Evaluating the Comprehensive Performance of 5G Base Station: A Hybrid MCDM Model Based on Bayesian Best-Worst Method and DQ-GRA Technique

Abstract

In recent years, 5G technology has rapidly developed, which is widely used in medical, transportation, energy, and other fields. As the core equipment of the 5G network, 5G base stations provide wireless coverage and realize wireless signal transmission between wired communication networks and wireless terminals. However, as the scale of 5G base stations gradually increases, problems such as poor user experience and insufficient coverage area frequently occur. Hence, it is necessary to evaluate the comprehensive performance of 5G base stations, so as to clarify the problems existing in the construction of base stations. First, the performance evaluation index system is constructed from the perspectives of operational performance, financial performance, environmental impact, and social influence. Then, a novel hybrid multicriteria decision-making (MCDM) model based on the Bayesian best-worst method (BBWM) and difference-quotient gray relational analysis (DQ-GRA) technique is adopted. Finally, sixteen 5G base stations are taken as examples for analysis. The result shows that the signal coverage area and per capita input cost are the most important indicators greatly affecting the overall performance of the 5G base station. Compared with the two other MCDM models, the proposed hybrid MCDM model has good applicability and effectiveness for performance evaluation of 5G base stations.

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