A Novel Self-Optimization Algorithm for 5G HetNets Using Automatic Weight Function and TOPSIS

In this paper, we propose a novel self-optimization framework that incorporates an automatic weight function for determining Handover Control Parameters (HCP) and utilizes the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm for target cell selection. The proposed algorithm estimates suitable HCP values automatically based on key factors such as Signal-to-Interference-plus-Noise Ratio (SINR), traffic load, and user equipment (UE)’s speed. Additionally, TOPSIS algorithm is employed to select the most suitable target cell based on three evaluation criteria: SINR, Reference Signal Received Power (RSRP), and load level. The performance of the proposed algorithm is evaluated using load level, throughput, and Radio Link Failure (RLF) as key performance indicators (KPIs). Extensive simulations are carried out to assess the performance of the proposed approach. The findings indicate that the proposed algorithm outperforms the existing solutions in terms of load level, throughput, and RLF.