Handover parameter for self-optimisation in 6G mobile networks: A survey
One of the most crucial issues in mobile networks is ensuring reliable and stable connectivity during mobility. In recent years, numerous research has examined Fourth Generation (4G) and Fifth Generation (5G) mobile networks to address issues related to handover (HO) self-optimisation. Different app...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823005987 |
| _version_ | 1797736865688715264 |
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| author | Ukasyah Mahamod Hafizal Mohamad Ibraheem Shayea Marinah Othman Fauzun Abdullah Asuhaimi |
| author_facet | Ukasyah Mahamod Hafizal Mohamad Ibraheem Shayea Marinah Othman Fauzun Abdullah Asuhaimi |
| author_sort | Ukasyah Mahamod |
| collection | DOAJ |
| description | One of the most crucial issues in mobile networks is ensuring reliable and stable connectivity during mobility. In recent years, numerous research has examined Fourth Generation (4G) and Fifth Generation (5G) mobile networks to address issues related to handover (HO) self-optimisation. Different approaches have been developed to identify the best Handover Control Parameters (HCPs) settings, including Time-To-Trigger (TTT) and Handover Margin (HOM), since managing HCP values is a key factor in determining the efficiency and accuracy of handover decisions. The purpose of this work is to address the challenges associated with HO management in Sixth Generation (6G) mobile networks, where a massive number of diverse devices, high mobility, and varying network conditions (e.g., Ultra Dense Network (UDN), Heterogeneous Network (HetNet) and Millimetre Waves (mmWaves)) are established. This, in turn, presents complex HO issues which require solutions that can adapt to different deployment settings. To provide a brief background of mobility management, the paper presents the basic HO concept, HO history, and HO procedure. Additionally, a comparison of HO in 4G to 6G is discussed to gain a better understanding of technology development and its effect on HO solutions. Furthermore, the basics of Mobility Robustness Optimisation (MRO) is explained, which involve HCP values. Previous MRO approaches have made significant advancements; however, they often lack adaptability and robustness to dynamic network conditions. By leveraging Artificial Intelligence (AI) algorithms with MRO techniques, this approach offers an improved solution to optimize handover decisions in a self-optimizing manner, thereby enhancing the overall network performance. To achieve this goal, it is necessary to analyze previous MRO and AI-integrated solutions and make comparisons between them. Additionally, the advantages and disadvantages of these solutions are considered. The contribution of this work lies in identifying the directions for HO self-optimization in 6G deployment. It demonstrates that deploying an AI-based solution would benefit future MRO deployments. This survey will aid in the analysis of mobility management challenges, particularly for the future mobile MRO self-optimization implementation in future technologies. |
| first_indexed | 2024-03-12T13:20:13Z |
| format | Article |
| id | doaj.art-324fa8e668b54309b7af2e2bc4bd9fb5 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-12T13:20:13Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-324fa8e668b54309b7af2e2bc4bd9fb52023-08-26T04:42:51ZengElsevierAlexandria Engineering Journal1110-01682023-09-0178104119Handover parameter for self-optimisation in 6G mobile networks: A surveyUkasyah Mahamod0Hafizal Mohamad1Ibraheem Shayea2Marinah Othman3Fauzun Abdullah Asuhaimi4Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan, MalaysiaFaculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan, Malaysia; Corresponding author.Department of Electronics and Communication Engineering, Faculty of Electrical and Electronics Engineering, Istanbul Technical University, 34469 Istanbul, TurkeyFaculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan, MalaysiaFaculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan, MalaysiaOne of the most crucial issues in mobile networks is ensuring reliable and stable connectivity during mobility. In recent years, numerous research has examined Fourth Generation (4G) and Fifth Generation (5G) mobile networks to address issues related to handover (HO) self-optimisation. Different approaches have been developed to identify the best Handover Control Parameters (HCPs) settings, including Time-To-Trigger (TTT) and Handover Margin (HOM), since managing HCP values is a key factor in determining the efficiency and accuracy of handover decisions. The purpose of this work is to address the challenges associated with HO management in Sixth Generation (6G) mobile networks, where a massive number of diverse devices, high mobility, and varying network conditions (e.g., Ultra Dense Network (UDN), Heterogeneous Network (HetNet) and Millimetre Waves (mmWaves)) are established. This, in turn, presents complex HO issues which require solutions that can adapt to different deployment settings. To provide a brief background of mobility management, the paper presents the basic HO concept, HO history, and HO procedure. Additionally, a comparison of HO in 4G to 6G is discussed to gain a better understanding of technology development and its effect on HO solutions. Furthermore, the basics of Mobility Robustness Optimisation (MRO) is explained, which involve HCP values. Previous MRO approaches have made significant advancements; however, they often lack adaptability and robustness to dynamic network conditions. By leveraging Artificial Intelligence (AI) algorithms with MRO techniques, this approach offers an improved solution to optimize handover decisions in a self-optimizing manner, thereby enhancing the overall network performance. To achieve this goal, it is necessary to analyze previous MRO and AI-integrated solutions and make comparisons between them. Additionally, the advantages and disadvantages of these solutions are considered. The contribution of this work lies in identifying the directions for HO self-optimization in 6G deployment. It demonstrates that deploying an AI-based solution would benefit future MRO deployments. This survey will aid in the analysis of mobility management challenges, particularly for the future mobile MRO self-optimization implementation in future technologies.http://www.sciencedirect.com/science/article/pii/S1110016823005987HandoverHandover control parametersSelf-organisation networkSelf-optimisationMobility robustness optimization6G network |
| spellingShingle | Ukasyah Mahamod Hafizal Mohamad Ibraheem Shayea Marinah Othman Fauzun Abdullah Asuhaimi Handover parameter for self-optimisation in 6G mobile networks: A survey Alexandria Engineering Journal Handover Handover control parameters Self-organisation network Self-optimisation Mobility robustness optimization 6G network |
| title | Handover parameter for self-optimisation in 6G mobile networks: A survey |
| title_full | Handover parameter for self-optimisation in 6G mobile networks: A survey |
| title_fullStr | Handover parameter for self-optimisation in 6G mobile networks: A survey |
| title_full_unstemmed | Handover parameter for self-optimisation in 6G mobile networks: A survey |
| title_short | Handover parameter for self-optimisation in 6G mobile networks: A survey |
| title_sort | handover parameter for self optimisation in 6g mobile networks a survey |
| topic | Handover Handover control parameters Self-organisation network Self-optimisation Mobility robustness optimization 6G network |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823005987 |
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