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De Montera , Louis; Mallet , Cécile; Barthès , Laurent; Golé , Peter (2008)
Publisher: European Geosciences Union (EGU)
Languages: English
Types: Article
Subjects: Geophysics. Cosmic physics, Q, [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Science, Physics, QC1-999, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, QC801-809, [ SPI.ELEC ] Engineering Sciences [physics]/Electromagnetism
This paper shows how nonlinear models originally developed in the finance field can be used to predict rain attenuation level and volatility in Earth-to-Satellite links operating at the Extremely High Frequencies band (EHF, 20–50 GHz). A common approach to solving this problem is to consider that the prediction error corresponds only to scintillations, whose variance is assumed to be constant. Nevertheless, this assumption does not seem to be realistic because of the heteroscedasticity of error time series: the variance of the prediction error is found to be time-varying and has to be modeled. Since rain attenuation time series behave similarly to certain stocks or foreign exchange rates, a switching ARIMA/GARCH model was implemented. The originality of this model is that not only the attenuation level, but also the error conditional distribution are predicted. It allows an accurate upper-bound of the future attenuation to be estimated in real time that minimizes the cost of Fade Mitigation Techniques (FMT) and therefore enables the communication system to reach a high percentage of availability. The performance of the switching ARIMA/GARCH model was estimated using a measurement database of the Olympus satellite 20/30 GHz beacons and this model is shown to outperform significantly other existing models. <br><br> The model also includes frequency scaling from the downlink frequency to the uplink frequency. The attenuation effects (gases, clouds and rain) are first separated with a neural network and then scaled using specific scaling factors. As to the resulting uplink prediction error, the error contribution of the frequency scaling step is shown to be larger than that of the downlink prediction, indicating that further study should focus on improving the accuracy of the scaling factor.
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    • Baillie, R. T., Bollerslev, T., and Mikkelsen, H. O.: Fractionally Integrated Generalized Autoregressive Heteroscedasticity, J. Econometrics, 74, 3-30, 1996.
    • Barthes, L., Mallet, C., and Brisseau, O.: A neural network model for the separation of atmospheric effects on attenuation: Application to frequency scaling, Radio Sci., 41(4), 4012, doi:10.1029/2005RS003310, 2006.
    • Baxter, P. D., Upton, G. J. G., and Eden, D.: Measuring rain-fadeslope: objective evaluation of filtering techniques, Proceeding of the International Workshop of COST Actions 272 and 280, 67- 74, 2003.
    • Baxter, P. D. and Upton, G. J. G.: Differentiating noisy radio communications signals, wavelet estimation of a derivative in the presence of heteroskedastic noise, Appl. Statist., 54(4), 753-767, 2005.
    • Beguin, J.-M., Gourie´roux, C., and Monfort, A.: Identification of a mixed autoregressive-moving average process: The corner method, in: Time Series, edited by: Anderson, O. D., Amsterdam, pp. 423-436, 1980.
    • Bendjoudi, H., Hubert, P., Schertzer, D., and Lovejoy, S.: Interpretation multifractale des courbes intensite-duree-frequence des precipitations, Multifractal point of view on rainfall intensityduration-frequency curves, C. R. Acad. Sci. Paris, II, 325, pp. 323-326, 1997.
    • Bollerslev, T.: Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics 31, pp. 307-327, 1986.
    • Bollerslev, T. and Mikkelsen, H. O.: Modeling and pricing long memory in stock market volatility, J. Econometrics, 73, 151-184, 1996.
    • Box, G. E. P. and Jenkins, J. M.: Time series analysis: Forecasting and control, San Francisco, Holden-Day, 1976.
    • Bolea-Alaman˜ac, A., Bousquet, M., Castanet, L., and Van de Kamp, M. M. J. L.: Implementation of short-term prediction models in fade mitigation techniques control loops, COST 272/280 Workshop., Noordwijk, The Netherlands, ESA/ESTEC, ESTEC, PM5-067, 26-28 May 2003.
    • Castanet, L., Deloues, T., and Lemorton, J.: Channel modelling based on N-state Markov chains for SatCom systemes simulation, IEE conf. Pub., issue 49, vol. 1, pp. 119-112, ICAP 2003, Exceter, UK, 31 March-3 April 2003.
    • Chambers, A. P. and Otung, L. E.: Neural network approach to short-term fade prediction on satellite links, Electronics Lett., 41(23), 1990-1292, 10 November 2005.
    • Dissanayake, A., Allnutt, J., and Haidara, F.: A prediction model that combines rain attenuation and other propagation impairments along Earth-satellite paths, IEEE Trans. Antennas and Propagation, 45(10), 1546-1558, 1997.
    • Dossi, L.: Real-time prediction of attenuation for applications to fade countermeasures in satellite communications, Electronics Lett., 26(4), 250-251, 1990.
    • Engle, R.: Autoregressive conditional heteroscedasticity with estimates of the variance of UK inflation, Econometrica, 50, 987- 1008, 1982.
    • Ewing, B. T., Kruse, B. J., and Schroeder, J. L.: Time series analysis of wind speed with time-varying turbulence, Environmetrics, 17(2), 119-127, 2005.
    • Fiebig, U.-C.: A Time-Series Generator Modelling Rain Fading, Proc. Open Symposium on Propagation and Remote Sensing, URSI Commission F, Garmisch-Partenkirchen, 2002.
