Base station siting optimization based on greedy and simulated annealing algorithms

Abstract

In this paper, we establish an optimization model based on the greedy algorithm and simulated annealing algorithm to provide a reference for network operators and base station planners to select sites for 5G network base stations, and establish a transitive clustering model based on chunking idea and concurrent set for larger data volume. In this paper, under the constraint that the distance between new base stations and between old and new base stations is greater than 10, the service coverage is taken as the first optimization objective and the base station construction cost is taken as the second optimization objective, and the construction cost is taken as the first optimization objective after the service coverage reaches 90%. Based on the idea of the greedy algorithm, we calculate the “cost performance” of the optional base station coordinates under the establishment of two types of base stations, macro, and micro. On this basis, the formula for determining whether a weak coverage point is covered by a BTS is adjusted, and the angle constraints of three sectors are newly added, and the simulated annealing algorithm is used to randomly adjust the angle of three sectors of some BTSs at each iteration. The model-derived BTS siting scheme can cover 90% of the total service volume of the weak coverage point.