Machine learning for handover decision with mobile edge computing in 6G mobile network: a survey
The forthcoming Six-Generation (6G) cellular network promises to provide novel developments in network architecture, offering greater data rates and ultra-low latency, ensuring seamless and reliable connectivity with a high quality of service for a massive number of connected devices. Even though ef...
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| Format: | Article |
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Elsevier
2025-09-01
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| Series: | Engineering Science and Technology, an International Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098625001867 |
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| author | Soule Issa Loutfi Ibraheem Shayea Ufuk Tureli Ayman A. El-Saleh Waheeb Tashan Ramazan Caglar |
| author_facet | Soule Issa Loutfi Ibraheem Shayea Ufuk Tureli Ayman A. El-Saleh Waheeb Tashan Ramazan Caglar |
| author_sort | Soule Issa Loutfi |
| collection | DOAJ |
| description | The forthcoming Six-Generation (6G) cellular network promises to provide novel developments in network architecture, offering greater data rates and ultra-low latency, ensuring seamless and reliable connectivity with a high quality of service for a massive number of connected devices. Even though efficient handover decision-making is one of the critical challenges in 6G networks, especially with high mobility scenarios and complex characterization of future networks. The case becomes more critical with the implementation of Mobile Edge Computing (MEC), which will lead to making the handover decision process more challenging due to its characterization and high requirements. This paper presents a systematic review of the handover decision (HOD) with MEC in 6G networks, providing a deep understanding of the most standing challenges and solutions addressing mobility management issues in 6G mobile networks. Moreover, machine learning (ML) and deep learning (DL) technologies are the key promising solutions for intelligent HOD-making in 6G networks with MEC. Therefore, this research work also aims to give a main focus on studying and highlighting the advanced ML methods that can be used to enhance HOD-making in 6G cellular networks with MEC. Furthermore, a comprehensive review of HOD-based ML solutions is provided to enhance the Quality of Service (QoS) of user experience in Heterogeneous Networks (HetNet), instilling confidence in the paper’s findings. Besides, proposed solutions for HODs using ML models with next-generation network requirements and possible technologies are presented. We also describe research challenges and future directions for achieving this study. |
| format | Article |
| id | doaj-art-ea247b394f2547f9be451f545ab3b18c |
| institution | DOAJ |
| issn | 2215-0986 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj-art-ea247b394f2547f9be451f545ab3b18c2025-08-20T03:08:56ZengElsevierEngineering Science and Technology, an International Journal2215-09862025-09-016910213110.1016/j.jestch.2025.102131Machine learning for handover decision with mobile edge computing in 6G mobile network: a surveySoule Issa Loutfi0Ibraheem Shayea1Ufuk Tureli2Ayman A. El-Saleh3Waheeb Tashan4Ramazan Caglar5Electronics and Communication Engineering Department, Faculty of Electrical and Electronics Engineering, Yildiz Technical University, 34220 Istanbul, Turkey; Corresponding authors.Electronics and Communication Engineering Department, Faculty of Electrical and Electronics Engineering, Istanbul Technical University, 34467 Istanbul, Turkey; Corresponding authors.Electronics and Communication Engineering Department, Faculty of Electrical and Electronics Engineering, Yildiz Technical University, 34220 Istanbul, TurkeyDepartment of Electrical Engineering and Computer Science, College of Engineering, A'Sharqiyah University, 400 Ibra, Oman; Corresponding authors.Department of Electronics and Communication Engineering, Kocaeli University, 41001 Kocaeli, TurkeyDepartment of Electrical Engineering, Faculty of Electrical and Electronics Engineering, Istanbul Technical University (ITU), 34469 Istanbul, TurkeyThe forthcoming Six-Generation (6G) cellular network promises to provide novel developments in network architecture, offering greater data rates and ultra-low latency, ensuring seamless and reliable connectivity with a high quality of service for a massive number of connected devices. Even though efficient handover decision-making is one of the critical challenges in 6G networks, especially with high mobility scenarios and complex characterization of future networks. The case becomes more critical with the implementation of Mobile Edge Computing (MEC), which will lead to making the handover decision process more challenging due to its characterization and high requirements. This paper presents a systematic review of the handover decision (HOD) with MEC in 6G networks, providing a deep understanding of the most standing challenges and solutions addressing mobility management issues in 6G mobile networks. Moreover, machine learning (ML) and deep learning (DL) technologies are the key promising solutions for intelligent HOD-making in 6G networks with MEC. Therefore, this research work also aims to give a main focus on studying and highlighting the advanced ML methods that can be used to enhance HOD-making in 6G cellular networks with MEC. Furthermore, a comprehensive review of HOD-based ML solutions is provided to enhance the Quality of Service (QoS) of user experience in Heterogeneous Networks (HetNet), instilling confidence in the paper’s findings. Besides, proposed solutions for HODs using ML models with next-generation network requirements and possible technologies are presented. We also describe research challenges and future directions for achieving this study.http://www.sciencedirect.com/science/article/pii/S2215098625001867Mobility managementHandover decisionMobile edge computingMachine learningHeterogeneous networks6G network |
| spellingShingle | Soule Issa Loutfi Ibraheem Shayea Ufuk Tureli Ayman A. El-Saleh Waheeb Tashan Ramazan Caglar Machine learning for handover decision with mobile edge computing in 6G mobile network: a survey Engineering Science and Technology, an International Journal Mobility management Handover decision Mobile edge computing Machine learning Heterogeneous networks 6G network |
| title | Machine learning for handover decision with mobile edge computing in 6G mobile network: a survey |
| title_full | Machine learning for handover decision with mobile edge computing in 6G mobile network: a survey |
| title_fullStr | Machine learning for handover decision with mobile edge computing in 6G mobile network: a survey |
| title_full_unstemmed | Machine learning for handover decision with mobile edge computing in 6G mobile network: a survey |
| title_short | Machine learning for handover decision with mobile edge computing in 6G mobile network: a survey |
| title_sort | machine learning for handover decision with mobile edge computing in 6g mobile network a survey |
| topic | Mobility management Handover decision Mobile edge computing Machine learning Heterogeneous networks 6G network |
| url | http://www.sciencedirect.com/science/article/pii/S2215098625001867 |
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