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.

Highlights

  • Wireless communication networks have been witnessing an unprecedented demand in terms of bandwidth and number of connections in this so-called information age—in particular the age of big data1 where data is regarded as new oil [1]

  • 3Cell and base stations (BSs) are interchangeably used throughout this paper unless stated otherwise. In this survey, we focus on the application of machine learning (ML) algorithms to HO management in cellular networking with special attention aimed at 5G and B5G networks in order to keep the discussion timely as 5G has already become a reality, and visionary works about 6G has started to appear in the literature [25], [26], [26]

  • We reviewed the state-of-the-art on ML-based HO management in cellular networks by taking into account the data used during the implementation of such algorithms; a top-level taxonomy on the source of data generation is provided with two primary classes: visual data and wireless network data aided HO optimization

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Summary

INTRODUCTION

Wireless communication networks have been witnessing an unprecedented demand in terms of bandwidth and number of connections in this so-called information age—in particular the age of big data where data is regarded as new oil [1]. This makes the HO management concept even more crucial as THz band includes much higher frequencies that mm-wave band, and the smaller footprint (and more frequent HOs) will be much more significant To this end, we reviewed the state-of-the-art on ML-based HO management in cellular networks (with a special focus to 5G and B5G) by taking into account the data used during the implementation of such algorithms; a top-level taxonomy on the source of data generation is provided with two primary classes: visual data and wireless network data aided HO optimization. With this section, we try to canalize the research focus to the identified topics to open a road for practical solutions

RELATED WORKS
CHARACTERISTICS OF 5G AND BEYOND
MACHINE LEARNING FOR HO MANAGEMENT
CHALLENGES AND FUTURE RESEARCH DIRECTIONS
LOAD BALANCING
CONCLUSION
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