Tuning of Handover Control Parameters Using Whale Optimization Algorithm in Mobile Networks

Proper setting of handover control parameters (HCP) is essential for maintaining seamless connectivity and maximizing network performance in dense heterogeneous networks (HetNets). This paper proposes an adaptive population whale optimization algorithm (AP-WOA) to jointly optimize HCPs, specifically handover margin (HOM) and time-to-trigger (TTT), across all base stations with the objective of maximizing system capacity. The AP-WOA dynamically adjusts the population size based on stagnation detection to accelerate convergence and reduce computational cost. Additionally, the proposed AP-WOA framework is enhanced by incorporating a multi-criteria decision-making (MCDM) approach based on the technique for order preference by similarity to ideal solution (TOPSIS), which jointly evaluates received signal strength (RSRP), signal-to-interference-plus-noise ratio (SINR), and load level for selecting the target base station. Simulation results demonstrate that the proposed approach achieves significant improvements in average user capacity and reduces radio link failure (RLF) rates compared to state-of-the-art benchmark algorithms under varying user mobility scenarios. Specifically, capacity gains of up to 47% and reductions in RLF of up to 80% are observed, highlighting the effectiveness of the combined AP-WOA and TOPSIS framework in HetNets.