This thesis is based on the research of time series analysis. Our work evaluates a set of time series conceived by monitoring the traffic developed in a WiMAX network. Taking into consideration the high volume of information contained in this database, a data-mining approach was preferred. Assuming that the traffic associated with a BS bad positioned is heavier than the traffic associated with a BS well positioned, two approaches for the appreciation of the heaviness of the traffic were developed. The first approach is based on the supposition that a BS with heavy traffic has a reduced risk of saturation. Hence, it is necessary to appreciate the risk of saturation of each BS. So, the first objective of this thesis is to propose an approach for predicting time series. It is based on a multiple resolution decomposition of the signal using the Stationary Wavelet Transform and ARIMA modeling. The second approach for the appreciation of the heaviness of the traffic is based on Long Range Dependence analysis. The estimation of LRD degree is realized through the estimation of the Hurst parameter of the time-series under analysis. Our objective is to analyze the positioning of BSs in the architecture of a WiMAX network. The results show which BSs have a good localization and which BSs have a bad localization in the topology of the network and must be repositioned when the next session of network maintenance will take place. The application of both data mining techniques, forecasting and LRD analysis, in the wavelets domain is decisive for their performance, improving the speed and the precision of the developed algorithms.
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-00719668 |
| Date | 13 January 2012 |
| Creators | STOLOJESCU, Cristina Laura |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | English |
| Detected Language | English |
| Type | PhD thesis |
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