Optimization of 5G base station coverage based on self-adaptive mutation genetic algorithm

Abstract

In communication network planning, a rational base station layout plays a crucial role in improving communication speed, ensuring service quality, and reducing investment costs. To address this, the article calibrated the urban microcell (UMa) signal propagation model using the least squares method, based on road test data collected from three distinct environments: dense urban areas, general urban areas, and suburbs. With the calibrated model, a detailed link budget analysis was performed on the planning area, calculating the maximum coverage radius required for a single base station to meet communication demands, and accordingly determining the number of base stations needed. Subsequently, this article proposed the Adaptive Mutation Genetic Algorithm (AMGA) and formulated a mathematical model for optimizing 5G base station coverage to improve the base station layout. Simulation experiments were conducted in three different scenarios, and the results indicate that the proposed AMGA algorithm effectively enhances base station coverage while reducing construction costs, thoroughly demonstrating the value of base station layout optimization in practical applications.