AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular Networks
This paper presents a comprehensive review of handover management and load balancing optimization (LBO) in ultra-dense 5G and emerging 6G cellular networks. With the increasing deployment of small cells and the rapid growth of data traffic, these networks face significant challenges in ensuring seam...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2227-7080/13/7/276 |
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| author | Chaima Chabira Ibraheem Shayea Gulsaya Nurzhaubayeva Laura Aldasheva Didar Yedilkhan Saule Amanzholova |
| author_facet | Chaima Chabira Ibraheem Shayea Gulsaya Nurzhaubayeva Laura Aldasheva Didar Yedilkhan Saule Amanzholova |
| author_sort | Chaima Chabira |
| collection | DOAJ |
| description | This paper presents a comprehensive review of handover management and load balancing optimization (LBO) in ultra-dense 5G and emerging 6G cellular networks. With the increasing deployment of small cells and the rapid growth of data traffic, these networks face significant challenges in ensuring seamless mobility and efficient resource allocation. Traditional handover and load balancing techniques, primarily designed for 4G systems, are no longer sufficient to address the complexity of heterogeneous network environments that incorporate millimeter-wave communication, Internet of Things (IoT) devices, and unmanned aerial vehicles (UAVs). The review focuses on how recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), are being applied to improve predictive handover decisions and enable real-time, adaptive load distribution. AI-driven solutions can significantly reduce handover failures, latency, and network congestion, while improving overall user experience and quality of service (QoS). This paper surveys state-of-the-art research on these techniques, categorizing them according to their application domains and evaluating their performance benefits and limitations. Furthermore, the paper discusses the integration of intelligent handover and load balancing methods in smart city scenarios, where ultra-dense networks must support diverse services with high reliability and low latency. Key research gaps are also identified, including the need for standardized datasets, energy-efficient AI models, and context-aware mobility strategies. Overall, this review aims to guide future research and development in designing robust, AI-assisted mobility and resource management frameworks for next-generation wireless systems. |
| format | Article |
| id | doaj-art-b60cc232c4114c209fe2eea2a7890642 |
| institution | Kabale University |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-b60cc232c4114c209fe2eea2a78906422025-08-20T03:56:47ZengMDPI AGTechnologies2227-70802025-07-0113727610.3390/technologies13070276AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular NetworksChaima Chabira0Ibraheem Shayea1Gulsaya Nurzhaubayeva2Laura Aldasheva3Didar Yedilkhan4Saule Amanzholova5Laboratory of System Signal Analysis (LASS), Department of Electronics, Faculty of Technology, Mohamed Boudiaf University, M’sila 28000, AlgeriaDepartment of Intelligent Systems and Cybersecurity, Astana IT University, Astana 010000, KazakhstanDepartment of Intelligent Systems and Cybersecurity, Astana IT University, Astana 010000, KazakhstanDepartment of Intelligent Systems and Cybersecurity, Astana IT University, Astana 010000, KazakhstanSmart City Research Center, Astana IT University, Astana 010000, KazakhstanDepartment of Intelligent Systems and Cybersecurity, Astana IT University, Astana 010000, KazakhstanThis paper presents a comprehensive review of handover management and load balancing optimization (LBO) in ultra-dense 5G and emerging 6G cellular networks. With the increasing deployment of small cells and the rapid growth of data traffic, these networks face significant challenges in ensuring seamless mobility and efficient resource allocation. Traditional handover and load balancing techniques, primarily designed for 4G systems, are no longer sufficient to address the complexity of heterogeneous network environments that incorporate millimeter-wave communication, Internet of Things (IoT) devices, and unmanned aerial vehicles (UAVs). The review focuses on how recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), are being applied to improve predictive handover decisions and enable real-time, adaptive load distribution. AI-driven solutions can significantly reduce handover failures, latency, and network congestion, while improving overall user experience and quality of service (QoS). This paper surveys state-of-the-art research on these techniques, categorizing them according to their application domains and evaluating their performance benefits and limitations. Furthermore, the paper discusses the integration of intelligent handover and load balancing methods in smart city scenarios, where ultra-dense networks must support diverse services with high reliability and low latency. Key research gaps are also identified, including the need for standardized datasets, energy-efficient AI models, and context-aware mobility strategies. Overall, this review aims to guide future research and development in designing robust, AI-assisted mobility and resource management frameworks for next-generation wireless systems.https://www.mdpi.com/2227-7080/13/7/2765G6Gultra-dense networkshandovermobility managementartificial intelligence |
| spellingShingle | Chaima Chabira Ibraheem Shayea Gulsaya Nurzhaubayeva Laura Aldasheva Didar Yedilkhan Saule Amanzholova AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular Networks Technologies 5G 6G ultra-dense networks handover mobility management artificial intelligence |
| title | AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular Networks |
| title_full | AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular Networks |
| title_fullStr | AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular Networks |
| title_full_unstemmed | AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular Networks |
| title_short | AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular Networks |
| title_sort | ai driven handover management and load balancing optimization in ultra dense 5g 6g cellular networks |
| topic | 5G 6G ultra-dense networks handover mobility management artificial intelligence |
| url | https://www.mdpi.com/2227-7080/13/7/276 |
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