Xiang Zhang, F. Zhou, Jiayi Ning, Peng Yu, Wenjing Li
{"title":"Hotspot localization and prediction in wireless cellular networks via spatial traffic fitting","authors":"Xiang Zhang, F. Zhou, Jiayi Ning, Peng Yu, Wenjing Li","doi":"10.1109/NOMS.2018.8406292","DOIUrl":null,"url":null,"abstract":"With the proliferation of bandwidth-demanding mobile applications in the era of 5G, the aggregation of a few users may lead to extremely high load in cellular base stations, producing traffic hotspot in wireless networks. Therefore the higher requirement is imposed on the flexibility of a 5G network, namely the capability of performing rapid capacity enhancement in hotspot area, which makes hotspot localization and critical prediction functions. In this paper, we proposed to localize hotspots with Gaussian Random Field (GRF)-based spatial traffic density model deduced from load data of base stations, together with the prediction with Holt-Winters. We measured the spatial traffic in a specific area within a short time span and forecasted the spatial traffic density distribution. Numeric results show the proposed approach can localize hotspot efficiently, and during traffic peak hours, hotspot prediction is of high success rate.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":"9 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NOMS.2018.8406292","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
With the proliferation of bandwidth-demanding mobile applications in the era of 5G, the aggregation of a few users may lead to extremely high load in cellular base stations, producing traffic hotspot in wireless networks. Therefore the higher requirement is imposed on the flexibility of a 5G network, namely the capability of performing rapid capacity enhancement in hotspot area, which makes hotspot localization and critical prediction functions. In this paper, we proposed to localize hotspots with Gaussian Random Field (GRF)-based spatial traffic density model deduced from load data of base stations, together with the prediction with Holt-Winters. We measured the spatial traffic in a specific area within a short time span and forecasted the spatial traffic density distribution. Numeric results show the proposed approach can localize hotspot efficiently, and during traffic peak hours, hotspot prediction is of high success rate.
随着5G时代对带宽要求较高的移动应用的激增,少量用户的聚集可能会导致蜂窝基站的负载极高,在无线网络中产生流量热点。因此对5G网络的灵活性提出了更高的要求,即在热点区域进行快速扩容的能力,从而实现热点定位和关键预测功能。本文提出了基于高斯随机场(Gaussian Random Field, GRF)的空间交通密度模型,并结合Holt-Winters预测方法对热点区域进行定位。对某一特定区域在短时间内的空间交通流量进行测量,并对空间交通密度分布进行预测。数值结果表明,该方法能有效地定位热点,在交通高峰时段,热点预测成功率高。