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Millimeter Wave Phased Array Antenna Synthesis Using a Machine Learning Technique for Different 5G Applications

Danesh, Shadi, Araghi, Ali, Khalily, Mohsen, Xiao, Pei and Tafazolli, Rahim (2020) Millimeter Wave Phased Array Antenna Synthesis Using a Machine Learning Technique for Different 5G Applications In: 2020 IEEE International Symposium on Networks, Computers and Communications (ISNCC'20), 20-22 Oct 2020, Montreal, Canada.

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Abstract

A machine learning (ML) technique has been used to synthesis a linear millimetre wave (mmWave) phased array antenna by considering the phase-only synthesis approach. For the first time, gradient boosting tree (GBT) is applied to estimate the phase values of a 16-element array antenna to generate different far-field radiation patterns. GBT predicts phases while the amplitude values have been equally set to generate different beam patterns for various 5G mmWave transmission scenarios such as multicast, unicast, broadcast and unmanned aerial vehicle (UAV) applications.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Danesh, Shadi
Araghi, Alia.araghi@surrey.ac.uk
Khalily, Mohsenm.khalily@surrey.ac.uk
Xiao, PeiP.Xiao@surrey.ac.uk
Tafazolli, RahimR.Tafazolli@surrey.ac.uk
Date : 20 October 2020
Copyright Disclaimer : © 2020 IEEE
Uncontrolled Keywords : 5G; Phased array antenna; Gradient boosting tree (GBT); Machine learning (ML); Millimetre wave (mmWave); Array factor; Phase-only synthesis
Related URLs :
Depositing User : Clive Harris
Date Deposited : 22 May 2020 15:01
Last Modified : 20 Oct 2020 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/856976

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