A method using time series analysis for IEEE 802.11 WLANs channel forecasting

ABSTRACT

The growth of wireless network use has greatly increased research demand. Some applications, which are context-aware, must adapt to the environment. So, information on both environment characteristics and the device's hardware are crucial. In this work, a new method called Natural Adaptive Exponential Smoothing (NAES) is proposed. It describes and forecasts, in real time, IEEE 802.11 WLAN networks channel behavior. The NAES method is a variation of the exponential smoothing technique to compute the channel quality indicators, namely the Received Signal Strength (RSS) and the link quality. A comparison with the results obtained by the Trigg and Leach (TL) method shows that NAES outperforms TL method.

REFERENCES

• 1: N. S. Yoshikazu Akinaga, Shigeru Kaneda and A. Miura, "A Proposal for a Mobile Communication Traffic Forecasting Method Using Time-series Analysis for Multi-variate Data," in Globecom, 2005.
• 2: C. Na, J. Chen, and T. Rappaport, "Hotspot Traffic Statistics and Throughput Model for Several Applications," in Global Telecommunications Conference, 2004. GLOBECOM'04. IEEE, 2004.
• 3: M. A. C. Jorge L. C. e Silva and P. R. F. Cunha, "ProgCon: um Sistema para Prognóstico de Congestionamento de Tráfego de Redes," in SBRC2005.
• 4: K. L. Dias, S. F. L. Fernandes, and D. F. H. Sadok, "Controle de Admissaão e Reserva de Recursos em Redes Móveis Utilizando Previsão de Séries Temporais," in SBRC2004, 2004.
• 5: D. W. Trigg and A. G. Leach, "Exponential Smoothing with an Adaptive Response Rate," no. 1, pp. 53--59, mar 1967.
• 6: R. P. C. Ricardo A. Rabelo Oliveira and A. A. F. Loureiro, "Monitora-mento de Eventos em Redes Sem Fio Utilizando EWMA e Wavelets," in SBRC 2005, maio.
• 7: M. A. A. A. Y. A. Rehim, "Horus: A WLAN-Based Indoor Location Determination System," Ph.D. dissertation, University of Maryland, College Park, 2004.
• 8: George Edward Pelham Box , Gwilym Jenkins, Time Series Analysis, Forecasting and Control, Holden-Day, Incorporated, 1990
• 9: P. A. Morettin and C. M. C. Toloi, Análise de Séries Temporais. BLÜCHER, 2004.
• 10: S. C. Wheelwright and S. Makridakis, Forecasting Methods for Management. N.Y, Wiley, 1985.
• 11: J. Tourrilhes, "Wireless tools for linux," Available on: http://www.hpl.hp.com/personal/Jean_Tourrilhes/Linux/Tools.html, 2007, release March, 27th 2007.
• 12: Theodore Rappaport, Wireless Communications: Principles and Practice, Prentice Hall PTR, Upper Saddle River, NJ, 2001
• 13: G. Box and M. Muller, "A Note on the Generation of Random Normal Deviates," The Annals of Mathematical Statistics, vol. 29, no. 2, pp. 610--611, 1958.
• 14: E. Bradley, "Time-series Analysis," Intelligent Data Analysis: An Introduction. Springer, 2000.

INDEX TERMS

Keywords:
WLAN, exponential smoothing, forecasting