{"title":"Performance Analysis in Wireless HetNets: Traffic, Energy, and Secrecy Considerations","authors":"Georgios Smpokos","doi":"10.3384/LIC.DIVA-174244","DOIUrl":null,"url":null,"abstract":"To this day, most of the communication networks are characterized by a “monolithic” operating approach. Network elements are configured and operate without any reconfiguration for long time periods. Softwarization, whereby dedicated elements are being replaced by more general-purpose devices, has been lately challenging this existing approach. Virtualizing the infrastructure through the softwarization can provide significant benefits to end users and operators, supporting more flexible service deployment, providing real time monitoring and operational changes. In this licentiate thesis, we consider techniques that can be used towards virtual networking. In Paper I we study a novel allocation technique and traffic optimization process for the access network. Cellular network technologies (i.e. UMTS, LTE, LTE-A) will coexist with non-cellular small cells and offload traffic from cellular to non-cellular networks mainly operating in 3GPP Wi-Fi (IEEE 802.11 standards). This is a scenario for indoor wireless access implementations where offloading mechanisms can improve the QoS offered by the operators, and reduce the traffic handled by the access fronthaul. The analysis of a novel optimization algorithm exhibited a holistic solution for access-core interworking where LWA (LTE-WiFi Aggregation) offers improved performance for the end users. analysed how can affect the power consumption of core network data centers (cooling systems). By applying machine learning techniques using data from a data center, we were able to forecast the power consumption based on to atmospheric weather conditions and analyse its accuracy.","PeriodicalId":303036,"journal":{"name":"Linköping Studies in Science and Technology. Licentiate Thesis","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Linköping Studies in Science and Technology. Licentiate Thesis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3384/LIC.DIVA-174244","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
To this day, most of the communication networks are characterized by a “monolithic” operating approach. Network elements are configured and operate without any reconfiguration for long time periods. Softwarization, whereby dedicated elements are being replaced by more general-purpose devices, has been lately challenging this existing approach. Virtualizing the infrastructure through the softwarization can provide significant benefits to end users and operators, supporting more flexible service deployment, providing real time monitoring and operational changes. In this licentiate thesis, we consider techniques that can be used towards virtual networking. In Paper I we study a novel allocation technique and traffic optimization process for the access network. Cellular network technologies (i.e. UMTS, LTE, LTE-A) will coexist with non-cellular small cells and offload traffic from cellular to non-cellular networks mainly operating in 3GPP Wi-Fi (IEEE 802.11 standards). This is a scenario for indoor wireless access implementations where offloading mechanisms can improve the QoS offered by the operators, and reduce the traffic handled by the access fronthaul. The analysis of a novel optimization algorithm exhibited a holistic solution for access-core interworking where LWA (LTE-WiFi Aggregation) offers improved performance for the end users. analysed how can affect the power consumption of core network data centers (cooling systems). By applying machine learning techniques using data from a data center, we were able to forecast the power consumption based on to atmospheric weather conditions and analyse its accuracy.