Citation :

Ezeilo Ifeoma Kelechukwu, Ifeoma Bernardine Asianuba, "Optimization Model for Load Balancing in 5G Three Tier Heterogenous Networks," International Journal of Recent Engineering Science (IJRES), vol. 12, no. 6, pp. 12-24, 2025. Crossref, https://doi.org/10.14445/23497157/IJRES-V12I6P102

Abstract

Load imbalance is a scenario that occurs in a HetNet where the macrocell is overcongested with loads while leaving the smaller cells underutilized. Over the years, this has remained one of the major issues affecting the quality of service in the 5G network and has continued to attract research interest. In this work, the ability to identify the dynamic characteristics of real time user behavior that make cell overload a complex problem was established. The methodology required the use of machine learning and simulation to address the challenges of load balancing. The dynamic network data was collected and applied to train a multi-layered neural network algorithm, utilizing dropout regularization and optimization techniques. An overload detection model was formulated and integrated with a vertical handover technique to develop an Intelligent Overload Management (IOM) model in a 5G network. Matlab classification software was used to train the neural network, while the Python programming language was used to integrate the IOM into the 5G HetNet. Simulation results considering varying load factors presented an average latency of 140ms and a throughput of 80%. To validate the model, real-time data from the HetNet was collected during a live walk-test experiment using Open Signal software and then compared with the formulated 5G network with IOM system integration. The results showed an average latency of 139.8ms with IOM as against 148ms in the existing network, presenting a 5.4% improvement in latency reduction.

Keywords

Optimization, Heterogeneous network, 5G, Intelligent overload management, Load balancing, Latency, Handover.

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