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Title: Channel Characterization for Aircraft Integrated Antennas via Machine Learning
Authors: Aksan, Yasin
Yüksel, Melda
Özbayoğlu, A. Murat
Keywords: Airframe shadowing
channel characterization
ensemble bagged trees
exponential Gaussian process regression
link budget analysis
Issue Date: 2021
Publisher: Ieee
Abstract: Wireless communication with unmanned aerial vehicles (UAVs) will become an integral part of future wireless communication systems. In UAVs real-time data transfer is very critical because UAVs need to be controlled from the ground and their data is also transferred in real time. However, aircraft antennas are prone to airframe shadowing. Aircraft surfaces, on which antennas are placed, obscure the main line-of-sight path. In addition, losses used in link budget analyses show great variability in real time. Therefore, observed end-to-end channels, including all impairments, are in general quite different from theoretical calculations in practice. In this work an end-to-end channel link budget, including all effects, is modelled by applying machine learning methods on measured data obtained during past flights. It is observed that ensemble bagged trees (EBT) and exponential Gaussian process regression (GPR) provide the two best results. Pre-processing data and utilizing raw data are also compared. EBT and exponential GPR can predict the amount of end-to-end losses with 7.49% and 8.07% sensitivity respectively using processed data. When raw data is used as input to the EBT method, it can predict the amount of end-to-end loss with a sensitivity of 2.79%, while a theoretical prediction error is 21.9%.
Description: IEEE Wireless Communications and Networking Conference (WCNC) -- MAR 29-APR 01, 2021 -- Nanjing, PEOPLES R CHINA
ISBN: 978-1-7281-9505-6
ISSN: 1525-3511
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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