Are mobility management solutions ready for 5G and beyond?

Jain, Akshay; López-Aguilera, Elena; Demirkol, Ilker

doi:10.1016/j.comcom.2020.07.016

Cited by 35 publications

(24 citation statements)

References 67 publications

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“…The authors in [39] produced an extensive survey on mobility management by questioning the readiness of the state-of-the-art solutions for the next-generations of cellular networks, namely 5G and B5G. First, the requirements of the next generations of cellular networks in terms of mobility management were first identified, followed by an introduction of their own qualitative performance metrics for the existing mobility management solutions.…”

Section: B Related Workmentioning

confidence: 99%

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%

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

et al. 2021

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“…Enabling users to move to different geographic locations within a mobile network while continuously operating across the full range of service requirements is and will be a key challenge today. It means meeting the criteria of the required performance, reliability, flexibility, and scalability of traf-fic for a constantly growing number of end users [43]. In addition to such technological solutions as network slicing and edge computing, it requires the use of new mobility management solutions.…”

Section: G Verticals a Backgroundmentioning

confidence: 99%

Verticals in 5G MEC-Use Cases and Security Challenges

et al. 2021

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5G is the fifth-generation cellular network satisfying the requirements IMT-2020 (International Mobile Telecommunications-2020) of the International Telecommunication Union. Mobile network operators started using it worldwide in 2019. Generally, 5G achieves exceptionally high values of performance parameters of access and transmission. The application of edge servers facilitated the implementation of such requirements of 5G, which resulted in 5G MEC (Multi-access Edge Computing) technology. Moreover, to optimize services for specific business applications, the concept of 5G vertical industries has been proposed. In this paper, we study how the application of the MEC technology affects the functioning of 5G MEC-based services. We consider twelve representative vertical industries of 5G MEC by presenting their essential characteristics, threats, vulnerabilities, and known attacks. Next, we analyze their functional properties, give efficiency patterns and identify the effect of applying the MEC technology in 5G on the resultant network's quality parameters to identify the expected security requirements. Finally, we identify the impact of classified threats on the 5G empowered vertical industries and identify the most sensitive cases to focus on their protection against network attacks in the first place.

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“…Such deployment strategies can significantly improve the perceived data rates and connectivity for end-users. However, they also increase network management's complexity, especially in terms of mobility management [21]. While users in indoor spaces can expect the presence of access points and small cells, the dense deployment of small cells does not happen at a fast enough pace to provide demanding services for mobile users [22].…”

Section: A Machine Learning Based Network Optimizationmentioning

confidence: 99%

Predictive UAV Base Station Deployment and Service Offloading With Distributed Edge Learning

Zhao

Pacheco

Santos

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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In modern networks, edge computing will be responsible for processing and learning from the critical networkand user-generated data, such as wireless link usage, mobility information, application requests, and many others. The presence of Artificial Intelligence-based (AI) applications at the edge of the network will enable the network to predict necessary user behavior and its impact on network infrastructure, such as base station overloading. One of the main strategies for offloading users and base stations is to deploy UAV base stations, or flying base stations, which can dynamically provide service and connectivity. In this article, we introduce a framework for distributed learning over Multi-access Edge Computing (MEC), which manages data applications in a fully distributed setting across edge servers, thus reducing the cost of collecting user information in a centralized server. We couple the proposed distributed learning with a novel similarity metric for user trajectories, which can aggregate neural network models with similar costs as other model aggregation techniques. However, the aggregation technique can achieve much higher accuracy. Furthermore, we apply the proposed distributed learning scheme to manage and deploy flying base stations to areas that experience high demand or poor user connectivity, thus optimizing connectivity in terms of user satisfaction, delay, and network throughput.

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Are mobility management solutions ready for 5G and beyond?

Cited by 35 publications

References 67 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

Verticals in 5G MEC-Use Cases and Security Challenges

Predictive UAV Base Station Deployment and Service Offloading With Distributed Edge Learning

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