ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile Network
The Fifth Generation (5G) and Beyond (B5G) mobile systems employ advanced technologies, such as millimeter Wave (mmWave) and Ultra-Dense Networks (UDNs), to meet future networks’ requirements. However, implementing these technologies may pose several challenges to the B5G network. One key...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10836672/ |
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| author | Saddam Alraih Rosdiadee Nordin Asma Abu-Samah Ibraheem Shayea Nor Fadzilah Abdullah |
| author_facet | Saddam Alraih Rosdiadee Nordin Asma Abu-Samah Ibraheem Shayea Nor Fadzilah Abdullah |
| author_sort | Saddam Alraih |
| collection | DOAJ |
| description | The Fifth Generation (5G) and Beyond (B5G) mobile systems employ advanced technologies, such as millimeter Wave (mmWave) and Ultra-Dense Networks (UDNs), to meet future networks’ requirements. However, implementing these technologies may pose several challenges to the B5G network. One key challenge is the need for efficient Handover (HO) optimization processes. HO aims to ensure seamless connectivity and uninterrupted services for users while moving from one cell to another within the coverage area. Thus, this study introduces a new, intelligent, and robust self-optimization HO technique designed to work efficiently with the B5G networks. The technique utilizes Machine Learning (ML), particularly leveraging the Regression Tree (RT) model. In this study, the proposed technique is referred to as the ML-based Self-Optimization Handover Technique (ML-SOHOT). The technique is evaluated and validated using different major HO metrics, including Handover Probability (HOP), Handover Failure (HOF), and Handover Ping-Pong (HOPP) across various mobility patterns in B5G, specifically considering an urban environment. The results demonstrate that ML-SOHOT enhanced the HO optimization performance significantly and surpassed the competitive algorithms. Furthermore, ML-SOHOT achieves an average HO performance improvement of up to 96% compared to competitive algorithms from the literature. Consequently, the technique could enhance the overall B5G system performance and user experience. |
| format | Article |
| id | doaj-art-7a70d1fe23df46e9935638e5a39b12c1 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7a70d1fe23df46e9935638e5a39b12c12025-01-21T00:01:52ZengIEEEIEEE Access2169-35362025-01-01138568858410.1109/ACCESS.2025.352835710836672ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile NetworkSaddam Alraih0https://orcid.org/0000-0002-5152-4686Rosdiadee Nordin1https://orcid.org/0000-0001-9254-2023Asma Abu-Samah2https://orcid.org/0000-0001-8514-1459Ibraheem Shayea3https://orcid.org/0000-0003-0957-4468Nor Fadzilah Abdullah4https://orcid.org/0000-0002-6593-5603Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, MalaysiaDepartment of Engineering, School of Engineering and Technology, Sunway University, Bandar Sunway, Selangor, MalaysiaDepartment of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, MalaysiaDepartment of Electronics and Communication Engineering, Faculty of Electrical and Electronics Engineering, Istanbul Technical University (ITU), İstanbul, TürkiyeDepartment of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, MalaysiaThe Fifth Generation (5G) and Beyond (B5G) mobile systems employ advanced technologies, such as millimeter Wave (mmWave) and Ultra-Dense Networks (UDNs), to meet future networks’ requirements. However, implementing these technologies may pose several challenges to the B5G network. One key challenge is the need for efficient Handover (HO) optimization processes. HO aims to ensure seamless connectivity and uninterrupted services for users while moving from one cell to another within the coverage area. Thus, this study introduces a new, intelligent, and robust self-optimization HO technique designed to work efficiently with the B5G networks. The technique utilizes Machine Learning (ML), particularly leveraging the Regression Tree (RT) model. In this study, the proposed technique is referred to as the ML-based Self-Optimization Handover Technique (ML-SOHOT). The technique is evaluated and validated using different major HO metrics, including Handover Probability (HOP), Handover Failure (HOF), and Handover Ping-Pong (HOPP) across various mobility patterns in B5G, specifically considering an urban environment. The results demonstrate that ML-SOHOT enhanced the HO optimization performance significantly and surpassed the competitive algorithms. Furthermore, ML-SOHOT achieves an average HO performance improvement of up to 96% compared to competitive algorithms from the literature. Consequently, the technique could enhance the overall B5G system performance and user experience.https://ieeexplore.ieee.org/document/10836672/5GB5Ghandover managementhandover optimizationPing-Ponghandover probability |
| spellingShingle | Saddam Alraih Rosdiadee Nordin Asma Abu-Samah Ibraheem Shayea Nor Fadzilah Abdullah ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile Network IEEE Access 5G B5G handover management handover optimization Ping-Pong handover probability |
| title | ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile Network |
| title_full | ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile Network |
| title_fullStr | ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile Network |
| title_full_unstemmed | ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile Network |
| title_short | ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile Network |
| title_sort | ml based self optimization handover technique for beyond 5g mobile network |
| topic | 5G B5G handover management handover optimization Ping-Pong handover probability |
| url | https://ieeexplore.ieee.org/document/10836672/ |
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