Design and Optimization of a 5G Millimeter-Wave Antenna using Machine Learning and its Application in Beamforming of a Phased Array Antenna

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Jafarieh, Alireza | 2023

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 55932 (05)
University: Sharif University of Technology
Department: Electrical Engineering
Advisor(s): Farzaneh, Forouhar; Behroozi, Hamid
Abstract:
The fifth generation of mobile communication has a significant development in comparison to the last generations. However, the available frequency bands in sub-6 GHz frequencies are insufficient to fulfill this development. So the mm-waves bands play an essential role in 5G. The 5G applications demand antennas with low dimensions, wide beam, high gain, and wide bandwidth. In this thesis, a mm-wave antenna is designed and optimized by machine learning to meet 5G requirements. In addition, two arrays of this antenna are proposed with 4 and 8 elements. The analog beamforming is achieved by means of these arrays. The 4-element array is simulated in an IPhone package model. Finally, an antenna with wide beam, wide band, high gain, and small size is achieved within the Ka band
Keywords:
Fifth Generation of Mobile Networks ; Phased Array Antenna ; Beam Shaping ; Millimeter Wave ; Machine Learning ; Optimization ; Antenna Array

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