Advanced performance monitoring for self-healing cellular mobile networks
Published inJyväskylä studies in computing
This dissertation is devoted to development and validation of advanced per- formance monitoring system for existing and future cellular mobile networks. Knowledge mining techniques are employed for analysis of user speciﬁc logs, collected with Minimization of Drive Tests (MDT) functionality. Ever increas- ing quality requirements, expansion of the mobile networks and their extend- ing heterogeneity, call for effective automatic means of performance monitoring. Nowadays, network operation is mostly controlled manually through aggregated key performance indicators and statistical proﬁles. These methods are are not able to fully address the dynamism and complexity of modern mobile networks. Self-organizing networks introduce automation to the most important network functions, but the opportunity of processing large arrays of user reported perfor- mance data is underutilized. Advanced performance monitoring system developed in the presented re- search considers both numerical and sequential properties of the MDT data for detection of faults. Network malfunctions analyzed in this study are sleeping cells in either physical or medium access layer. A full data mining cycle is em- ployed for identiﬁcation of problematic regions in the network. Pre-processing with statistical normalization and sliding window methods, both linear and non- linear transformation and dimensionality reduction algorithms, together with clustering and classiﬁcation methods are used in the discussed research. Sev- eral post-processing and detection quality evaluation methods are proposed and applied. The developed system is capable of fast and accurate detection of non- trivial network dysfunctions and is suitable for future mobile networks, even in combination with cognitive self-healing. As a result, operation of modern mo- bile networks would become more robust, increasing quality of service and user experience. ...
PublisherUniversity of Jyväskylä
- Article I: Fedor Chernogorov, Sergey Chernov, Kimmo Brigatti, Tapani Ristaniemi. Sequence-based Detection of Sleeping Cell Failures in Mobile Networks. Wireless Networks, The Journal of Mobile Communication, Computation and Information, 2015. (submitted for review, available on arxiv.org)
- Article II: Sergey Chernov, Fedor Chernogorov, Dmitry Petrov, Tapani Ristaniemi. Data Mining Framework for Random Access Failure Detection in LTE Networks. Proc. 25th IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), 2014. DOI: 10.1109/PIMRC.2014.7136373
- Article III: Fedor Chernogorov, Tapani Ristaniemi, Kimmo Brigatti, Sergey Chernov. N-gram analysis for sleeping cell detection in LTE networks. Proc. 39th IEEE International Conference on Acoustics, Speech and Signal Processing, 2013. DOI: 10.1109/ICASSP.2013.6638499
- Article IV: Fedor Chernogorov, Jussi Turkka, Tapani Ristaniemi, Amir Averbuch. Detection of Sleeping Cells in LTE Networks Using Diffusion Maps. Proc. 73rd IEEE Vehicular Technology Conference (VTC Spring), 2011. DOI: 10.1109/VETECS.2011.5956626
- Article V: Jussi Turkka, Fedor Chernogorov, Kimmo Brigatti, Tapani Ristaniemi, and Jukka Lempiäinen. An Approach for Network Outage Detection from Drive-Testing Databases. Journal of Computer Networks and Communications, Volume 2012 (2012), Article ID 163184. DOI: 10.1155/2012/163184
- Article VI: Fedor Chernogorov, Ilmari Repo, Vilho Räisänen, Timo Nihtilä, Janne Kurjenniemi. Cognitive Self-Healing for Future Mobile Networks. Proc. 11th IEEE International Wireless Communications & Mobile Computing Conference (IWCMC), 2015
quality and performance management knowledge mining performance monitoring self-organizing networks data mining anomaly detection sleeping cell sequence-based analysis cellular mobile networks tiedonlouhinta monitorointi sekvensointi häiriöt toimintahäiriöt tietoliikenneverkot matkaviestinverkot rakenteettomat verkot
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Chernov, Sergey (University of Jyväskylä, 2015)Analytical companies unanimously forecast the exponential growth of mobile trafﬁc consumption over the next ﬁve years. The densiﬁcation of a network structure with small cells is regarded as a key solution to meet growing ...
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