SOMNet: Self-Optimizing mobility management for resilient 5G heterogeneous networks
Effective mobility management in heterogeneous networks is significant in ensuring seamless handovers (HOs) between diverse cell types, especially as users move between macrocells, small cells, and femtocells. The widespread and overlapping deployment of diverse cells raises the HO probability occur...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Engineering Science and Technology, an International Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098624000570 |
| _version_ | 1797220266308272128 |
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| author | Abdulraqeb Alhammadi Zool Hilmi Ismail Ibraheem Shayea Zaid Ahmed Shamsan Majid Alsagabi Sulaiman Al-Sowayan Sawsan Ali Saad Mohammad Alnakhli |
| author_facet | Abdulraqeb Alhammadi Zool Hilmi Ismail Ibraheem Shayea Zaid Ahmed Shamsan Majid Alsagabi Sulaiman Al-Sowayan Sawsan Ali Saad Mohammad Alnakhli |
| author_sort | Abdulraqeb Alhammadi |
| collection | DOAJ |
| description | Effective mobility management in heterogeneous networks is significant in ensuring seamless handovers (HOs) between diverse cell types, especially as users move between macrocells, small cells, and femtocells. The widespread and overlapping deployment of diverse cells raises the HO probability occurrence massively, particularly when users are on the move and connected to multiple cells simultaneously to ensure uninterrupted connectivity. This phenomenon leads to an increase in HO ping pong (HOPP) and HO failure (HOF) occurrences, ultimately degrading network performance. In this context, this paper proposes a self-optimization algorithm to address the contradiction in the optimization tasks of mobility robustness optimization (MRO) and load balancing optimization (LBO) functions. The algorithm aims to facilitate seamless user communication as individuals move across various deployment scenarios. The MRO function leverages two key parameters: the reference signal received power (RSRP) levels of serving and target cells, as well as user movement speed. On the other hand, the LBO function takes into account the traffic load of serving and target cells to determine the suitable values of HO control parameters. Moreover, our research contributes to the present network optimization challenges and positions itself as an enabler for the seamless integration of emerging technologies. As the wireless ecosystem continues to evolve, with the advent of edge computing and network slicing technologies, our self-optimization algorithm offers adaptability and scalability to meet the evolving demands of next-generation wireless networks. This forward-looking solution benefits network operators and end-users by providing robust and efficient mobility management. The proposed algorithm demonstrates its effectiveness through comprehensive simulations by significantly reducing HOPP and HOF compared to investigated methods selected from the literature, showcasing its potential to enhance network performance and user experience. |
| first_indexed | 2024-04-24T12:46:48Z |
| format | Article |
| id | doaj.art-69722f730e6a4549ad729e0423455343 |
| institution | Directory Open Access Journal |
| issn | 2215-0986 |
| language | English |
| last_indexed | 2024-04-24T12:46:48Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj.art-69722f730e6a4549ad729e04234553432024-04-07T04:35:45ZengElsevierEngineering Science and Technology, an International Journal2215-09862024-04-0152101671SOMNet: Self-Optimizing mobility management for resilient 5G heterogeneous networksAbdulraqeb Alhammadi0Zool Hilmi Ismail1Ibraheem Shayea2Zaid Ahmed Shamsan3Majid Alsagabi4Sulaiman Al-Sowayan5Sawsan Ali Saad6Mohammad Alnakhli7Center for Artificial Intelligence and Robotics (CAIRO), Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, MalaysiaCenter for Artificial Intelligence and Robotics (CAIRO), Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia; Corresponding authors.Electronics and Communication Engineering Department, Faculty of Electrical and Electronics Engineering, Istanbul Technical University, 34469 Istanbul, Turkey; Corresponding authors.Department of Electrical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Saudi ArabiaDepartment of Computer Engineering, University of Ha’il, Ha’il 55211, Saudi ArabiaElectrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addwasir 11991, Saudi ArabiaEffective mobility management in heterogeneous networks is significant in ensuring seamless handovers (HOs) between diverse cell types, especially as users move between macrocells, small cells, and femtocells. The widespread and overlapping deployment of diverse cells raises the HO probability occurrence massively, particularly when users are on the move and connected to multiple cells simultaneously to ensure uninterrupted connectivity. This phenomenon leads to an increase in HO ping pong (HOPP) and HO failure (HOF) occurrences, ultimately degrading network performance. In this context, this paper proposes a self-optimization algorithm to address the contradiction in the optimization tasks of mobility robustness optimization (MRO) and load balancing optimization (LBO) functions. The algorithm aims to facilitate seamless user communication as individuals move across various deployment scenarios. The MRO function leverages two key parameters: the reference signal received power (RSRP) levels of serving and target cells, as well as user movement speed. On the other hand, the LBO function takes into account the traffic load of serving and target cells to determine the suitable values of HO control parameters. Moreover, our research contributes to the present network optimization challenges and positions itself as an enabler for the seamless integration of emerging technologies. As the wireless ecosystem continues to evolve, with the advent of edge computing and network slicing technologies, our self-optimization algorithm offers adaptability and scalability to meet the evolving demands of next-generation wireless networks. This forward-looking solution benefits network operators and end-users by providing robust and efficient mobility management. The proposed algorithm demonstrates its effectiveness through comprehensive simulations by significantly reducing HOPP and HOF compared to investigated methods selected from the literature, showcasing its potential to enhance network performance and user experience.http://www.sciencedirect.com/science/article/pii/S2215098624000570Mobility managementHandover5GHetNetsSelf-optimization |
| spellingShingle | Abdulraqeb Alhammadi Zool Hilmi Ismail Ibraheem Shayea Zaid Ahmed Shamsan Majid Alsagabi Sulaiman Al-Sowayan Sawsan Ali Saad Mohammad Alnakhli SOMNet: Self-Optimizing mobility management for resilient 5G heterogeneous networks Engineering Science and Technology, an International Journal Mobility management Handover 5G HetNets Self-optimization |
| title | SOMNet: Self-Optimizing mobility management for resilient 5G heterogeneous networks |
| title_full | SOMNet: Self-Optimizing mobility management for resilient 5G heterogeneous networks |
| title_fullStr | SOMNet: Self-Optimizing mobility management for resilient 5G heterogeneous networks |
| title_full_unstemmed | SOMNet: Self-Optimizing mobility management for resilient 5G heterogeneous networks |
| title_short | SOMNet: Self-Optimizing mobility management for resilient 5G heterogeneous networks |
| title_sort | somnet self optimizing mobility management for resilient 5g heterogeneous networks |
| topic | Mobility management Handover 5G HetNets Self-optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2215098624000570 |
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