Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations
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 ran...
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MDPI AG
2025-06-01
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| author | Oluwole John Famoriji Thokozani Shongwe |
| author_facet | Oluwole John Famoriji Thokozani Shongwe |
| author_sort | Oluwole John Famoriji |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6194491589584f08ab032fcbfad3ecb9 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-6194491589584f08ab032fcbfad3ecb92025-08-20T02:35:59ZengMDPI AGApplied Sciences2076-34172025-06-011513730210.3390/app15137302Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base StationsOluwole John Famoriji0Thokozani Shongwe1Department of Electrical and Electronic Engineering Technology, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South AfricaDepartment of Electrical and Electronic Engineering Technology, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South AfricaElectromagnetic 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.https://www.mdpi.com/2076-3417/15/13/7302artificial intelligence5G networkEM wave propagationEM fieldwireless communication |
| spellingShingle | Oluwole John Famoriji Thokozani Shongwe Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations Applied Sciences artificial intelligence 5G network EM wave propagation EM field wireless communication |
| title | Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations |
| title_full | Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations |
| title_fullStr | Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations |
| title_full_unstemmed | Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations |
| title_short | Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations |
| title_sort | machine learning approach for ground level estimation of electromagnetic radiation in the near field of 5g base stations |
| topic | artificial intelligence 5G network EM wave propagation EM field wireless communication |
| url | https://www.mdpi.com/2076-3417/15/13/7302 |
| work_keys_str_mv | AT oluwolejohnfamoriji machinelearningapproachforgroundlevelestimationofelectromagneticradiationinthenearfieldof5gbasestations AT thokozanishongwe machinelearningapproachforgroundlevelestimationofelectromagneticradiationinthenearfieldof5gbasestations |