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

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	20888708
DOI:	10.11591/ijece.v16i3.pp1236-1248