Intelligent beam blockage prediction for seamless connectivity in vision-aided next-generation wireless networks

Al-Quraan, M., Khan, A., Mohjazi, L. , Centeno, A. , Zoha, A. and Imran, M. A. (2022) Intelligent beam blockage prediction for seamless connectivity in vision-aided next-generation wireless networks. IEEE Transactions on Network and Service Management, (doi: 10.1109/TNSM.2022.3216556) (Early Online Publication)

Abstract

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users’ location and speed. In addition, this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE).

• Item Type: Articles
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zoha, Dr Ahmed and Alquraan, Mohammad Mahmoud Younes and Khan, Ahsan Raza and Imran, Professor Muhammad and Centeno, Dr Anthony and Mohjazi, Dr Lina
• Authors: Al-Quraan, M., Khan, A., Mohjazi, L., Centeno, A., Zoha, A., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: IEEE Transactions on Network and Service Management
• Publisher: IEEE
• ISSN: 1932-4537
• ISSN (Online): 1932-4537
• Published Online: 21 October 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Transactions on Network and Service Management 2022
• Publisher Policy: Reproduced in accordance with the publisher copyright policy