Analysing 3G radio network performance with fuzzy methods
| Autor: | Mika Särkioja, Kimmo Hätönen, Mikko Kylväjä, Pekka Kumpulainen |
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| Rok vydání: | 2013 |
| Předmět: |
Fuzzy clustering
business.industry Computer science Cognitive Neuroscience Network monitoring computer.software_genre Machine learning Telecommunications network Fuzzy logic Computer Science Applications Network element Artificial Intelligence Anomaly detection Network performance Artificial intelligence Data mining business computer Communication channel |
| Zdroj: | Neurocomputing. 107:49-58 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2012.07.033 |
| Popis: | In comparison to the earlier telecommunications networks, present-day 3rd generation (3G) networks are able to provide more complex and detailed performance data, such as distributions of channel quality indicators. However, the operators lack proper methods and tools to efficiently utilize these data in monitoring and analysis of the networks. In this article, we apply fuzzy computing to channel quality measurement distributions to get the network elements (cells) clustered into groups of similar behavior. Groups and their descriptors provide valuable information for a radio expert, who is responsible for hundreds or thousands of elements. We introduce a fuzzy inference system based on features extracted from the distributional data and provide interpretation of the found categories to demonstrate their usability on network monitoring. Additionally we present how fuzzy clustering can be used in network performance monitoring and anomaly detection. Finally, we introduce further analysis on how time dimension is an interesting perspective to analyze network element behavior. All the achieved results were discussed with radio network performance experts who found them informative and useful. |
| Databáze: | OpenAIRE |
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