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An adaptive optimized handover decision model for heterogeneous networks.

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Area of Science:

  • Mobile Communications
  • Network Engineering
  • Optimization Algorithms

Background:

  • Heterogeneous networks (HetNets) are crucial for meeting evolving mobile user demands and enhancing network performance.
  • Vertical handover (VHO) algorithms are essential for maintaining seamless user connectivity within HetNets.
  • Existing VHO algorithms face challenges in achieving optimal network and user-centric performance.

Purpose of the Study:

  • To propose an adaptive optimized VHO algorithm for improved performance in HetNets.
  • To enhance network efficiency and user experience through intelligent handover decisions.
  • To address the limitations of current VHO strategies in complex wireless environments.

Main Methods:

  • Developed an adaptive VHO algorithm integrating multi-attribute decision-making (MADM) with particle swarm optimization and gravitational search algorithm (PSOGSA).
  • Introduced novel control parameters: load factor (LF) and score priority (SP) to manage handovers and network load.
  • Formulated an objective function optimized by PSOGSA to determine optimal LF and SP values for handover execution.

Main Results:

  • The proposed algorithm reduced unnecessary handovers by over 40%.
  • Achieved significant improvements in load distribution, ranging from 20% to 40%.
  • Demonstrated superior performance compared to traditional handover approaches in simulated 5G-enabled HetNets.

Conclusions:

  • The adaptive optimized VHO algorithm effectively enhances HetNet performance.
  • The integration of MADM and metaheuristic optimization (PSOGSA) provides a robust framework for VHO.
  • The proposed approach offers a promising solution for future mobile network generations requiring efficient resource management and seamless connectivity.