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Forecasting WiMAX BS Traffic by Statistical Processing in the Wavelet Domain

Forecasting WiMAX BS Traffic by Statistical Processing in the Wavelet Domain . Cristina Stolojescu 1 , Alina Cu ș nir 2 , Sorin Moga 3 , Alexandru Isar 1 1 Politehnica University, Timisoara, Romania, 2 Alcatel-Lucent, Timisoara, Romania, 3 Telecom Bretagne, Brest, France. Goal.

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Forecasting WiMAX BS Traffic by Statistical Processing in the Wavelet Domain

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  1. Forecasting WiMAX BS Traffic by Statistical Processing in the Wavelet Domain Cristina Stolojescu1, Alina Cușnir2, Sorin Moga3, Alexandru Isar1 1 Politehnica University, Timisoara, Romania, 2 Alcatel-Lucent, Timisoara, Romania, 3Telecom Bretagne, Brest, France. ISSCS 2009, Iasi, Romania

  2. Goal • predict where and when BS upgrading must take place in a WiMAX network • statistical data processing in the wavelets domain ISSCS 2009, Iasi, Romania

  3. Papagiannaki & alls, 2003 Wire network 1.5 years of data with 15 minutesgranularity ISSCS 2009, Iasi, Romania

  4. Pros & cons • Potential of generalization • Accurate forecasting • High volume of data required • Stationary network ISSCS 2009, Iasi, Romania

  5. Proposed Method Wireless network containing 64 BSs, 11 weeks of data with 15 minutes granularity. ISSCS 2009, Iasi, Romania

  6. Initial Observations The weekly traffic for a BS (arbitrarily selected). The corresponding power spectral density. Analyzing the first week of the considered period for all the 64 BSs we have found a periodicity of 24 hours in 77% of cases. ISSCS 2009, Iasi, Romania

  7. MRA c6 – long term trend d3 and d4 – variability. ISSCS 2009, Iasi, Romania

  8. ANOVA ISSCS 2009, Iasi, Romania

  9. Validation ISSCS 2009, Iasi, Romania

  10. Parameters Extraction ISSCS 2009, Iasi, Romania

  11. ARIMA MODELING ISSCS 2009, Iasi, Romania

  12. Long Term Trend Estimation Applying the Box-Jenkins methodology for the first difference of the time series c6 in our example we have obtained an ARIMA(011) model for the overall tendency: ISSCS 2009, Iasi, Romania

  13. Where and when an upgrading must take place ? ISSCS 2009, Iasi, Romania

  14. Saturation Risk ISSCS 2009, Iasi, Romania

  15. Conclusions • The proposed methodology is capable to isolate the overall long term trend and to identify those components that significantly contribute to its variability. • Predictions based on approximations of those components provide accurate estimates with a minimal computational overhead. ISSCS 2009, Iasi, Romania

  16. Metcalfe’s Law “The value of any communication network grows as the square of the number of users of the network” Andrew J. Viterbi, Four Laws of Nature and Society: The Governing Principles of Digital Wireless Communication Networks, in: H. Vincent POOR, Gregory W. WORNELL, Wireless Communications-Signal Processing Perspectives, Prentice Hall, 1998 ISSCS 2009, Iasi, Romania

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