Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

Shayea, Ibraheem; Ergen, Mustafa; Azmi, Marwan Hadri; Aldırmaz-Çolak, Sultan; Nordin, Rosdiadee; Daradkeh, Yousef Ibrahim

doi:10.1109/access.2020.3023802

Cited by 120 publications

(84 citation statements)

References 119 publications

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“…Dual connectivity means that the UEs can establish connection to two different cells at the same time [96]. Usually, in dual connectivity, UEs either connect to BSs of different sizes (macro cell and SC) or two different RATs simultaneously (e.g.…”

Section: Dual Connectivitymentioning

confidence: 99%

“…With the introduction of mm-wave, the use of dual connectivity could lead to an increase in HO probability, thereby causing additional problems with mobility management, including an increase in signaling overhead, synchronization complexity between RATs for multi-RAT connectivity, simultaneous utilization of resources in multiple BSs, and reduction in battery lifespan. The increase in signaling overhead is due to flow control between the RATs [96], [97], and these issues could be addressed using intelligent approaches.…”

Section: Dual Connectivitymentioning

confidence: 99%

“…Improved solutions for mobility and KPI's prediction for the proactive HO scheme. [36], [96], [97], [99],…”

Section: Machine Learning For Ho Managementmentioning

confidence: 99%

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

et al. 2021

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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 stateof-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. INDEX TERMS Handover, machine learning, mobility management, fifth generation.

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Section: Dual Connectivitymentioning

confidence: 99%

Section: Dual Connectivitymentioning

confidence: 99%

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

et al. 2021

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“…Besides, it supports dual connectivity, which means that the UE can be connected to more than one BS. However, when it comes to HO in dual connectivity, the individual connections perform hard HO, and new HO scenarios emerge, which lead to more HO complications in mm-wave communication [15].…”

Section: Ho Management In 5g Networkmentioning

confidence: 99%

Deep Reinforcement Learning based Handover Management for Millimeter Wave Communication

Mollel¹,

Kaijage²,

Kisangiri³

2021

IJACSA

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The Millimeter Wave (mm-wave) band has a broadspectrum capable of transmitting multi-gigabit per-second daterate. However, the band suffers seriously from obstruction and high path loss, resulting in line-of-sight (LOS) and non-line-ofsight (NLOS) transmissions. All these lead to significant fluctuation in the signal received at the user end. Signal fluctuations present an unprecedented challenge in implementing the fifth generation (5G) use-cases of the mm-wave spectrum. It also increases the user's chances of changing the serving Base Station (BS) in the process, commonly known as Handover (HO). HO events become frequent for an ultra-dense dense network scenario, and HO management becomes increasingly challenging as the number of BS increases. HOs reduce network throughput, and hence the significance of mm-wave to 5G wireless system is diminished without adequate HO control. In this study, we propose a model for HO control based on the offline reinforcement learning (RL) algorithm that autonomously and smartly optimizes HO decisions taking into account prolonged user connectivity and throughput. We conclude by presenting the proposed model's performance and comparing it with the state-of-art model, rate based HO scheme. The results reveal that the proposed model decreases excess HO by 70%, thus achieving a higher throughput relative to the rates based HO scheme.

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“…The demands for higher bandwidth [1,2], link reliability and high data rate applications have been significantly growing in the past few years. New techniques are therefore required to meet these demands and solve the challenges that may arise [1,3,4]. The rapid and expanding demands for multimedia and bandwidth services have shifted research attention to the Passive Optical Network (PON) [5,6].…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancement of SCM/WDM-RoF-XGPON System for Bidirectional Transmission With Square Root Module

et al. 2021

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The 10-Gigabit Passive Optical Network (GPON), also known as (XGPON), is a key technology for the next generation of optical fibre communication systems which can improve reliability and data rate. However, the increment in the transmission distance and data rate will lead to increased dispersion. This paper proposes a model based on the Sub-Carrier Multiplexing/Wavelength Division Multiplexing-Radio over Fibre-10-Gigabit Passive Optical Network system (SCM/WDM-RoF-XGPON) for Radio Frequency (RF) using a conventional Optical Network Unit (ONU) with the Square Root Module (SRM) on the side of the receiver. It is connected to a central office Optical Line Terminal (OLT) via an access network for multi-channel optical fibre transfers with a distance of 80 km and at 10 Gbps data rate. The Optisystem 15 simulation software is used to evaluate the proposed system based on the Bit Error Rate (BER), Quality factor (Q-factor) and eye diagram. The proposed system of bidirectional fibre links uses 1270 nm and 1577 nm wavelengths for uplink and downlink transmissions, respectively. The performance is further enhanced through the use of SRM to compensate for the square law characteristics, making it suitable for providing broadband services. Due to the utilisation of SRM in the architecture, the reported results reveal a double enhancement in successful transition performance at an optical fibre length of 80 km distance. The BER displayed significant improvement (less than 1.00E-09) with SRM at 80 km; however, without SRM and at 50 km, the BER is less than 1.00E-09. Investigations have presented an enhancement in the Q-factor's effectiveness with the use of SRM, which helps to increase the XGPON length. When the length of the fibre is 80 km, the Q-factor is 6 with SRM, while it is 3.7 without SRM.

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Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

Cited by 120 publications

References 119 publications

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

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

Deep Reinforcement Learning based Handover Management for Millimeter Wave Communication

Performance Enhancement of SCM/WDM-RoF-XGPON System for Bidirectional Transmission With Square Root Module

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