Deep neural network for localization of mobile users using raytracing

Kaur, J. , Popoola, O. R. , Imran, M. A. , Abbasi, Q. H. and Abbas, H. T. (2022) Deep neural network for localization of mobile users using raytracing. In: 2022 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), Denver, CO, USA, 10-15 Jul 2022, pp. 76-77. ISBN 9781946815163 (doi: 10.23919/USNC-URSI52669.2022.9887432)

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As the world evolves towards faster data transmission, there is an ever-increasing demand for better user localization which will find application in transport, medicine, and robotics. In this study, we present an accurate localization algorithm for mobile users using deep neural network with Bayesian optimization and a communication channel operating at 3.75GHz frequency. We design a deep neural network model which facilitates and speeds up the localization process. The deep neural network (DNN) is utilized in this study to locate moving user equipment (UEs) with randomly assigned velocities. Using preliminary computer simulations, we present a method for training a neural network that extracts channel parameters (features) that are used to estimate location. Our method produces localization accuracy for line of sight (LOS) users, less than 1 m error, and the accuracy can further be improved by implementing higher rate of data sets.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Popoola, Dr Olaoluwa and Imran, Professor Muhammad and Abbas, Dr Hasan and Abbasi, Professor Qammer and Kaur, Jaspreet
Authors: Kaur, J., Popoola, O. R., Imran, M. A., Abbasi, Q. H., and Abbas, H. T.
College/School:College of Science and Engineering
College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Published Online:16 September 2022
Copyright Holders:Copyright © 2022 IEEE
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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