Optimized 5G-MMW Compact Yagi-Uda Antenna Based on Machine Learning Methodology

The fifth generation (5G) of the mobile communication should provide a faster latency rate, wider Bandwidth (BW), and higher Gain (G) in comparison with older systems, (e.g. fourth generation (4G)). For 5G applications, the millimeter wave (MMW) antennas seem to be a suitable choice due to their sma...

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Bibliographic Details
Main Authors: Jafarieh, Alireza, Nouri, Mahdi, Behroozi, Hamid
Format: Conference Proceeding
Language:English
Subjects:
5G communication
5G mobile communication
Directive antennas
Machine learning
Machine learning (ML)
microstrip antenna
Microstrip antennas
MMW
Simulation
surrogate-based optimization (SBO)
Task analysis
Yagi-Uda antenna
Yagi-Uda antennas
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Summary:The fifth generation (5G) of the mobile communication should provide a faster latency rate, wider Bandwidth (BW), and higher Gain (G) in comparison with older systems, (e.g. fourth generation (4G)). For 5G applications, the millimeter wave (MMW) antennas seem to be a suitable choice due to their small size. Owing to a large number of design parameters, designing an optimum antenna that can satisfy the 5G conditions is a very challenging task. In the meanwhile, using machine learning (ML) approaches to find the optimum design is an appropriate solution. Surrogate-based optimization (SBO) can handle the high computational cost of ML approaches, especially when the number of design parameters is large. The microstrip Yagi-Vda antennas play an important role in 5G communication systems due to their high BW and high G.
ISSN:2642-9527
DOI:10.1109/ICEE52715.2021.9544194