{"title":"Geometry-based stochastic channel models for 5G: Extending key features for massive MIMO","authors":"À. Martínez, P. Eggers, E. Carvalho","doi":"10.1109/PIMRC.2016.7794648","DOIUrl":null,"url":null,"abstract":"This paper introduces three key features in geometry-based stochastic channel models in order to include massive MIMO channels. Those key features consists of multiuser (MU) consistency, non-stationarities across the base station array and inclusion of spherical wave modelling. To ensure MU consistency, we introduce the concept of “user aura”, which is a circle around the user with radius defined according to the stationarity interval. The overlap between auras determines the share of common clusters among users. To model non-stationarities across a massive array, sub-arrays are defined for which clusters are independently generated. At last, we describe a procedure to incorporate spherical wave modelling, where a cluster focal point is defined to account for distance between user and cluster.","PeriodicalId":137845,"journal":{"name":"2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PIMRC.2016.7794648","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23
Abstract
This paper introduces three key features in geometry-based stochastic channel models in order to include massive MIMO channels. Those key features consists of multiuser (MU) consistency, non-stationarities across the base station array and inclusion of spherical wave modelling. To ensure MU consistency, we introduce the concept of “user aura”, which is a circle around the user with radius defined according to the stationarity interval. The overlap between auras determines the share of common clusters among users. To model non-stationarities across a massive array, sub-arrays are defined for which clusters are independently generated. At last, we describe a procedure to incorporate spherical wave modelling, where a cluster focal point is defined to account for distance between user and cluster.