6G technology based advanced virtual multi-purpose embedding algorithm to solve far-reaching network effects

Saad, Aldosary; Al-Ma’aitah, Mohammed; Alwadain, Ayed

doi:10.1016/j.comcom.2020.07.025

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…The 6G security communities also combine AI to empower health solutions for efficient, costeffective, and technology-based strategies [48][49][50]. For instance, the combination of 6G and AI has the potential to manage dementia successfully (a general term for the impaired ability to remember, think, or make decisions that interfere with doing everyday activities) in patients until a cure becomes available [51][52][53].…”

Section: Figure 20: Recent Studies In 6gmentioning

confidence: 99%

A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

Salih,

Rahman,

Firdaus

et al. 2024

CMES

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The race to develop the next generation of wireless networks, known as Sixth Generation (6G) wireless, which will be operational in 2030, has already begun. To realize its full potential over the next decade, 6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ones. However, the studies about 6G are mainly limited and scattered, whereas no bibliometric study covers the 6G field. Thus, this study aims to review, examine, and summarize existing studies and research activities in 6G. This study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and 2021. Then, we applied the bibliometric analysis methods by including (1) document type, (2) subject area, (3) author, and (4) country of publication. The study's results reflect the research 6G community's trends, highlight important research challenges, and elucidate potential directions for future research in this interesting area.

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Section: Figure 20: Recent Studies In 6gmentioning

confidence: 99%

A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

Salih,

Rahman,

Firdaus

et al. 2024

CMES

View full text Add to dashboard Cite

show abstract

Multivariate regressive deep stochastic artificial learning for energy and cost efficient 6G communication

Ramesh

Ramachandran

Patan

et al. 2021

Sustainable Computing: Informatics and Systems

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A Normalized Slicing-assigned Virtualization Method for 6G-based Wireless Communication Systems

Alharbi

Aljebreen

Tolba

et al. 2022

ACM Trans. Multimedia Comput. Commun. Appl.

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The next generation of wireless communication systems will rely on advantageous 6G features and sophisticated edge Internet of Things (IoT) technology to provide continuous service delegation and resource allocation. Network slicing and virtualization are common in these scenarios to meet user demands and application services. This article introduces a Normalized Slicing-assigned Virtualization Method (NSVM) for satisfying the 6G features in future generation systems. The proposed method relies on available resource roots and time intervals for replications. Based on the availability and Accessibility, the resource virtualization and network slicing processes are forwarded. The proposed method exploits federated learning for determining availability and accessibility models in detecting slicing, virtualization, or both the requirements throughout the resource sharing process. This improves the resource sharing rate, with less latency and high processing despite the user and application demands. The learning models are trained to balance replication and network slicing for confining complexity across different resources. The proposed method's performance is validated using the above metrics for varying users and intervals.

show abstract

6G technology based advanced virtual multi-purpose embedding algorithm to solve far-reaching network effects

Cited by 5 publications

References 32 publications

A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

Multivariate regressive deep stochastic artificial learning for energy and cost efficient 6G communication

A Normalized Slicing-assigned Virtualization Method for 6G-based Wireless Communication Systems

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