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Analyzing 3G Quality Distribution Data with Fuzzy Rules and Fuzzy Clustering

Abstract : The amount of data collected from telecommunications networks has increased significantly during the last decade. In comparison to the earlier networks, present-day 3G networks are able to provide more complex and detailed data, such as distributions of quality indicators. However, the operators lack proper tools to efficiently utilize these data in monitoring and analyzing the networks. Classification of the network elements (cells) into groups of similar behavior provides valuable information for a radio expert, who is responsible of hundreds or thousands of elements.In this paper we propose fuzzy methods applied to 3G network channel quality distributions for analyzing the network performance. We introduce a traditional fuzzy inference system based on features extracted from the distributional data. We provide interpretation of the resulting classes to demonstrate their usability on network monitoring. Constructing and maintaining fuzzy rule sets are laborious tasks, therefore there is a demand for data driven methods that can provide similar information to the experts. We apply fuzzy C-means clustering to create performance classes. Finally, we introduce further analysis on how the performance of individual network elements varies between the classes in the course of time.
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Pekka Kumpulainen, Mika Särkioja, Mikko Kylväjä, Kimmo Hätönen. Analyzing 3G Quality Distribution Data with Fuzzy Rules and Fuzzy Clustering. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.402-411, ⟨10.1007/978-3-642-23957-1_45⟩. ⟨hal-01571325⟩

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