Distribution Function-Driven Handover Solutions for 5G Mobile Networks

The advent of 5G technology demands significant improvements in handover mechanisms to ensure seamless connectivity and optimal performance in mobile networks. In a 5G network setting, the issue of excessive handovers for mobile devices using cell data and pattern analysis was identified. This study proposes a distribution function driven handover solution for 5G mobile networks, aiming to enhance handover efficiency and reliability. Similarly, leveraging advanced statistical distribution functions, our approach dynamically adjusts handover parameters to accommodate varying network conditions and user mobility patterns. Extensive simulations testing demonstrated that our method achieves a 98% accuracy rate in predicting and managing handovers, significantly surpassing the performance of existing methodologies. The proposed solution not only minimizes handover failures and latency but also optimizes resource allocation and network throughput. These results highlight the potential of distribution function driven strategies to revolutionize handover processes in 5G networks, paving the way for more resilient and adaptive mobile communication systems. The results underscore the advantages of using probability distribution functions for handover management in 5G networks. The adaptive and dynamic nature of this approach addresses the limitations of traditional fixed-threshold methods, providing a more resilient and flexible framework suited for the complex and variable conditions of 5G environments.

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Publication Details

Journal
Journal of Statistical Sciences and Computational Intelligence
Published
2025-06-29
DOI
https://doi.org/10.64497/jssci.1
Citations
1
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
0.73

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article

Distribution Function-Driven Handover Solutions for 5G Mobile Networks

Aftab Alam Janisar, Rufai Yusuf Zakari, Umar Danjuma Maiwada
1 citations
Journal of Statistical Sciences and Computational Intelligence
Advanced MIMO Systems Optimization
0.73
article

Distribution Function-Driven Handover Solutions for 5G Mobile Networks

Aftab Alam Janisar, Rufai Yusuf Zakari, Umar Danjuma Maiwada
article en
1 citations

Abstract

The advent of 5G technology demands significant improvements in handover mechanisms to ensure seamless connectivity and optimal performance in mobile networks. In a 5G network setting, the issue of excessive handovers for mobile devices using cell data and pattern analysis was identified. This study proposes a distribution function driven handover solution for 5G mobile networks, aiming to enhance handover efficiency and reliability. Similarly, leveraging advanced statistical distribution functions, our approach dynamically adjusts handover parameters to accommodate varying network conditions and user mobility patterns. Extensive simulations testing demonstrated that our method achieves a 98% accuracy rate in predicting and managing handovers, significantly surpassing the performance of existing methodologies. The proposed solution not only minimizes handover failures and latency but also optimizes resource allocation and network throughput. These results highlight the potential of distribution function driven strategies to revolutionize handover processes in 5G networks, paving the way for more resilient and adaptive mobile communication systems. The results underscore the advantages of using probability distribution functions for handover management in 5G networks. The adaptive and dynamic nature of this approach addresses the limitations of traditional fixed-threshold methods, providing a more resilient and flexible framework suited for the complex and variable conditions of 5G environments.

Journal of Statistical Sciences and Computational IntelligenceVol. 1(1)
Petronas (Malaysia) (MY), Universiti Brunei Darussalam (BN), Universiti Teknologi Petronas (MY), Umaru Musa Yar'adua University (NG)
Universiti Teknologi Petronas
Openalex Percentile: Top 25%
Advanced MIMO Systems Optimization
0.73
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