期刊文献+

Modified particle swarm optimization-based antenna tilt angle adjusting scheme for LTE coverage optimization 被引量:5

LTE网络覆盖优化中一种基于改进粒子群的天线倾角调整的算法(英文)
下载PDF
导出
摘要 In order to solve the challenging coverage problem that the long term evolution( LTE) networks are facing, a coverage optimization scheme by adjusting the antenna tilt angle( ATA) of evolved Node B( e NB) is proposed based on the modified particle swarm optimization( MPSO) algorithm.The number of mobile stations( MSs) served by e NBs, which is obtained based on the reference signal received power(RSRP) measured from the MS, is used as the metric for coverage optimization, and the coverage problem is optimized by maximizing the number of served MSs. In the MPSO algorithm, a swarm of particles known as the set of ATAs is available; the fitness function is defined as the total number of the served MSs; and the evolution velocity corresponds to the ATAs adjustment scale for each iteration cycle. Simulation results showthat compared with the fixed ATA, the number of served MSs by e NBs is significantly increased by 7. 2%, the quality of the received signal is considerably improved by 20 d Bm, and, particularly, the system throughput is also effectively increased by 55 Mbit / s. 为了解决LTE网络所面临的具有挑战性的覆盖问题,提出一种基于改进的粒子群优化(MPSO)的覆盖优化方案.该方案通过调整演进基站(e NB)的天线倾角(ATA)优化网络覆盖.e NB利用移动台(MS)测量到的参考信号接受功率(RSRP)判断自身服务的MS数目,并用服务的MS数目作为覆盖优化的评价指标,通过最大化服务MS的数量来优化覆盖.在MPSO算法中,存在一群可被看作是ATA集合的粒子,适应度函数定义为被服务的MS总数,每次迭代中的进化速度对应于ATA的调整尺度.仿真结果表明,与固定天线倾角相比,提出的算法使得e NB服务的MS数目增加7.2%,接收信号的质量提升20 d Bm,同时系统吞吐量提升55 Mbit/s.
出处 《Journal of Southeast University(English Edition)》 EI CAS 2015年第4期443-449,共7页 东南大学学报(英文版)
基金 The National High Technology Research and Development Program of China(863 Program)(No.2014AA01A702) the National Science and Technology Major Project(No.2013ZX03001032-004) the National Natural Science Foundation of China(No.61221002 61201170)
关键词 long term evolution(LTE) networks antenna tilt angle coverage optimization modified particle swarm optimization algorithm LTE网络 天线倾角 覆盖优化 改进粒子群优化算法
  • 相关文献

参考文献22

  • 1Jiang Y, Yu P, Li W, et al. Automated coverage optimi- zation scheme based on downtilt adjustment in wireless access networks[ C ] //lnternational Wireless Communica- tions and Mobile Computing (IWCMC). Limassol, Cy- prus, 2012 : 945 -948.
  • 2Naseer ul Islam M, Mitschele-Thiel A. Reinforcement learning strategies for self-organized coverage and capaci- ty optimization [ C ]//IEEE Wireless Communications and Networking Conference (WCNC). Shanghai, China, 2012 : 2818 - 2823.
  • 3Engels A, Reyer M, Xu X, et al. Autonomous self-opti- mization of coverage and capacity in LTE cellular net- works [ J ]. IEEE Transactions on Vehicular Technology, 2013, 62(5) : 1989-2004.
  • 4Berger S, Fehske A, Zanier P, et al. Online antenna tilt- based capacity and coverage optimization [ J] . IEEE Wireless Communications Letters, 2014, 3 (4) : 437 - 440.
  • 5Rouzbeh R, Siegfried K, Holger C. A fuzzy reinforce- ment learning approach for self-optimization of coverage in LTE networks [ J ]. Bell Labs Technical Journal, 2010, 15(3) : 153-175.
  • 6Luketic I, Simunic D, Blajic T. Optimization of cover- age and capacity of self-organizing network in LTE [ C ]//Proceedings of the 34th International Convention. Opatija, the Republic of Croatia, 2011:612-617.
  • 7Razavi R, Klein S, Claussen H. Self-optimization of ca- pacity and coverage in LTE networks using a fuzzy rein- forcement learning approach [ C ]//IEEE International Symposium on Personal Indoor and Mobile Radio Com- munications (PIMRC). Istanbul, Turkey, 2010: 1865- 1870.
  • 8Karvounas D, Vlacheas P, Georgakopoulos A, et al. An opportunistic approach for coverage and capacity optimi- zation in self-organizing networks [ C ]//Future Network and Mobile Summit. Lisboa, Portugal, 2013 : 1 - 10.
  • 9Combes R, Altman Z, Altman E. Self-organization in wireless networks: a flow-level perspective E C ]//Pro- ceedings of IEEE INFOCOM. Orlando, FL, USA, 2012: 2946 - 2950.
  • 10Gao M, Huang L, Cai H. Intelligent coverage optimiza- tion with multi objective genetic algorithm in cellular sys- tem [ C]//International Conference on Computer Science & Education (ICCSE). Colombo, Democratic Socialist Republic of Sri Lanka, 2013 : 859 - 863.

同被引文献15

引证文献5

二级引证文献13

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部