Deep learning enabled beam tracking for non-line of sight millimeter wave communications

Wang, R., Valente Klaine, P. , Onireti, O. , Sun, Y. , Imran, M. and Zhang, L. (2021) Deep learning enabled beam tracking for non-line of sight millimeter wave communications. IEEE Open Journal of the Communications Society, 2, pp. 1710-1720. (doi: 10.1109/OJCOMS.2021.3096118)

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To solve the complex beam alignment issue in non-line-of-sight (NLOS) millimeter wave communications, this paper presents a deep neural network (DNN) based procedure to predict the angle of arrival (AOA) and angle of departure (AOD) both in terms of azimuth and elevation, i.e., AAOA/AAOD and EAOA/EAOD. In order to evaluate the performance of the proposed procedure under practical assumptions, we employ a trajectory prediction method by considering dynamic window approach (DWA) to estimate the location information of the user equipment (UE), which is utilized as the input parameter of the trained DNN to generate the prediction of AAOA/AAOD and EAOA/EAOD. The robustness of the prediction procedure is analyzed in the presence of prediction errors, which proves that the proposed DNN is a promising tool to predict AOA and AOD in NLOS scenarios based on the estimated UE location. Simulation results shows that the prediction errors of the AOA and AOD can be maintained within an acceptable range of ±2∘.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Zhang, Professor Lei and Onireti, Oluwakayode and Valente Klaine, Mr Paulo and Sun, Dr Yao and Wang, Ruiyu and Imran, Professor Muhammad
Authors: Wang, R., Valente Klaine, P., Onireti, O., Sun, Y., Imran, M., and Zhang, L.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Open Journal of the Communications Society
ISSN (Online):2644-125X
Published Online:09 July 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in IEEE Open Journal of the Communications Society 2: 1710-1720
Publisher Policy:Reproduced under a Creative Commons License

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