A Comprehensive Survey on Mobility Management in 5G Heterogeneous Networks: Architectures, Challenges and Solutions

Güreş, Emre; Shayea, Ibraheem; Alhammadi, Abdulraqeb; Ergen, Mustafa; Mohamad, Hafizal

doi:10.1109/access.2020.3030762

Cited by 87 publications

(55 citation statements)

References 86 publications

(87 reference statements)

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“…Another comprehensive survey was conducted in [36] for mobility management in 5G HetNets. In particular, the authors provided a detailed tutorial on radio resource control (RRC) states included in 5G NR along with the initial access and reachability.…”

Section: B Related Workmentioning

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: B Related Workmentioning

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%

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

et al. 2021

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“…For allocation of virtual resources, the mobility management factor of devices needs to be investigated with controlled handover to improve the spectrum utility in 5G networks [182]. The major challenge in D2D communication is its short-range requirement for a reliable link.…”

Section: ) Mobility Managementmentioning

confidence: 99%

Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges

et al. 2021

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With the tremendous demand for connectivity anywhere and anytime, existing network architectures should be modified. To cope with the challenges that arise due to the increasing flood of devices/users and a diverse range of application requirements, new technologies and concepts must be integrated to enable their benefits. Service providers and business companies are looking for new areas of research to enhance overall system performance. This paper gives a detailed survey about the recent 5G technologies, the solutions they provide, and the effect caused by their addition to current cellular networks. It is based on the three most important 5G concepts: Device to Device (D2D), Network Slicing (NS), and Mobile Edge Computing (MEC). This study proposes to design the future 5G networks by the integration of all three technologies. It is believed that spectrum efficiency, energy efficiency, and overall throughput will be greatly improved by using D2D. The system delay and computational load will be reduced as tasks will be handled by edge routers located at the base stations. Thus offloading the core network and the system capital expenses and operational expenses will be reduced significantly by slicing the network.

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“…SDN separates the control plane from the data plane, shifting the configuration decisions to a logically centralized controller with a global view of the network. The adoption of heterogeneous network (HetNet) architectures, where legacy 4G LTE macro-cells co-exist with new 5G SCs, is considered an important solution to meet increasing network capacity demands and provide high coverage in 5G networks [27]. This leverages the separation of the control and data planes, where the SCs could provide a high capacity data plane, while the control plane could be offloaded to the macro cells [28].…”

Section: Sdn-based Orchestration Of Steerable Backhaul Reconfigurationmentioning

confidence: 99%

A Randomized Greedy Heuristic for Steerable Wireless Backhaul Reconfiguration

Skorin-Kapov¹,

Santos

Ghazzai

et al. 2021

Electronics

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In this paper, we consider the reconfiguration of wireless backhaul networks with mechanically steerable antennas in the presence of changing traffic demands. Reconfiguration requires the scheduling and coordination of several operations, including antenna alignment and link establishment/removal, with minimal disruption to existing user traffic. Previously, we proposed a Mixed Integer Linear Program (MILP) to orchestrate such reconfiguration with minimal packet loss. While the MILP solves the problem optimally for a limited number of discrete reconfiguration time slots, it does not scale well. In this paper, we propose an iterative randomized greedy algorithm to obtain suboptimal solutions in reduced time. The algorithm schedules the reconfiguration of wireless links by ranking them according to a set of attributes with associated weights and selecting them according to a randomized greedy function. Results on six different network scenarios indicate that the proposed algorithm can achieve good quality solutions in significantly less time. Furthermore, by extending the reconfiguration time beyond the maximum number of time slots solvable by the MILP, the proposed heuristic can obtain superior solutions for some problem instances. The number of iterations of the algorithm can be tuned for its applicability in both offline and online planning scenarios.

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A Comprehensive Survey on Mobility Management in 5G Heterogeneous Networks: Architectures, Challenges and Solutions

Cited by 87 publications

References 86 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

Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges

A Randomized Greedy Heuristic for Steerable Wireless Backhaul Reconfiguration

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