8 Bibliography Radiowave propagation modelling for new satcom services at Ku-band and above

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1 8 Bibliography 1 ARBESSER-RASTBURG et al. Radiowave propagation modelling for new satcom services at Ku-band and above. COST 255 Final Report, ESA Publications Division, SP-1252, March CASTANET, L. Fade Mitigation Techniques for new SatCom systems operating at Ka and V bands. Ph'D thesis of SUPAERO, Toulouse, France, December LEMORTON, J. Propagation Channel Modelling and Time Series Synthesizers for the Simulation of Satellite Communication Systems. ONERA-DEMR document, Toulouse, France. 4 CASTANET, L.; VAN DE KAMP, M. Modelling the Dynamic Properties of the Propagation Channel. ONERA-DEMR document, Toulouse, France. 5 CASTANET, L.; MERTENS, D.; BOUSQUET, M. Simulation of the performance of a Ka-band VSAT videoconferencing system with uplink power control and data rate reduction to mitigate atmospheric propagation effects. Int. J. Sat. Com., Vol. 20, July BOLEA-ALAMANAC, A. et al. FMT control loop performance assessment on a point-to-point oriented satellite broadband system for multimedia applications. 10 th Ka-band Utilization Conference, Vicenza, Italy, October EMILIANI, L. D. et al. Short- And Mid-Term Prediction Techniques Of Propagation Conditions From Weather Radar Observations. Application to resource management in SATCOM systems. IEEE. 8 ITU-R. Recommendation ITU-R P : Prediction Method of Fade Dynamics on Earth-Space Paths ROQUET-LLORET, V.; GIMONET, E.; CASTANET, L. Spectral analysis of propagation impairments on experimental earth-space links at Ka and V bands. ICAP 2001, Manchester, UK, April MATRICIANI, E.; MAURI, M.; RIVA, C. Scintillation and simultaneous rain attenuation at 49.5 GHz. 9th ICAP, Eindhoven, The Netherlands, MATRICIANI, E.; MAURI, M.; RIVA, C. Scintillation and simultaneous rain attenuation at 12.5 GHz to satellite Olympus. Radio Science, Vol. 32, No 5, September-October MATRICIANI, E. Physical-mathematical model of dynamics of rain attenuation with application to power spectrum. Electronics Letters, Vol. 30, No 6, pp , March 1994.

2 VAN DE KAMP, M.; CASTANET, L. Fade Dynamics Review. First COST 280Workshop, doc. PM3-018, Malvern, UK, July ITU-R. Recommendation ITU-R P : Acquisition, presentation and analysis of data in studies of tropospheric propagation. 15 GREMONT, B. C.; GALLOIS, A. P.; BATE, S. Efficient fade compensation for Ka-band VSAT systems. 2 nd Ka-band Utilization Conference, pp , Florence, Italy, VAN DE KAMP, M. Statistical Analysis of Rain Fade Slope. IEEE Trans. on Antennas and Propagation, Vol. 51, N o 8, August VAN DE KAMP, M. Dynamic Modelling of Rain Attenuation. Postdoc Research Report, ONERA Electromagnetics and Radar Department, March LACOSTE, F. Modelling of the dynamics of the Earth-space propagation channel at Ka and EHF bands. Ph D of SUPAERO, Toulouse, France, September FIEBIG U-C. Channel modelling for Ka-band and above. Proceedings of the Personal Indoor Mobile Radio Conference, PIMRC 99, Osaka, Japan, SHIN S. K.; LEE S. I.; KIM Y. S.; KIM J. M. A dynamic rain attenuation modelling technique for satellite communication link. Proceedings of the International Conference on Telecommunications, 2:33 37, Cheju, Korea, AUDOIRE B.; CASTANET L.; CHRISTOPHE F.; GREMONT B. Ka band attenuation time series generator and comparison with OLYMPUS data. AIAA-ICSSC-19 Conference, Toulouse, France, April LEMORTON, J. et al. Development and validation of time series synthesizers of rain attenuation for Ka-band and Q/V-band satellite communication systems. International Journal of Satellite Communications and Networking, Volume 25, October 2007, pp MASENG, T.; BAKKEN, P. M. A Stochastic Dynamic Model of Rain Attenuation. IEEE Trans. Commun., Vol. 29, n 5, pp , May LACOSTE, F. et al. Improvement of the ONERA-CNES rain attenuation time series synthesizer and validation of the dynamic characteristics of the generated fade events. Space Communication Journal, Vol. 20, n 1-2, CASTANET, L.; DELOUES, T.; LEMORTON, J. Channel Modelling Based on N-state Markov Chains for Satcom Systems Simulation. ICAP 2003, Exeter, United Kingdom, April VALBONESI L. A large database of ITALSAT attenuation data for tropospheric channel simulations. First COST 280 Workshop, doc. PM3-006, Malvern, U.K., 1-3 July 2002.

