Architecture selection for 5G-radio access network using type-2 neutrosophic numbers based decision making model

Abdullah Hussein Abdullah Al-Amoodi

Architecture selection for 5G-radio access network using type-2 neutrosophic numbers based decision making model

AVAILABLE Article 402 Views

Back to search

Scan QR Code to share or view item link:

Digital Assets & Download Files

Guest Preview PDF

Public preview document

Preview PDF

General Information

Title

Architecture selection for 5G-radio access network using type-2 neutrosophic numbers based decision making model

Type

Article

Main Author

Abdullah Hussein Abdullah Al-Amoodi

Subject

T Technology (General)

ISSN

0957-4174

Language Code

eng

Bibliographic Details

Place of Production

Tanjong Malim

Publisher

Fakulti Komputeran dan META-Teknologi

Year of publication

2024

Notes

Expert Systems with Applications

Degree Type

Others

Corporate Name

Perpustakaan Tuanku Bainun

HTTP Link

Access Web Resource

Abstract

Fifth-generation (5G) technology provides new possibilities for a variety of applications, but it also comes with challenges influenced by distinct aspects, such as the size of organizations that use such technology. Therefore, it is important to understand which architecture of 5G-radio access networks (RANs) is best for a given purpose; this requires an evaluation platform for assessment. This paper tackles this problem by presenting a novel multi-criteria decision-making (MCDM) solution based on a new integrated fuzzy set. The proposed integrated approach, which is based on a Type-2 neutrosophic fuzzy environment, is developed to address the application challenges of 5G-RANs architecture evaluation, as also to face the MCDM theoretical challenge represented by ambiguities and inconsistencies among decision makers within the decision making context of the presented case study. Many MCDM techniques for weighting and selection were presented from the literature, yet many of them still suffer from inconsistencies and uncertainty. Therefore, the chosen methods in this research are unique in a way that previous issues are addressed, making them suitable for integration with Type-2 neutrosophic fuzzy environment, and therefore creating a more robust decision platform for the presented challenge in this research, as a theoretical contribution. First, a new “Type-2 Neutrosophic Fuzzy-Weighted Zero-Inconsistency” (T2NN-FWZIC) technique is formulated for weighting the evaluation criteria of RAN architectures. Second, another new method, namely, “Type-2 Neutrosophic Fuzzy Decision by Opinion Score Method” (T2NN-FDOSM), was formulated to select the optimal RAN architecture using the obtained weights. The weighting results by T2NN-FWZIC for the (n = 25) evaluation criteria revealed that (C21 latency and C22 reliability) as the most important criteria, with 0.06 value for each as opposed to (C15 Data Processing) as the lowest weighted criteria with 0.0186 value. As for T2NN-FDOSM, a total of four 5G-RAN architectures were evaluated, including “virtualized cloud RAN” coming as the optimal one, followed by “fog RAN,” “cloud RAN,” and finally “heterogeneous cloud RAN.” The results were confirmed by carrying out a sensitivity analysis. The outcome of this study can be used to assist future 5G-RAN developments according to business needs and to establish an assessment platform for 5G technology in different domains and applications.

References

M. Abdel-Basset et al. (2019) An approach of TOPSIS technique for developing supplier selection with group decision making under type-2 neutrosophic number Applied Soft Computing.

J.C.R. Alcantud et al. (2020) Aggregation of infinite chains of intuitionistic fuzzy sets and their application to choices with temporal intuitionistic fuzzy information Information Sciences.

L. Al-Tarawneh (2023) An optimal algorithm for energy harvesting in optical networks Optical Fiber Technology

A.A. Barakabitze et al. (2020) 5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges Computer Networks.

N. Basil et al. (2023) Evaluation of autonomous underwater vehicle motion trajectory optimization algorithms Knowledge-Based Systems.

J. Cheng et al. (2018) Industrial IoT in 5G environment towards smart manufacturing Journal of Industrial Information Integration.

M. Deveci et al. (2021) Type-2 neutrosophic number based multi-attributive border approximation area comparison (MABAC) approach for offshore wind farm site selection in USA Engineering Applications of Artificial Intelligence.

M. Deveci et al. (2022) An analytics approach to decision alternative prioritization for zero-emission zone logistics Journal of Business Research.

M. Deveci et al. (2021) Remanufacturing facility location for automotive Lithium-ion batteries: An integrated neutrosophic decision-making model Journal of Cleaner Production.

W. Ejaz et al. (2020) A comprehensive survey on resource allocation for CRAN in 5G and beyond networks Journal of Network and Computer Applications.

T. Garai et al. (2022) Multi-criteria decision making of water resource management problem (in Agriculture field, Purulia district) based on possibility measures under generalized single valued non-linear bipolar neutrosophic environment Expert Systems with Applications.

I. Gokasar et al. (2022) CO2 Emission based prioritization of bridge maintenance projects using neutrosophic fuzzy sets based decision making approach Research in Transportation Economics.

Ö.F. Görçün (2022) A novel integrated MCDM framework based on Type-2 neutrosophic fuzzy sets (T2NN) for the selection of proper Second-Hand chemical tankers Transportation Research Part E: Logistics and Transportation Review.

Ö.F. Görçün et al. (2023) The selection of appropriate Ro-Ro Vessel in the second-hand market using the WASPAS’Bonferroni approach in type 2 neutrosophic fuzzy environment Engineering Applications of Artificial Intelligence.

I. Kaya et al. (2019) A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making Energy Strategy Reviews.

M. Khaturia et al. (2021) 5G-Flow: A unified Multi-RAT RAN architecture for beyond 5G networks Computer Networks.

S.-S. Lin et al. (2022) Risk evaluation of excavation based on fuzzy decision-making model Automation in Construction.