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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...
| Autores principales: | , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2023
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| Materias: | |
| 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 |