Research On Optimization Of Base Station Position Selection Based On Intelligent Algorithms And Multi-objective Models

With the rapid development of Internet and the mobile users, the location of the new constructed base station optimization has become a major problem in order to supply better communication service. Reasonable base station location optimization can reduce the cost of construction and improve the quality of network. Therefore, the focus of this paper is the placement optimization of the planning base station.Base station planning is a multi-objective optimization problem. The optimization process is complicated and costs too much time when the number of base station is large. Aiming at overcoming the shortcoming of the traditional annealing planning methods, for instance, they are instable and easy to fall into local optimal, accelerating genetic algorithm is used in solving multi-objective optimization problem for its flexibility, a new method based on accelerating genetic algorithm is proposed. In order to design planning method which is closer to real project, base station coverage and electromagnetic interference conditions are considered, therefore,the optimization model is established. Simulation results show that accelerating genetic algorithm which is applied to obtain the best base station placement solution is very effective in solving the complex optimization problem compared with the simulated annealing algorithm. Therefore, the method can get a better placement of the base station.Considering the complex communication environment, in order to obtain a good quality of service, a mathematical model that considering multi-factors must be established, this paper considers the cost, coverage, capacity, regional division and so on. The optimization of base station placement model is established with high complexity. The optimal method of base station placement based on the combination of particle swarm optimization algorithm is proposed. According to different subarea that is planned, the density of different test points is distributed in different subarea.The test points have two forms that is uniform and non-uniform, at the same time, the type of base stations placed are different. Then two different type of base stations are placed in different subarea. When the user is covered by the base station, the base station can provide services for users. Experiment results show that method based on combination strategy is more reasonable in the base station position selection compared with the standard particle swarm optimization algorithm in the two simulation scenarios.