3 MATRICCIANI E. Physical-mathematical model of the dynamics of rain attenuation based on rain rate time series and two layer vertical structure of precipitation. Radio Science 1996; 31: KANELLOPOULOS, S. A.; PANAGOPOULOS, A. D.; KANELLOPOULOS, J. D. Calculation of the dynamic input parameter for a stochastic model simulating rain attenuation: a novel mathematical approach. IEEE Trans. on Antennas and Propagation, Vol.5, n 11, pp , November GRÉMONT, B. C.; FILIP, M. Spatio-Temporal Rain Attenuation Model for Application To Fade Mitigation Techniques. IEEE Transactions on Antennas and Propagation, Vol. 52, N 5, pp , May LACOSTE, F. et al. Classical and On-Demand Rain Attenuation Time Series Synthesis: Principle and Applications. Proceedings of 24 th AIAA-ICSSC Conference, San Diego, California, June ITU-R. Recommendation ITU-R P.837-5: Characteristics of precipitation for propagation modelling. 32 CARRIÉ, G.; LACOSTE, F.; CASTANET, L. A Robust Estimator of the Dynamic Characteristics of Rain Attenuation Events. Proceedings of 2 nd JA2310 SatNEx Workshop, Oberpfaffenhofen, Germany, April LIN, S. H. Statistical behavior of rain attenuation. Bell Syst. Tech. J., vol 52, pp , April CARRIÉ, G.; LACOSTE, F.; CASTANET, L. New on-demand channel model to synthesize rain attenuation time series at Ku-, Ka- and Q/V-bands. International Journal of Satellite Communications and Networking, Special issue on Channel Modelling and Propagation Impairment Simulation, November CASTANET, L.; DELOUES, T.; LEMORTON, J. Methodology to Simulate Long-term Propagation Time Series from the Identification of Attenuation Periods Filled with Synthesized Events. COST International Workshop on Satellite Communications from Fade Mitigation to Service Provision, Noordwijk, The Netherlands, May ABRAMOWITZ, M.; STEGUN, I. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. 37 COUTO DE MIRANDA, E.; SILVA MELLO, L. A. R.; PONTES, M. S. Dynamic Aspects of Rainfall and Attenuation in Equatorial Regions. Proceedings of the 2nd CNES Workshop on Earth-Space propagation, Toulouse, COUTO DE MIRANDA, E.; SILVA MELLO, L. A. R.; PONTES, M.; SOUZA, R. Dynamics of attenuation in equatorial Brazil: 1-year analysis of three 12GHz earth-space links. Proceedings of the IEEE Antennas And Propagation Symposium On Wireless Communications, Waltham, 1998.

4 COUTO DE MIRANDA, E.; SILVA MELLO, L. A. R.; PONTES, M.; ALMEIDA, M; SOUZA, R. Rainfall Induced Satellite Beacon Attenuation in Tropical and Equatorial Regions. Electronics Letters, v. 39, n. 11, p , COUTO DE MIRANDA, E.; SILVA MELLO, L. A. R.; PONTES, M. Statistical modelling of the cumulative probability distribution function of rainfall rate in Brazil. Proceedings of 1998 CLIMPARA, MIRANDA, E. C.; PONTES, M. S.; SILVA MELLO, L. A. R. Modeling the Cumulative Distribution of Rainfall Rate as a Mixture Random Process. 3 rd CNES Workshop on Earth-Space Propagation, Toulouse, France, October PONTES, M. S.; DA SILVA MELLO, L.; DE SOUZA, R. S. L.; MIRANDA, C. S. B. Review of rain attenuation studies in tropical and equatorial regions in Brazil. 5 th Intl. Conf. Information, Comms. and Signal Proc., Bangkok, 6-9 December, pp CASTANET, L. et al. Assessment of radiowave propagation for satellite communication and navigation systems in tropical and sub-tropical areas. Final Report of the study. ONERA Electromagnetics and Radar Department, June TATTELMAN, P.; SCHARR, K. G. A Model for estimating oneminute rainfall rates. American Meteorological Society, vol. 22, September November LACOSTE, F. et al. Methodology of validation of time series synthesiser for the Ka-band satellite propagation channel. Proceedings of AIAA/ICSSC 2004 conference, Monterrey, USA, May LACOSTE, F. et al. Event-based testing analysis of rain attenuation time series synthesizers for the ka-band satellite propagation channel. Proceedings of CLIMDIFF conference, Cleveland, OH, USA, September CASTANET, L. (editor) et al. Influence of the variability of the propagation channel on mobile, fixed multimedia and optical satellite communications. SatNEx JA-2310 book, ISBN , Shaker, JAMBU, M. Méthodes de base de l analyse de données. Book from Eyrolles et France Telecom-Cnet, Paris, 1999, pp

