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Quadri-dimensional approach for data analytics in mobile networks

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dc.contributor.advisor Sumbwanyambe. M. Minerve, Mampaka Maluambanzila 2019-10-21T05:35:51Z 2019-10-21T05:35:51Z 2018-10
dc.description.abstract The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data. While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation. The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms. en
dc.format.extent 1 online resource (xiv, 91 leaves) : color illustrations, color graphs en
dc.language.iso en en
dc.subject Telecommunication en
dc.subject Mobile networks en
dc.subject Packet-Switched en
dc.subject QoS en
dc.subject QoE en
dc.subject SQM en
dc.subject CEM en
dc.subject Root cause analysis en
dc.subject Data analytics en
dc.subject Big Data en
dc.subject Machine learning en
dc.subject Artificial intelligence en
dc.subject ANN en
dc.subject Deep learning en
dc.subject.ddc 621.382
dc.subject.lcsh Network performance (Telecommunication) -- Reliability en
dc.subject.lcsh Big data en
dc.subject.lcsh Computer algorithms en
dc.subject.lcsh Data mining -- Computer programs en
dc.subject.lcsh Mobile computing en
dc.subject.lcsh Cell phone systems en
dc.subject.lcsh Mobile communication systems en
dc.subject.lcsh Wireless communication systems en
dc.subject.lcsh Reliability (Engineering) en
dc.title Quadri-dimensional approach for data analytics in mobile networks en
dc.type Dissertation en
dc.description.department Electrical and Mining Engineering en M. Tech. (Electrical Engineering) en

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