    • Garcia, P., Riera, J. M., and Benarroch, A.: Statistics of dry and wet scintillation in Madrid using Italsat 50 GHz beacon, COST 280, 1st International workshop, PM3013, July 2002.
    • Gibbins, C. J.: Improved algorithms for the determination of specific attenuation at sea level by dry air and water vapor, in the frequency range 1-350 GHz, Radio Sci., 21(6), 949-954, 1986.
    • Ghashghaie, S., Breymann, W., Peinke, J., Talkner, P., and Dodge, Y.: Turbulent cascades in foreign exchange markets, Nature, 381, 767-770, 1996.
    • Gre´mont, B., Philip, M., Galois, P., and Bate, S.: Comparative analysis and Performance of two Predictive Fade Detection Schemes for Ka-band Fade Countermeasures, IEEE J. selected areas in communications, 17(2), 180-192, 1999.
    • Gole´, P., Lavergnat, J., Ulmer-Moll, A.-M., and Vernet, M.: Les re´sultats de l'expe´rience OLYMPUS France Telecom FTR& D, technical report NT/CETP/001, 1994.
    • Hamilton, J. D.: Time Series Analysis, Princeton University Press, 1994.
    • Hodges, D. D. and Watson, R. J.: Initial comparisons of forecast attenuation and beacon measurements at 20 and 40 GHz, Proceedings of the European Conference on Antennas and Propagation (EUCAP 2006), Nice, France, p. 356.1, ESA SP-626, November 2006.
    • ITU-R: Propagation data and prediction methods required for the design of Earth-space telecommunication systems, Recommendations of the ITU-R, Rec., 618-8, 2003.
    • Lavergnat, J. and Gole´, P.: A Stochastic Raindrop Time Distribution Model, J. Appl. Meteorol., 37, 805-805, 1998.
    • Liebe, H. J.: MPM - An atmospheric millimeter-wave propagation model, Int. J. Infr. Mill. Waves, 10, 631-650, 1989.
    • Liebe, H. J., Hufford, G. A., and Cotton, M. G.: Propagation modeling of moist air and suspended water/ice particles below 1000 GHz, paper presented at AGARD 52nd Specialists Meeting of Electromagnetic Wave Propagation, Advis. Group for Aerosp. Res. and Dev., Palma de Mallorca, Spain, 17-21 May 1993.
    • Ljung, L.: System identification - theory for the user, 2nd edition, Prentice-Hall, 1999.
    • Mallet, C., Barthes, L., and Marsault, T.: A neural network model for the separation of atmospheric effects on attenuation statistics, Proceedings of the European Conference on Antennas and Propagation (EUCAP 2006), Nice, France, ESA SP-626, November 2006.
    • Manning, R. M.: A Unified statistical Rain Attenuation Model for Communication Link Fade Predictions and Optimal Stochastic Fade Control Design Using a Location Dependent Rain Statistic Database, Int. J. Satellite Commun., 8, 11-30, 1990.
    • Manning, R. M.: A statistical rain attenuation prediction model with application to the Advanced Communication Technology Satellite Project, part III: a stochastic rain fade control algorithm for satellite link power via nonlinear Markov filtering theory, NASA TM-100243, 1991.
    • Marsault, T., Hermant, J. D., Bouyer, F., et al.: EHF Propagation Experiment with Syracuse 3 satellite, EuCAP 2006 (ESA SP626), p. 357.1, Nice, France, 6-10 November 2006.
    • Mie, G.: Contributions to the optics of turbid media, particularly of colloidal metal solutions, Ann. Physik, 25(3), 377-445, 1908.
    • Nu´ n˜ez, A., Pastoriza, V., Marin˜o, P., Fonta´n, F. P., and Fiebig, U.- C.: Cellular automata for predicting three-state rain rate and rain attenuation field dynamics, Proceedings of the European Conference on Antennas and Propagation (EUCAP), Nice, ESA SP626, 2006.
    • OPEX: Second workshop of the OLYMPUS propagation experimenters, vol. 1: reference book on attenuation measurement and prediction, Noordwijk, 8-10 November 1994.
    • Peters, O., Hertlein, C., and Christensen, K.: A complexity view of rainfall. Phys. Rev. Lett., 88, 018701, 1-4, 2002.
    • Schertzer, D. and Lovejoy, S.: Universal Mulitfractals Do Exist!; Comments on “A Statistical Analysis of Mesoscale Rainfall as a Random Cascade”, J. Appl. Meteorol., 36, 1296-1303, 1997.
    • Van de Kamp, M. M. J. L.: Short-term prediction of rain attenuation using two samples, Electronics Lett., 38(23), 1476-1477, 2002.
    • Wang, W., Van Gelder, P. H. A. J. M., Vrijling, J. K., and Ma, J.: Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes, Nonlin. Processes Geophys., 12, 55-66, 2005, http://www.nonlin-processes-geophys.net/12/55/2005/.
    • Wei, W. W. S.: Time Series Analysis: Univariate and Multivariate Methods, 2nd edn., Addison Wesley, 2005.
    • Zhou, B., He, D., and Sun, Z.: Traffic modeling and prediction using ARIMA/GARCH model, in: Proc. 2nd EuroNGI Conference on Next Generation Internet Design and Engineering, 2006.
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