5 9 Glossary CCDF Complementary Cumulative Distribution Function (see CDF). Let F(x X) be the CDF of a given random variable, then the CCDF is given by 1 - F(x X), i.e., F(x > X). CDF Cumulative Distribution Function. The probability of a random variable to have a value less of equal a given value X: F(x X). CETUC Center for Telecommunications Studies of PUC-Rio. DAU Data Acquisition Unit. Specialized computer for acquiring and logging of rain and propagation (beacon and/or radiometer) data in Brazilian measurements campaigns. Information is stored in compacted files for transmission over telephonic lines. First order statistics statistics related to stationary features of the process. Usually described by the Cumulative Distribution Function. See CDF. ITU-R International Telecommunications Union, Radio sector. ITU is an international organism responsible for the release of global recommendations in the telecommunications arena. MATLAB MATrix LABoratory. Software tool from MathWorks for the development of scripts and functions based on input/output data represented by matrices. It has built-in mathematical libraries and was used in this work as a developing environment for computational tools design. ONERA Office National d Études et de Recherches Aérospatiales. The French aerospace research center. Public establishment with industrial and commercial operations, carrying out application-oriented research to support enhanced innovation and competitiveness in the aerospace and defense sectors.

6 199 PUC-Rio Pontifical Catholic University of Rio de Janeiro Brazil. Rain-gauge specially designed equipment for the collection of rain aimed, ultimately, at the measurement of the point rainfall rate. Among many types of rain-gauges, the one used for the measurements reported on this work is of the tipping bucket type. Rain drops fill a bucket with known volume through an aperture with a given area. Knowing that 1 mm of rainfall is equivalent to 1 liter of water per squared meter, one can easily compute the amount of rain (in mm) corresponding to one tip (one tip occurs at each fulfillment of the bucket). Second order statistics statistics related to dynamic features of a process. Usually described by the duration and slope of the represented quantity. For attenuation time series, Fade Duration and Fade Slope, as well as the autocorrelation function, are usual descriptions of the dynamics. Up-time The net time a measurement set-up is working and properly executing the tasks it is intended to do (equipment is said to be on air). It is usually expressed as a percentage of the total time of the measurements campaign. For example: in an one-year campaign, if the measurement equipments are on air for 340 days, the up-time is about 340/365* %. WGN White Gaussian Noise.

7 Appendix A Generation of the data structure based on experimental extracted events A data structure to be used by the microscopic model is generated from the extracted events database. The creation of this structure, exemplified in an extract shown in Table 19, is now explained. The macroscopic two-state Markov chain channel model provides as output a collection of rain events positions in a synthesized long term time series. Therefore, from the generated time series it is straightforward to obtain the parameter D for each event. To build the microscopic channel model, it is necessary to implement a strategy to associate typical A max and D peak for any given D. Using as input the experimental events database, an algorithm creates a data structure containing [A max, D peak, D] triplets to be used by the microscopic portion of the two-state Markov chain channel model. This algorithm works in the following way. 1. Every isolated experimental event is read from the database and parameters [A max, D peak, D] are extracted. 2. Bins of duration D are created, ranging from zero to the maximum value of D in the database. The size of the duration bins is user defined, being 5 minutes the default one. 3. A histogram of events duration is created by the allocation of each rain attenuation event in the proper duration bin. 4. For each duration bin, arranged in columns in the data structure to be output, pairs [A max, D peak ] are put in lines. In the data structure, instead of saving the D peak parameter as an absolute value, it is stored as a percentage from the beginning of the event. 5. As a last adjust, A max values are downgraded to a resolution of 0.1 db, which is the adopted resolution in all models because of the fact that it is the resolution at which beacon data were recorded.

8 201 In the data structure sampled in Table 19, for a selected duration bin (column), each line is a pair [A max, D peak ] with D peak expressed as a percentage from the beginning of the event to be used for on-demand event generation in the microscopic portion of the model. The first line in the table corresponds to the bin, for example: the second column groups the events having duration between five and ten minutes. Table 19 Sample of the data structure created from the experimental events database

9 Appendix B RMS error results detailed per thresholds The color plots shown in Section 6.8 are a sample aimed at the illustration of the improvement such a tool can bring to the analysis of the results and, therefore, to the comparison among the different models, for many thresholds of attenuation, duration and fade slope. This Appendix presents all the results, grouped in the same way as it is done in Figure 88 (to make it easier for the reader to compare all the models), for each one of three 2 nd order statistics analyzed in this work: FDn, FDt and FSc.

10 203 biln Figure 105 RMS error plots for models comparison (FDn for MOS)

11 204 biln Figure 106 RMS error plots for models comparison (FDn for RIO)

12 205 biln Figure 107 RMS error plots for models comparison (FDn for CUR)

13 206 biln Figure 108 RMS error plots for models comparison (FDn for POA)

14 207 biln Figure 109 RMS error plots for models comparison (FDt for MOS)

15 208 biln Figure 110 RMS error plots for models comparison (FDt for RIO)

16 209 biln Figure 111 RMS error plots for models comparison (FDt for CUR)

17 210 biln Figure 112 RMS error plots for models comparison (FDt for POA)

18 211 biln Figure 113 RMS error plots for models comparison (FSc for MOS)

19 212 biln Figure 114 RMS error plots for models comparison (FSc for RIO)

20 213 biln Figure 115 RMS error plots for models comparison (FSc for CUR)

21 214 biln Figure 116 RMS error plots for models comparison (FSc for POA)