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

• Main Authors: Haque, M.A., Saha, D., Al-Bawri, S.S., Paul, L.C., Rahman, M.A., Alshanketi, F., Alhazmi, A., Rambe, A.H., Zakariya, M.A., Ba Hashwan, S.S.
• Format: Article
• Published: Elsevier Ltd 2023
• 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
• Institution: Universiti Teknologi Petronas

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...