In previous research on 5 G wireless networks, the optimization of base station deployment primarily relied on human expertise, simulation software, and algorithmic optimization.
However, the selection of 5G base station locations is also influenced by local terrain and population distribution, and obstacles such as streets, buildings, and trees can significantly impact signal propagation.
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.
Layout results of 5G base station in dense urban areas. From the simulation comparison results in Tables 8 and it can be seen that when m 1 = 0.3, m 2 = 0.7, although the coverage target function result is slightly lower than the 92.8 % coverage result, the result saves the cost of building the station.
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The objective of this study is to develop a location optimization model to support the planning of ultra-dense 5G BSs in urban outdoor areas and to help address the cost …
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In China, the coverage of 5G network is increasing rapidly, and the cost of base station construction is huge. Therefore, reasonable and efficient site planning is an extremely …
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Therefore, this proposes a 5G base station planning model based on the idea of the binary mask, combining differential evolution algorithm and Monte Carlo simulation to fully consider the …
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