Detecting cellular network anomalies using the knowledge discovery process
Published inJyväskylä studies in computing
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 capacity demands. The manual management of a multi-layer network is a very expensive, error prone, and sluggish process. Hence, the automation of the whole life cycle of network operation is highly anticipated. To this aim 3GPP introduces a self-management concept referred to as SON. It is envisioned that SON updates information concerning the latest network conditions through the MDT mecha- nism. MDT enables a network operator to collect radio and service quality measurements from regular mobile phones. Self-healing is SON’s functionality that implements fault management in radio networks. The automated and timely detection of a malfunctioning cell is one of the crucial challenges for network operators. The thesis investigates the topic of self-organizing radio networks and proposes a cell outage detection framework based on MDT measurements and advanced data mining techniques. The sequential analysis of LTE network events underlies the proposed idea. The conducted research demonstrates the feasibility of the original idea and designs the KDD process for the automated analysis of cell failures. The second part of the study improves the computational complexity and performance of the proposed solution. Besides, the research discovers the impact of location accuracy and scarcity of MDT measurements on the quality of cell outage detection. The validation of the framework has been conducted on the state-of-the-art LTE/LTE-A system level simulator. Results demonstrate reliable and timely detection of a malfunctioning cell. Therefore, the developed cell outage detection solution can be considered for the practical validation and implementation. ...
PublisherUniversity of Jyväskylä
- Article I: Fedor Chernogorov, Tapani Ristaniemi, Kimmo Brigatti, Sergey Chernov. N-gram Analysis for Sleeping Cell Detection in LTE Networks. 38th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada, 2013. DOI: 10.1109/ICASSP.2013.6638499
- Article II: Sergey Chernov, Fedor Chernogorov, Dmitry Petrov, Tapani Ristaniemi. Data Mining Framework for Random Access Failure Detection in LTE Networks. 25th IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), Washington DC, USA, 2014. DOI: 10.1109/PIMRC.2014.7136373
- Article III: Sergey Chernov, Michael Cochez, Tapani Ristaniemi. Anomaly Detection Algorithms for the Sleeping Cell Detection in LTE Networks. 81st IEEE Vehicular Technology Conference (VTC) Spring, Glasgow, Scotland, 2015. DOI: 10.1109/VTCSpring.2015.7145707
- Article IV:Sergey Chernov, Dmitry Petrov, Tapani Ristaniemi. Location Accuracy Impact on Cell Outage Detection in LTE-A Networks. 11th IEEE International Wireless Communications & Mobile Computing Conference (IWCMC), Dubrovnik, Croatia, 2015.DOI: 10.1109/IWCMC.2015.7289247
- Article V: Fedor Chernogorov, Sergey Chernov, Kimmo Brigatti, Tapani Ristaniemi. Sequence-based Detection of Sleeping Cell Failures in Mobile Networks. Wireless Networks: DOI: 10.1007/s11276-015-1087-9
- Article VI: Sergey Chernov, Mykola Pechenizkiy, Tapani Ristaniemi. The Influence of Dataset Size on the Performance of Cell Outage Detection Approach in LTE-A Networks. 10th IEEE International Conference on Information, Communications and Signal Processing (ICICS), Singapore, 2015.
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