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Location Aware Analytics in the Context of Mobile Network Performance Optimization
Urbanová, Lucie ; Miloš, Jiří (oponent) ; Slanina, Martin (vedoucí práce)
This thesis deals with the location aware analytics in the context of mobile network performance optimization. A tool which estimates initial network parameters in the location with unknown network performance based on RTR NetTest measurements database is presented. The thesis briefly introduces the topic of big data and machine learning and gives an overview of NetTest application concept and functionality. A set of regression methods is presented and their complexity and suitability for the purposes of coverage maps creation is compared. After their thorough 1D analysis, IDW and GPR are analysed in 2D and used to create a set of estimation maps of network parameters. Evaluation of their accuracy is made based on reference measurements using NetTest application.
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Big data analytics in the context of mobile network performance optimization
Klus, Roman ; Miloš, Jiří (oponent) ; Slanina, Martin (vedoucí práce)
This thesis focuses on big data techniques in the context of network performance measurement. The topic of big data and its utilization is being presented, followed by the research on basic network parameters, their measurement and evaluation methods. The RTR NetTest application, testing procedure and measured parameters are being evaluated. A set of tools was created for estimating the basic quantitative parameters of a mobile network on the basis of RTR dataset analysis. Time-of-Day Effect study concludes the time variance of the network. The spatial behaviour is evaluated by applying binning and clustering algorithms and in the end the comparison of drive test and crowdsourcing is provided.
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Location Aware Analytics in the Context of Mobile Network Performance Optimization
Urbanová, Lucie ; Miloš, Jiří (oponent) ; Slanina, Martin (vedoucí práce)
This thesis deals with the location aware analytics in the context of mobile network performance optimization. A tool which estimates initial network parameters in the location with unknown network performance based on RTR NetTest measurements database is presented. The thesis briefly introduces the topic of big data and machine learning and gives an overview of NetTest application concept and functionality. A set of regression methods is presented and their complexity and suitability for the purposes of coverage maps creation is compared. After their thorough 1D analysis, IDW and GPR are analysed in 2D and used to create a set of estimation maps of network parameters. Evaluation of their accuracy is made based on reference measurements using NetTest application.
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Big data analytics in the context of mobile network performance optimization
Klus, Roman ; Miloš, Jiří (oponent) ; Slanina, Martin (vedoucí práce)
This thesis focuses on big data techniques in the context of network performance measurement. The topic of big data and its utilization is being presented, followed by the research on basic network parameters, their measurement and evaluation methods. The RTR NetTest application, testing procedure and measured parameters are being evaluated. A set of tools was created for estimating the basic quantitative parameters of a mobile network on the basis of RTR dataset analysis. Time-of-Day Effect study concludes the time variance of the network. The spatial behaviour is evaluated by applying binning and clustering algorithms and in the end the comparison of drive test and crowdsourcing is provided.
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