改进免疫算法在无线网络基站选址优化中的应用

Application of improved immune algorithm in wireless network base station location planning optimization

为了最小化网络建设成本、提高用户的服务质量,提出了一种基于矢量距离免疫计算的基站选址问题求解方案。设计了基于矢量距离的抗体浓度计算方法,采用了反学习的种群初始化策略,给出了问题求解的数学模型。主要工作是准确确定基站的位置和数目,以达到建站代价最小、且满足用户覆盖率和系统容量的目的。仿真结果表明:所提算法有更突出的全局搜索能力和快速收敛性,能更好地实现用较低的建站成本满足用户覆盖率和系统容量。

In order to minimize network construction cost,improve quality of user＇s service,a solution for base station location planning based on vector distance immune computation is proposed. The method for antibody concentration calculation based on vector distance is designed,a population initialization strategy based on opposition-based learning is adopted,mathematical model for problem solving is presented. The main task is to accurately determine locations and numbers of base stations,in order to reach the purpose of the minimum cost,and meet user coverage rate and system capacity. Simulation results shows that the proposed algorithm has outstanding global searching ability and fast convergence ability,can well meet user＇s coverage rate needs and system capacity with low cost of station construction relatively.