一种改进的热点区域微基站优化部署算法研究  

Research on an Improved Algorithm for Optimizing Deployment of Micro Base Stations in Hotspot Regions

作　　者：刘江亮 徐东辉 刘思佚 苏阳[2] LIU Jiang-liang;XUI Dong-hui;LIU Si-yi;SU Yang(School of Combat Support,Rocket Force University of Engineering,Xi'an,Shaanxi 710025,China;School of Cryptography Engineering,Engineering University of PAP,Xi'an,Shaanxi 710086,China)

机构地区：[1]火箭军工程大学作战保障学院,陕西西安710025 [2]武警工程大学密码工程学院,陕西西安710086

出　　处：《计算机仿真》2025年第1期307-312,共6页Computer Simulation

摘　　要：热点区域基站优化部署问题是5G移动通信中必须关注的问题之一。为了减少热点区域微基站布设数量、节约基站维护成本,在满足各类约束条件的前提下,以服务用户数量最大为目标函数,建立5G微基站部署优化模型,并提出一种改进灰狼算法对模型进行求解。上述算法使用Logistic混沌初始化代替原始灰狼算法中的随机初始化,使用非线性指数收敛因子代替原有的线性收敛因子,并将粒子群算法中的惯性机制引入到传统灰狼算法中。通过对改进算法与传统灰狼算法、粒子群算法的对比分析,实验结果表明,在基站数量较低场景下,所提改进算法的收敛速度分别较传统灰狼算法、粒子群算法快15次迭代和50次迭代,在基站数量高的场景下,改进算法覆盖率分别较传统灰狼算法、粒子群算法提高了5.4%和1.9%。The optimization and deployment of base stations in hot areas is one of the issues that must be addressed in 5G mobile communication.In order to reduce the number of micro base stations deployed in hotspots and save maintenance costs,a 5G micro base station deployment optimization model is established with the objective function of maximizing the number of service users,while meeting various constraints.An improved Grey Wolf algorithm is proposed to solve the model.This algorithm uses Logistic chaos initialization instead of random initialization in the original Grey Wolf algorithm,uses nonlinear exponential convergence factor instead of linear convergence factor,and introduces the inertia mechanism in particle swarm optimization into the traditional Grey Wolf algorithm.By comparing and analyzing the improved algorithm with traditional Grey Wolf algorithm and particle swarm algorithm,the experimental results show that in scenarios with low number of base stations,the convergence speed of the proposed improved algorithm is 15 and 50 iterations faster than traditional Grey Wolf algorithm and particle swarm algorithm,respectively.In scenarios with a high number of base stations,the coverage of the improved algorithm is increased by 5.4%and 1.9%compared to the traditional Grey Wolf algorithm and particle swarm algorithm,respectively.

关 键 词：灰狼算法 部署优化 微基站 热点区域 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]