fifth-generation (5G) infrastructure, as the new 5G network emerges as a network of services. By installing many base stations in strategic locations that operate in the millimeter-wave range, 5G services are able to meet serious demands for bandwidth. To evaluate the ground-plane radiation level of electromagnetics close to 5G base stations, we propose a unique machine-learning-based approach. Because a machine learning algorithm is trained by utilizing data obtained from numerous 5G base stations, it exhibits the capability to estimate the strength of the electric field effectively at every point of arbitrary radiation, while the base station generates a network and serves various numbers of 5G terminals running in different modes of service. The model requires different numbers of inputs, including the antenna's transmit power, antenna gain, terminal service modes, number of 5G terminals, distance between the 5G terminals and 5G base station, and environmental complexity. Based on experimental data, the estimation method is both feasible and effective; the machine learning model's mean absolute percentage error is about 5.89%. The degree of correctness shows how dependable the developed technique is. In addition, the developed approach is less expensive when compared to measurements taken on-site. The results of the estimates can be used to save test costs and offer useful guidelines for choosing the best location, which will make 5G base station electromagnetic radiation management or radio wave coverage optimization easier.  
Article  
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Electromagnetic radiation is a highly relevant and compelling application, particularly given the rapid expansion of 5G networks and the rising public concern about their potential health and environmental effects. Utilizing a machine learning-based approach to estimate ground-level electromagnetic radiation (EMR) near 5G base stations provides a powerful solution that combines accuracy with scalability. Consequently, this approach provides a guide for the adoption and deployment of 5G technology in Africa, with a specific interest in Nigeria.  
Electromagnetic radiation measurement and management emerge as crucial factors in the economical deployment of fifth-generation (5G) infrastructure, as the new 5G network emerges as a network of services. By installing many base stations in strategic locations that operate in the millimeter-wave range, 5G services are able to meet serious demands for bandwidth. To evaluate the ground-plane radiation level of electromagnetics close to 5G base stations, we propose a unique machine-learning-based approach. Because a machine learning algorithm is trained by utilizing data obtained from numerous 5G base stations, it exhibits the capability to estimate the strength of the electric field effectively at every point of arbitrary radiation, while the base station generates a network and serves various numbers of 5G terminals running in different modes of service. The model requires different numbers of inputs, including the antenna's transmit power, antenna gain, terminal service modes, number of 5G terminals, distance between the 5G terminals and 5G base station, and environmental complexity. Based on experimental data, the estimation method is both feasible and effective; the machine learning model's mean absolute percentage error is about 5.89%. The degree of correctness shows how dependable the developed technique is. In addition, the developed approach is less expensive when compared to measurements taken on-site. The results of the estimates can be used to save test costs and offer useful guidelines for choosing the best location, which will make 5G base station electromagnetic radiation management or radio wave coverage optimization easier.  
Article