{"title":"Impact of SON function combinations on the KPI behaviour in realistic mobile network scenarios","authors":"S. Hahn, M. Schweins, T. Kürner","doi":"10.1109/WCNCW.2018.8368974","DOIUrl":null,"url":null,"abstract":"Self-Organising Network (SON) functions cover a plethora of use cases, each addressing a different (self-optimisation) task reducing the manual work of the Mobile Network Operator (MNO). A combined SON operation, with numerous SON functions running in parallel, might lead to an undesired network performance due to unforeseen coherencies of parameter changes. In order to gain a greater understanding of the interworking of SON functions, well-known SON use cases are tested with different combinations in a realistic multi-layer, multi-RAT scenario. Results show that a substantial impact on the network performance can be observed for different SON function combinations. Furthermore, the effects also differ if multiple context classes of cells (i.e. different sizes, locations, mobility types) are considered.","PeriodicalId":122391,"journal":{"name":"2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCNCW.2018.8368974","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
Self-Organising Network (SON) functions cover a plethora of use cases, each addressing a different (self-optimisation) task reducing the manual work of the Mobile Network Operator (MNO). A combined SON operation, with numerous SON functions running in parallel, might lead to an undesired network performance due to unforeseen coherencies of parameter changes. In order to gain a greater understanding of the interworking of SON functions, well-known SON use cases are tested with different combinations in a realistic multi-layer, multi-RAT scenario. Results show that a substantial impact on the network performance can be observed for different SON function combinations. Furthermore, the effects also differ if multiple context classes of cells (i.e. different sizes, locations, mobility types) are considered.