基于改进蝙蝠算法的电力无线专网室内覆盖优化策略研究
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摘要
随着电力无线专网建设的不断深入,深度覆盖问题日益凸显。基站位置及数量的合理规划可满足大部分室外环境的信号覆盖,但仍有部分终端深处室内,需辅以适当的深度覆盖系统及优化策略。为了获得室内全覆盖、低成本、低辐射的无线网络,利用异构微蜂窝系统完成室内数据采集并上传基站,提出基于改进蝙蝠算法的深度覆盖优化策略。针对蝙蝠算法中个体缺乏交互、收敛速度与精度制约等问题,引入遗传算法对原有算法进行改进,以加强个体间的交互能力,利用信息交叉变异提高算法的搜索速度。仿真结果表明,该算法可有效优化多目标的室内无线网络规划。
With the continuous development of the electric wireless private network,the problem of deep coverage has become increasingly prominent. Reasonable location and number planning of the base stations can meet the signal coverage of most outdoor environments,but some of the terminals are in deep rooms. So it needs to be supplemented with appropriate depth coverage systems and optimization strategies. In order to obtain a full coverage,low cost and low radiation wireless network,this paper uses the heterogeneous femtocell system to complete the indoor data acquisition and upload it to the base station. In addition,an deep coverage optimization strategy based on the improved bat algorithm is proposed. There are some problems with the bat algorithm,such as lack of interaction between individuals,and the convergence speed and precision are mutually constrained. The genetic algorithm is introduced to improve the original algorithm to enhance the interaction ability,and the information crossover and mutation are used to improve the search speed. The simulation results show that the algorithm can effectively optimize multi-objective indoor wireless network planning.
With the continuous development of the electric wireless private network,the problem of deep coverage has become increasingly prominent. Reasonable location and number planning of the base stations can meet the signal coverage of most outdoor environments,but some of the terminals are in deep rooms. So it needs to be supplemented with appropriate depth coverage systems and optimization strategies. In order to obtain a full coverage,low cost and low radiation wireless network,this paper uses the heterogeneous femtocell system to complete the indoor data acquisition and upload it to the base station. In addition,an deep coverage optimization strategy based on the improved bat algorithm is proposed. There are some problems with the bat algorithm,such as lack of interaction between individuals,and the convergence speed and precision are mutually constrained. The genetic algorithm is introduced to improve the original algorithm to enhance the interaction ability,and the information crossover and mutation are used to improve the search speed. The simulation results show that the algorithm can effectively optimize multi-objective indoor wireless network planning.
引文
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[2]高志远,曹阳,黄海峰,等.支撑智慧城市的智能电网服务接口设计[J].供用电,2015,32(7):33-37.GAO Zhiyuan,CAO Yang,HUANG Haifeng,et al.Design of smart grid service interface supporting smart city[J].Distribution&Utilization,2015,32(7):33-37.
[3]毛永泉,周子冠,宋彦斌,等.基于LTE-230 MHz无线专网的用电信息采集技术研究[J].供用电,2015,32(12):1-7.MAO Yongquan,ZHOU Ziguan,SONG Yanbin,et al.Study on power consumption information collection technology based on LTE-230 MHz wireless private[J].Distribution&Utilization,2015,32(12):1-7.
[4]韦磊,缪巍巍,赵新建.配电自动化通信技术选择及建设方案[J].供用电,2014,31(5):38-42,4.WEI Lei,MIAO Weiwei,ZHAO Xinjian.The choice of communication technology and construction scheme of DA[J].Distribution&Utilization,2014,31(5):38-42,4.
[5]卞恒坤.TD-LTE深度覆盖解决方案研究[D].南京:南京邮电大学,2018.
[6]DING S,HANG J,WEI B,et al.Modelling of supercapacitors based on SVM and PSO algorithms[J].Iet Electric Power Applications,2018,12(4):502-507.
[7]NIU Y,LI Y,JIN D P,et al.A survey of millimeter wave communications(mmWave)for 5G:opportunities and challenges[J].Wireless Networks,2015,21(8):2657-2676.
[8]KOUTITAS G,SAMARAS T.Exposure minimization in indoor wireless networks[J].IEEE Antennas and Wireless Propagation Letters,2010(9):199-202.
[9]陈晓科.TD-LTE230电力营销宽带无线专网系统设计研究[D].北京:华北电力大学,2017.
[10]王勇.面向智能电网的通信数据动态处理研究与实现[D].成都:电子科技大学,2016.
[11]刘宁,秦志光,David Piets,等.基于混合算法的多目标异构无线网络室内规划[J].计算机应用研究,2016,33(9):2774-2777.LIU Ning,QIN Zhiguang,PIETS D,et al.Multi-objective indoor heterogeneous wireless network planning based on hybrid algorithm[J].Application Research of Computers,2016,33(9):2774-2777.
[12]LIU N,PLETS D,GOUDOS S K,et al.Multi-objective network planning optimization algorithm:human exposure,power consumption,cost,and capacity[J].Wireless Networks,2015,21(3):841-857.
[13]刘宁.室内无线网络规划优化[D].成都:电子科技大学,2015.
[14]耿艳香,张立毅,孙云山,.基于RNA遗传操作的改进蝙蝠算法[J].天津大学学报,2019,52(3):315-320.GENG Yanxiang,ZHANG Liyi,SUN Yunshan,et al.Improved bat algorithm based on RNA genetic algorithm[J].Journal of Tianjin University,2019,52(3):315-320.
[15]杨晓琴.基于改进蝙蝠算法的软件缺陷预测模型[J].计算机技术与发展,2018,28(12):74-78.YANG Xiaoqin.Software defect prediction model based on improved bat algorithm[J].Computer Technology and Development,2018,28(12):74-78.
[16]ALOMARI O A,KHADER A T,AL-BETAR M A,et al.A novel gene selection method using modified MRMR and hybrid bat-inspired algorithm withβ-Hill climbing[J].Applied Intelligence,2018,48(11):4429-4447.
[17]张程,金涛,李培强,等.采用多目标蝙蝠算法的电力系统广域协调控制策略[J].浙江大学学报(工学版),2019,53(3):589-597.ZHANG Cheng,JIN Tao,LI Peiqiang,et al.Wide-area coordination control strategy for power system using multiobjective bat algorithm[J].Journal of Zhejiang University(Engineering Science),2019,53(3):589-597.
[18]邵泽林.基于LTE系统的室内覆盖预测相关研究[D].北京:北京邮电大学,2017.
[19]AZHARI S V,ERCETIN O,DAEI M H,et al.Delay sensitive resource allocation over high speed IEEE802.11wireless LANs[J].Wireless Networks,2018(1):1-20.
[20]STANIEC K,KOWAl M.Measurement evaluation of the Tgn radio channel models usefulness in predicting WLANperformance[J].Progress in Electromagnetics Research,2013(137):311-333.