计算机应用 ›› 2017, Vol. 37 ›› Issue (1): 97-102.DOI: 10.11772/j.issn.1001-9081.2017.01.0097

• 2016年全国开放式分布与并行计算学术年会(DPCS2016)论文 • 上一篇    下一篇

长期演进网络中基于粒子群的天线下倾角自优化方法

连晓灿, 张彭园, 谭国平, 李岳衡   

  1. 河海大学 通信与信息系统研究所, 南京 211100
  • 收稿日期:2016-08-21 修回日期:2016-09-11 出版日期:2017-01-10 发布日期:2017-01-09
  • 通讯作者: 谭国平
  • 作者简介:连晓灿(1992-),女,福建泉州人,硕士研究生,主要研究方向:移动自组网;张彭园(1991-),男,江苏徐州人,硕士,主要研究方向:LTE有源天线技术;谭国平(1975-),男,湖南澧县人,副教授,博士,CCF会员,主要研究方向:移动自组网、无线多媒体通信、随机网络优化与控制、网络信息论;李岳衡(1971-),男,湖南永兴人,教授,博士,主要研究方向:移动通信中的多天线传输理论与技术、现代无线传感网络协同信息获取与处理。
  • 基金资助:
    中国科学院上海微系统与信息技术研究所无线传感网与通信重点实验室开放课题资助项目(2016001);中央高校基本科研业务费专项资金资助项目(2015B18914)。

Antenna down-tilt angle self-optimization method based on particle swarm in long term evolution network

LIAN Xiaocan, ZHANG Pengyuan, TAN Guoping, LI Yueheng   

  1. Communication and Information Systems Institute, Hohai University, Nanjing Jiangsu 211100, China
  • Received:2016-08-21 Revised:2016-09-11 Online:2017-01-10 Published:2017-01-09
  • Supported by:
    This work is partially supported by the Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences (2016001), the Fundamental Research Funds for the Central Universities (2015B18914).

摘要: 针对第三代合作伙伴项目(3GPP)中自组织网络(SON)的覆盖与容量自优化问题,提出了一种基于粒子群优化(PSO)算法的有源天线下倾角优化方法。首先,确定基站(eNB)中传输数据的用户设备(UE)数,用户测量上报邻小区参考信号接收功率(RSRP)信息和位置信息;然后,确定优化目标预设适应度评价函数为频谱效率(SE);其次,将下倾角同时优化问题看作是多维优化问题,选择天线下倾角为粒子集合,使用PSO算法求解得到天线下倾角的最优值;最后,通过系统自主调整优化下倾角,实现长期演进(LTE)网络中容量及覆盖的自优化。通过建模及仿真结果分析,此算法在优化目标不同时可以取得不同的优化效果:优化目标为用户平均频谱效率时,采用传统黄金分割优化算法频谱效率较初始设定提升12.9%,采用PSO算法可提升22.5%;调整优化目标为用户加权平均频谱效率时,对边缘用户,传统黄金分割优化算法并无明显提升,PSO算法取得了19.3%的优化提升。实验结果表明,该方法可提升用户吞吐量,改善系统性能。

关键词: 长期演进, 下倾角优化, 自组织网络, 容量与覆盖优化, 粒子群优化

Abstract: To solve the coverage and capacity optimization problem of Self-Organizing Network (SON) in the 3rd Generation Partnership Project (3GPP), an active antenna down-tilt angle optimization method based on Particle Swarm Optimization (PSO) algorithm was proposed. First, the number of User Equipments (UE) served by evolved Node B (eNB) was determined, and the Reference Signal Received Power (RSRP) and position measured from the UE were obtained. Second, the Spectral Efficiency (SE) was regarded as the fitness function which defined by optimization goals. Then, down-tilt angle optimization was regarded as multidimensional optimization problem, and antenna down-tilt angle was regarded as the set of particles to resolve the optimal value by the PSO algorithm. Finally, the capacity and coverage self-optimization of Long Term Evolution (LTE) networks was achieved by adjusting down-tilt angle independently. By simulations, different objective functions made different optimization results. When the average spectrum efficiency was set as the optimization goal, the spectral efficiency of traditional golden section algorithm increased by 12.9% than primary settings, the spectral efficiency of PSO was increased by 22.5%. When the weighted average spectral efficiency was set as the optimization goal, the spectral efficiency of golden section algorithm was not significantly improved but that of PSO was increased by 19.3% for edge users. The experimental results show that the proposed method improves cell throughput and system performance.

Key words: Long Term Evolution (LTE), down-tilt angle optimization, Self-Organizing Network (SON), coverage and capacity optimization, Particle Swarm Optimization (PSO)

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