A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

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dc.contributor.author Mollel, Michael S.
dc.contributor.author Abubakar, Attai Ibrahim
dc.contributor.author Ozturk, Metin
dc.contributor.author Kaijage, Shubi F.
dc.contributor.author Michael, Kisangiri
dc.contributor.author Hussain, Sajjad
dc.contributor.author Imran, Muhammad Ali
dc.contributor.author Abbasi, Qammer H.
dc.date.accessioned 2021-05-04T08:14:14Z
dc.date.available 2021-05-04T08:14:14Z
dc.date.issued 2021-03-19
dc.identifier.uri https://doi.org/10.1109/ACCESS.2021.3067503
dc.identifier.uri https://dspace.nm-aist.ac.tz/handle/20.500.12479/1165
dc.description This research article published by IEEE, 2021 en_US
dc.description.abstract Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The state-of-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject 5G mobile communication en_US
dc.subject Cellular networks en_US
dc.subject 6G mobile communication en_US
dc.subject Bandwidth en_US
dc.title A Survey of Machine Learning Applications to Handover Management in 5G and Beyond en_US
dc.type Article en_US

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