Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey
The massive growth of mobile users and the essential need for high communication service quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) consisting of macro, micro, pico and femto cells. Each cell type provides different cell coverage and distinct system capacity i...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9740126/ |
| _version_ | 1811329745585963008 |
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| author | Emre Gures Ibraheem Shayea Mustafa Ergen Marwan Hadri Azmi Ayman A. El-Saleh |
| author_facet | Emre Gures Ibraheem Shayea Mustafa Ergen Marwan Hadri Azmi Ayman A. El-Saleh |
| author_sort | Emre Gures |
| collection | DOAJ |
| description | The massive growth of mobile users and the essential need for high communication service quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) consisting of macro, micro, pico and femto cells. Each cell type provides different cell coverage and distinct system capacity in HetNets. This leads to the pressing need to balance loads between cells, especially with the random distribution of users in numerous mobility directions. This paper provides a survey on the intelligent load balancing models that have been developed in HetNets, including those based on the machine learning (ML) technology. The survey provides a guideline and a roadmap for developing cost-effective, flexible and intelligent load balancing models in future HetNets. An overview of the generic problem of load balancing is also presented. The concept of load balancing is first introduced, and its purpose, functionality and evaluation criteria are then explained. Besides, a basic load balancing model and its operational procedure are described. A comprehensive literature review is then conducted, including techniques and solutions of addressing the load balancing problem. The key performance indicators (KPIs) used in the evaluation of load balancing models in HetNets are presented, along with the concurrent optimisation of coverage (CCO) and mobility robustness optimisation (MRO) relationship of load balancing. A comprehensive literature review of ML-driven load balancing solutions is specifically accomplished to show the historical development of load balancing models. Finally, the current challenges in implementing these models are explained as well as the future operational aspects of load balancing. |
| first_indexed | 2024-04-13T15:49:20Z |
| format | Article |
| id | doaj.art-037f4af8c51d46f4aac6f29923f0deb0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T15:49:20Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-037f4af8c51d46f4aac6f29923f0deb02022-12-22T02:40:53ZengIEEEIEEE Access2169-35362022-01-0110376893771710.1109/ACCESS.2022.31615119740126Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A SurveyEmre Gures0https://orcid.org/0000-0003-3785-9276Ibraheem Shayea1https://orcid.org/0000-0003-0957-4468Mustafa Ergen2https://orcid.org/0000-0003-0737-7575Marwan Hadri Azmi3Ayman A. El-Saleh4https://orcid.org/0000-0003-4754-9271Department of Electronics and Communication Engineering, Faculty of Electrical and Electronics Engineering, Istanbul Technical University (ITU), Istanbul, TurkeyDepartment of Electronics and Communication Engineering, Faculty of Electrical and Electronics Engineering, Istanbul Technical University (ITU), Istanbul, TurkeyDepartment of Electronics and Communication Engineering, Faculty of Electrical and Electronics Engineering, Istanbul Technical University (ITU), Istanbul, TurkeyWireless Communication Centre, Faculty of Engineering, School of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, MalaysiaDepartment of Electronics and Communication Engineering, College of Engineering, A’Sharqiyah University (ASU), Ibra, OmanThe massive growth of mobile users and the essential need for high communication service quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) consisting of macro, micro, pico and femto cells. Each cell type provides different cell coverage and distinct system capacity in HetNets. This leads to the pressing need to balance loads between cells, especially with the random distribution of users in numerous mobility directions. This paper provides a survey on the intelligent load balancing models that have been developed in HetNets, including those based on the machine learning (ML) technology. The survey provides a guideline and a roadmap for developing cost-effective, flexible and intelligent load balancing models in future HetNets. An overview of the generic problem of load balancing is also presented. The concept of load balancing is first introduced, and its purpose, functionality and evaluation criteria are then explained. Besides, a basic load balancing model and its operational procedure are described. A comprehensive literature review is then conducted, including techniques and solutions of addressing the load balancing problem. The key performance indicators (KPIs) used in the evaluation of load balancing models in HetNets are presented, along with the concurrent optimisation of coverage (CCO) and mobility robustness optimisation (MRO) relationship of load balancing. A comprehensive literature review of ML-driven load balancing solutions is specifically accomplished to show the historical development of load balancing models. Finally, the current challenges in implementing these models are explained as well as the future operational aspects of load balancing.https://ieeexplore.ieee.org/document/9740126/Mobility managementload balancingheterogeneous networkshandoverhandover problemshandover self-optimization |
| spellingShingle | Emre Gures Ibraheem Shayea Mustafa Ergen Marwan Hadri Azmi Ayman A. El-Saleh Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey IEEE Access Mobility management load balancing heterogeneous networks handover handover problems handover self-optimization |
| title | Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey |
| title_full | Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey |
| title_fullStr | Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey |
| title_full_unstemmed | Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey |
| title_short | Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey |
| title_sort | machine learning based load balancing algorithms in future heterogeneous networks a survey |
| topic | Mobility management load balancing heterogeneous networks handover handover problems handover self-optimization |
| url | https://ieeexplore.ieee.org/document/9740126/ |
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