Optimization of 5G base station coverage based on self-adaptive
To address these issues, this article proposes a mathematical model for optimizing 5G base station coverage and introduces an innovative adaptive mutation genetic algorithm
Comparison results of 5G base station optimization in general urban areas. As shown in Table 11, the algorithm proposed in this topic reduces the site construction cost by at least 13 %, improves the coverage by at least 5.4 %, and reduces the number of base stations by at least 17.6 % compared to other algorithms.
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.
To cover the same area as traditional cellular networks (2G, 3G, and 4G), the number of 5G base stations (BSs) could be tripled (Wang et al., 2014). Furthermore, Ge, Tu, Mao, Wang, and Han, (2016) suggested that to achieve seamless coverage services, the density of 5G BSs would reach 40-50 BSs/km 2.
It is known that there are 20 3/4G shared base stations in this area. According to Section 5, the number of base stations in general urban areas ranges from 20 to 36. Therefore, in the simulation experiment, the optimal results of the base station layout are shown in Table 10. Table 10. Layout results of 5G base station in general urban areas.
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