A survey on femtocell handover management in dense heterogeneous 5G networks

Ahmad, Rami; Sundararajan, Elankovan; Khalifeh, Ala’

doi:10.1007/s11235-020-00718-1

Cited by 38 publications

(21 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other authors have taken advantage of SDN technology [150] by performing the training operations in the controller and then sending their outputs (training process) to the WSN nodes via the SDN-Switches [124,126]. It is considered a successful and good idea, but it also needs to develop special protocols to deal with these particles.…”

Section: Location Of the ML Training Processmentioning

confidence: 99%

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Ahmad

Wazirali

Abu-Ain

2022

Sensors

Self Cite

View full text Add to dashboard Cite

Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.

show abstract

Section: Location Of the ML Training Processmentioning

confidence: 99%

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Ahmad

Wazirali

Abu-Ain

2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…As such, since ML was not the target, the source of data generation (visual data and wireless network data) were also not discussed. The authors in [37] analysed femtocell HOs in HetNets and provided a detailed background on LTE HO procedure with a particular interest to femtocell HOs. After identifying some challenges with the HO decision process in two-tier networks, an inclusive review was conducted on the existing HO decision techniques.…”

Section: B Related Workmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…For recent references where the importance of the account of existence of handover users is stressed see, e.g. [9]- [13].…”

Section: A Related Workmentioning

confidence: 99%

Mathematical Model of Operation of a Cell of a Mobile Communication Network With Adaptive Modulation Schemes and Handover of Mobile Users

Kim

Dudin

et al. 2021

IEEE Access

View full text Add to dashboard Cite

A model of a cell of a communication network, divided into zones, with the dependence of the users' service time on the zone, in which they are located, is considered. The arrival flow of users is defined by a marked Markovian process. The number of users that can receive service in a cell simultaneously is finite. If the number of users reached the limit, then new users are lost, except the users that come to the cell already during the service (handover users). For the short-term storage of such users, there is a buffer of a finite capacity. Cell operation is modeled by a multi-server queueing system. The behavior of this system is described by a multi-dimensional Markov chain. The algorithms for calculating the stationary distribution of this chain and the main characteristics of the quality of service have been developed.

show abstract

A survey on femtocell handover management in dense heterogeneous 5G networks

Cited by 38 publications

References 79 publications

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

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

Mathematical Model of Operation of a Cell of a Mobile Communication Network With Adaptive Modulation Schemes and Handover of Mobile Users

Contact Info

Product

Resources

About