Intelligent handover decision scheme using double deep reinforcement learning

Mollel, M. S. , Abubakar, A. I. , Ozturk, M., Kaijage, S., Kisangiri, M., Zoha, A. , Imran, M. A. and Abbasi, Q. H. (2020) Intelligent handover decision scheme using double deep reinforcement learning. Physical Communication, 42, 101133. (doi: 10.1016/j.phycom.2020.101133)

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Handovers (HOs) have been envisioned to be more challenging in 5G networks due to the inclusion of millimetre wave (mm-wave) frequencies, resulting in more intense base station (BS) deployments. This, by its turn, increases the number of HOs taken due to smaller footprints of mm-wave BSs thereby making HO management a more crucial task as reduced quality of service (QoS) and quality of experience (QoE) along with higher signalling overhead are more likely with the growing number of HOs. In this paper, we propose an offline scheme based on double deep reinforcement learning (DDRL) to minimize the frequency of HOs in mm-wave networks, which subsequently mitigates the adverse QoS. Due to continuous and substantial state spaces arising from the inherent characteristics of the considered 5G environment, DDRL is preferred over conventional -learning algorithm. Furthermore, in order to alleviate the negative impacts of online learning policies in terms of computational costs, an offline learning framework is adopted in this study, a known trajectory is considered in a simulation environment while ray-tracing is used to estimate channel characteristics. The number of HO occurrence during the trajectory and the system throughput are taken as performance metrics. The results obtained reveal that the proposed method largely outperform conventional and other artificial intelligence (AI)-based models.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Zoha, Dr Ahmed and Abubakar, Mr Attai and Imran, Professor Muhammad and Öztürk, Metin and Mollel, Dr Michael and Abbasi, Dr Qammer
Creator Roles:
Mollel, M. S.Conceptualization, Methodology, Software, Data curation, Writing – original draft
Abubakar, A.Writing – review and editing
Ozturk, M.Visualization, Investigation
Zoha, A.Supervision
Abbasi, Q.Supervision
Imran, M.Supervision, Software, Validation
Authors: Mollel, M. S., Abubakar, A. I., Ozturk, M., Kaijage, S., Kisangiri, M., Zoha, A., Imran, M. A., and Abbasi, Q. H.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Physical Communication
ISSN (Online):1876-3219
Published Online:22 May 2020
Copyright Holders:Copyright © 2020 Elsevier B.V.
First Published:First published in Physical Communication 42: 101133
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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