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For the problem of 5G network planning, a certain number of locations should be selected to build new base stations in order to solve the weak coverage problems of the existing network. Considering the construction cost and some other factors, it is impossible to cover all the weak coverage areas so it is necessary to consider the business volume and give priority to build new stations in the weak coverage areas with high business volume. Aimed at these problems, the clustering of weak point data was carried out by using k-means clustering algorithm. With the objective function as the minimization of the total construction cost of the new base stations, as well as the constraints as the minimal distance between adjacent base stations and the minimal coverage of the communication traffic, the single-objective nonlinear programming models were established to obtain the layout of macro and micro base stations in order to illustrate the impact of the shape of the station coverage area, the circular and the “shamrock” shaped coverage areas were compared in this paper. For the “shamrock” base station, a secondary clustering was undertaken to judge the main directions of the three sector coverage areas. Then, an improved model taking the coverage overlapping into consideration was proposed to correct the coverage area of different sectors. Finally, the optimal layout was obtained by adjusting the distribution of all base stations globally. The results show that the optimal planning method proposed in this paper has good practicability, which also provides a very good reference for solving similar allocation problems of dynamic resources.
For the problem of 5G network planning, a certain number of locations should be selected to build new base stations in order to solve the weak coverage problems of the existing network. Considering the construction cost and some other factors, it is impossible to cover all the weak coverage areas so it is necessary to consider the business volume and give priority to build new stations in the weak coverage areas with high business volume. Aimed at these problems, the clustering of weak point data was carried out by using k-means clustering algorithm. With the objective function as the minimization of the total construction cost of the new base stations, as well as the constraints as the minimal distance between adjacent base stations and the minimal coverage of the communication traffic, the single-objective nonlinear programming models were established to obtain the layout of macro and micro base stations in order to illustrate the impact of the shape of the station coverage area, the circular and the “shamrock” shaped coverage areas were compared in this paper. For the “shamrock” base station, a secondary clustering was undertaken to judge the main directions of the three sector coverage areas. Then, an improved model taking the coverage overlapping into consideration was proposed to correct the coverage area of different sectors. Finally, the optimal layout was obtained by adjusting the distribution of all base stations globally. The results show that the optimal planning method proposed in this paper has good practicability, which also provides a very good reference for solving similar allocation problems of dynamic resources.
The problem of insufficient signal coverage of 5G base stations can be solved by building new base stations in areas with weak signal coverage. However, due to construction costs and other factors, it is not possible to cover all areas. In general, areas with high traffic and weak coverage should be given priority. Although many scientists have carried out research, it is not possible to make the large-scale calculation accurately due to the lack of data support. It is necessary to search for the central point through continuous hypothesis testing, so there is a large systematic error. In addition, it is difficult to give a unique solution. In this paper, the weak signal coverage points were divided into three categories according to the number of users and traffic demand. With the lowest cost as the target, and constraints such as the distance requirement of base station construction, the proportion of the total signal coverage business, and so on, a single objective nonlinear programming model was established to solve the base station layout problem. Through traversal search, the optimal threshold of the traffic and the number of base stations was obtained, and then, a kernel function was added to the mean shift clustering algorithm. The center point of the new macro station was determined in the dense area, the location of the micro base station was determined from the scattered and abnormal areas, and finally the unique optimal planning scheme was obtained. Based on the assumptions made in this paper, the minimum total cost is 3752 when the number of macro and micro base stations were determined to be 31 and 3442 respectively, and the signal coverage rate can reach 91.43%. Compared with the existing methods, such as K-means clustering, K-medoids clustering, and simulated annealing algorithms, etc., the method proposed in this paper can achieve good economic benefits; when the traffic threshold and the number of base stations threshold are determined, the unique solution can be obtained.
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