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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Institute of Electrical and Electronics Engineers Inc
2022
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| Online Access: | http://eprints.utm.my/104373/1/MarwanHadriAzmi2022_MachineLearningBasedLoadBalancing.pdf |
| _version_ | 1796867644509388800 |
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| author | Gures, Emre Shayea, Ibraheem Ergen, Mustafa Azmi, Marwan Hadri El-Saleh, Ayman A. |
| author_facet | Gures, Emre Shayea, Ibraheem Ergen, Mustafa Azmi, Marwan Hadri El-Saleh, Ayman A. |
| author_sort | Gures, Emre |
| collection | ePrints |
| 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 | 2024-03-05T21:30:22Z |
| format | Article |
| id | utm.eprints-104373 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:30:22Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers Inc |
| record_format | dspace |
| spelling | utm.eprints-1043732024-02-04T09:40:09Z http://eprints.utm.my/104373/ Machine learning-based load balancing algorithms in future heterogeneous networks: A survey Gures, Emre Shayea, Ibraheem Ergen, Mustafa Azmi, Marwan Hadri El-Saleh, Ayman A. TK Electrical engineering. Electronics Nuclear engineering 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. Institute of Electrical and Electronics Engineers Inc 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/104373/1/MarwanHadriAzmi2022_MachineLearningBasedLoadBalancing.pdf Gures, Emre and Shayea, Ibraheem and Ergen, Mustafa and Azmi, Marwan Hadri and El-Saleh, Ayman A. (2022) Machine learning-based load balancing algorithms in future heterogeneous networks: A survey. IEEE Access, 10 (NA). pp. 37689-37717. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2022.3161511 DOI : 10.1109/ACCESS.2022.3161511 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Gures, Emre Shayea, Ibraheem Ergen, Mustafa Azmi, Marwan Hadri El-Saleh, Ayman A. Machine learning-based load balancing algorithms in future heterogeneous networks: A survey |
| 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 | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/104373/1/MarwanHadriAzmi2022_MachineLearningBasedLoadBalancing.pdf |
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