Machine learning-based technique for gain and resonance prediction of mid band 5G Yagi antenna

In this study, we present our findings from investigating the use of a machine learning (ML) technique to improve the performance of Quasi-Yagi–Uda antennas operating in the n78 band for 5G applications. This research study investigates several techniques, such as simulation, measurement, and an RLC...

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Detalles Bibliográficos
Autores principales: Haque, Md. Ashraful, Rahman, Md Afzalur, Al-Bawri, Samir Salem, Yusoff, Zubaida, Sharker, Adiba Haque, Abdulkawi, Wazie M., Saha, Dipon, Paul, Liton Chandra, Zakariya, M. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10400634/
https://www.ncbi.nlm.nih.gov/pubmed/37537201
http://dx.doi.org/10.1038/s41598-023-39730-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10400634/
https://www.ncbi.nlm.nih.gov/pubmed/37537201
http://dx.doi.org/10.1038/s41598-023-39730-1

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