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Machine learning-driven analysis of user bandwidth allocation and performance in 5G heterogeneous network: a survey.

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Title: Machine learning-driven analysis of user bandwidth allocation and performance in 5G heterogeneous network: a survey.
Authors: Leong, Pang Wai1 waileong.pang@taylors.edu.my, Chia, Raymond1 raymondchenjet.chia@sd.taylors.edu.my, King, Phang Swee1 sweeking.phang@taylors.edu.my, Hwang, Goh Hui1 jonathan.goh@taylors.edu.my, Yoong, Chan Kah2 kychan@mmu.edu.my, Chin, Chung Gwo2 gcchung@mmu.edu.my
Source: International Journal of Electrical & Computer Engineering (2088-8708). Jun2026, Vol. 16 Issue 3, p1236-1248. 13p.
Subjects: Bandwidth allocation, Machine learning, Resource allocation, Data analysis, 5G networks, Computer network management, Traffic estimation
Abstract: A key foundation of 5G heterogeneous networks (HetNets) is the use of network slicing, which divides bandwidth into multiple logical networks and accounts for each function's requirements. Currently, various machine learning (ML) models are being implemented into the network slicing algorithm to allocate bandwidth dynamically. The network slicing algorithm analyzes the traffic and allocates bandwidth based on the current services using a network-centric approach. However, limited work is found on further studying the impact of user-centric algorithms in bandwidth allocation. This paper presents the network slicing used in 5G and the limitations of these algorithms. A detailed review of user-centric bandwidth allocation algorithms is presented, along with a critical review of ML algorithms for traffic prediction and resource allocation decisions. Finally, the technology gaps and opportunities of the existing works are reported, and the direction for further research of ML in user-centric bandwidth allocation algorithms is tabulated. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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  Data: Machine learning-driven analysis of user bandwidth allocation and performance in 5G heterogeneous network: a survey.
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  Data: <searchLink fieldCode="AR" term="%22Leong%2C+Pang+Wai%22">Leong, Pang Wai</searchLink><relatesTo>1</relatesTo><i> waileong.pang@taylors.edu.my</i><br /><searchLink fieldCode="AR" term="%22Chia%2C+Raymond%22">Chia, Raymond</searchLink><relatesTo>1</relatesTo><i> raymondchenjet.chia@sd.taylors.edu.my</i><br /><searchLink fieldCode="AR" term="%22King%2C+Phang+Swee%22">King, Phang Swee</searchLink><relatesTo>1</relatesTo><i> sweeking.phang@taylors.edu.my</i><br /><searchLink fieldCode="AR" term="%22Hwang%2C+Goh+Hui%22">Hwang, Goh Hui</searchLink><relatesTo>1</relatesTo><i> jonathan.goh@taylors.edu.my</i><br /><searchLink fieldCode="AR" term="%22Yoong%2C+Chan+Kah%22">Yoong, Chan Kah</searchLink><relatesTo>2</relatesTo><i> kychan@mmu.edu.my</i><br /><searchLink fieldCode="AR" term="%22Chin%2C+Chung+Gwo%22">Chin, Chung Gwo</searchLink><relatesTo>2</relatesTo><i> gcchung@mmu.edu.my</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Jun2026, Vol. 16 Issue 3, p1236-1248. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Bandwidth+allocation%22">Bandwidth allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%225G+networks%22">5G networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+management%22">Computer network management</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+estimation%22">Traffic estimation</searchLink>
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  Data: A key foundation of 5G heterogeneous networks (HetNets) is the use of network slicing, which divides bandwidth into multiple logical networks and accounts for each function's requirements. Currently, various machine learning (ML) models are being implemented into the network slicing algorithm to allocate bandwidth dynamically. The network slicing algorithm analyzes the traffic and allocates bandwidth based on the current services using a network-centric approach. However, limited work is found on further studying the impact of user-centric algorithms in bandwidth allocation. This paper presents the network slicing used in 5G and the limitations of these algorithms. A detailed review of user-centric bandwidth allocation algorithms is presented, along with a critical review of ML algorithms for traffic prediction and resource allocation decisions. Finally, the technology gaps and opportunities of the existing works are reported, and the direction for further research of ML in user-centric bandwidth allocation algorithms is tabulated. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.11591/ijece.v16i3.pp1236-1248
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      – Code: eng
        Text: English
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        PageCount: 13
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    Subjects:
      – SubjectFull: Bandwidth allocation
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Resource allocation
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      – SubjectFull: Data analysis
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      – SubjectFull: 5G networks
        Type: general
      – SubjectFull: Computer network management
        Type: general
      – SubjectFull: Traffic estimation
        Type: general
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      – TitleFull: Machine learning-driven analysis of user bandwidth allocation and performance in 5G heterogeneous network: a survey.
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              Text: Jun2026
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              Y: 2026
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