Machine learning-based technique for resonance and directivity prediction of UMTS LTE band quasi Yagi antenna

In this study, we have presented our findings on the deployment of a machine learning (ML) technique to enhance the performance of LTE applications employing quasi-Yagi-Uda antennas at 2100 MHz UMTS band. A number of techniques, including simulation, measurement, and a model of an RLC-equivalent cir...

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Detalles Bibliográficos
Autores principales: Haque, Md. Ashraful, Saha, Dipon, Al-Bawri, Samir Salem, Paul, Liton Chandra, Rahman, Md Afzalur, Alshanketi, Faisal, Alhazmi, Ali, Rambe, Ali Hanafiah, Zakariya, M.A., Ba Hashwan, Saeed S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558792/
https://www.ncbi.nlm.nih.gov/pubmed/37809766
http://dx.doi.org/10.1016/j.heliyon.2023.e19548

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558792/
https://www.ncbi.nlm.nih.gov/pubmed/37809766
http://dx.doi.org/10.1016/j.heliyon.2023.e19548

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