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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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Published: |
Elsevier Ltd
2023
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| Online Access: | http://scholars.utp.edu.my/id/eprint/37366/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169906270&doi=10.1016%2fj.heliyon.2023.e19548&partnerID=40&md5=d659d9ed13c234b61aff5f6fc53eba63 |
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