Federated Learning for Reliable mmWave Systems: Vision-Aided Dynamic Blockages Prediction

Al-Quraan, M., Centeno, A. , Zoha, A. , Imran, M. A. and Mohjazi, L. (2023) Federated Learning for Reliable mmWave Systems: Vision-Aided Dynamic Blockages Prediction. In: IEEE Wireless Communications and Networking Conference (WCNC2023), Glasgow, Scotland, UK, 26-29 March 2023, ISBN 9781665491228 (doi: 10.1109/WCNC55385.2023.10118675)

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Abstract

Line of sight (LoS) links that use high frequencies are sensitive to blockages, making it challenging to scale future ultra-dense networks (UDN) that capitalise on millimetre wave (mmWave) and potentially terahertz (THz) networks. This paper embraces two novelties; Firstly, it combines machine learning (ML) and computer vision (CV) to enhance the reliability and latency of next-generation wireless networks through proactive identification of blockage scenarios and triggering proactive handover (PHO). Secondly, this study adopts federated learning (FL) to perform decentralised model training so that data privacy is protected, and channel resources are conserved. Our vision-aided PHO framework localises users using object detection and localisation (ODL) algorithm that feeds a multiple-output neural network (NN) model to predict possible blockages. This involves analysing images captured from the video cameras co-located with the base stations (BSs) in conjunction with wireless parameters to predict future blockages and subsequently trigger PHO. Simulation results show that our approach performs remarkably well in highly dynamic multi-user environments where vehicles move at different speeds, and achieves 93.6% successful PHO. Furthermore, the proposed framework outperforms the reactive-HO methods by a factor of 3.3 in terms of latency while maintaining a high quality of experience (QoE) for the users.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zoha, Dr Ahmed and Centeno, Dr Anthony and Imran, Professor Muhammad and Alquraan, Mohammad Mahmoud Younes and Mohjazi, Dr Lina
Authors: Al-Quraan, M., Centeno, A., Zoha, A., Imran, M. A., and Mohjazi, L.
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
ISSN:1558-2612
ISBN:9781665491228
Copyright Holders:Copyright © 2023, IEEE
First Published:First published in 2023 IEEE Wireless Communications and Networking Conference (WCNC)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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