A Statistical Calibration Method of Propagation Prediction Model Based on Measurement Results

• Autorzy: Kelner Jan M. , Kryk Michał , Łopatka Jerzy , Gajewski Piotr

Identyfikatory

DOI

10.24425/ijet.2019.130259

• Języki publikacji: EN

Abstrakty

EN

Radio environment maps (REMs) are beginning to be an integral part of modern mobile radiocommunication systems and networks, especially for ad-hoc, cognitive, and dynamic spectrum access networks. The REMs will use emerging military systems of tactical communications. The REM is a kind of database used at the stage of planning and management of the radio resources and networks, which considers the geographical features of an area, environmental propagation properties, as well as the parameters of radio network elements and available services. At the REM, for spatial management of network nodes, various methods of propagation modeling for determining the attenuation and capacity of wireless links and radio ranges are used. One method of propagation prediction is based on a numerical solution of the wave equation in a parabolic form, which allows considering, i.a., atmospheric refraction, terrain shape, and soil electrical parameters. However, the determination of a current altitudinal profile of atmospheric refraction may be a problem. If the propagation-prediction model uses a fixed refraction profile, then the calibration of this model based on empirical measurements is required. We propose a methodology for calibrating the analyzed model based on an example empirical research scenario. The paper presents descriptions of the propagation model, test-bed and scenario used in measurements, and obtained signal attenuation results, which are used for the initial calibration of the model.

Słowa kluczowe

EN

calibration of propagation model; empirical measurements; path loss model; propagation; parabolic equation method; radio environment maps

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2020
• Tom: Vol. 66, No. 1
• Strony: 11--16
• Opis fizyczny: Bibliogr. 36 poz., il., wykr.

Twórcy

autor

Kelner Jan M.

• Military University of Technology, Faculty of Electronics, Institute of Communications Systems, Gen. Sylwester Kaliski Str. No. 2, 00-908 Warsaw

autor

Kryk Michał

• Military University of Technology, Faculty of Electronics, Institute of Communications Systems, Gen. Sylwester Kaliski Str. No. 2, 00-908 Warsaw

autor

Łopatka Jerzy

• Military University of Technology, Faculty of Electronics, Institute of Communications Systems, Gen. Sylwester Kaliski Str. No. 2, 00-908 Warsaw

autor

Gajewski Piotr

• Military University of Technology, Faculty of Electronics, Institute of Communications Systems, Gen. Sylwester Kaliski Str. No. 2, 00-908 Warsaw

Bibliografia

• [1] L. W. Barclay, Ed., Propagation of radiowaves, 3rd ed. London, UK: The Institution of Engineering and Technology, 2012.
• [2] R. Vaughan and J. Bach Andersen, Channels, propagation and antennas for mobile communications. London, UK: Institution of Engineering and Technology, 2003.
• [3] S. Salous, Radio propagation measurement and channel modelling. Hoboken, NJ, USA: Wiley, 2013.
• [4] F. Pérez Fontán and P. Mariño Espiñeira, Modeling the wireless propagation channel: A simulation approach with Matlab. Chichester: John Wiley & Sons, 2008.
• [5] “Google Earth.” [Online]. Available: http://www.google.pl/intl/pl/earth/. [Accessed: 07-Nov-2015].
• [6] A. Corucci, P. Usai, A. Monorchio, and G. Manara, “Wireless propagation modeling by using ray-tracing,” in Computational electromagnetics. Recent advances and engineering applications, R. Mittra, Ed. New York, NY, USA: Springer, 2014, pp. 575–618.
• [7] Z. Yun and M. F. Iskander, “Radio propagation modeling and simulation using ray tracing,” in The world of applied electromagnetics. In appreciation of Magdy Fahmy Iskander, A. Lakhtakia and C. M. Furse, Eds. Cham, Switzerland: Springer, 2018, pp. 275–299.
• [8] F. R. Yu, M. Huang, and H. Tang, “Biologically inspired consensus-based spectrum sensing in mobile ad hoc networks with cognitive radios,” IEEE Netw., vol. 24, no. 3, pp. 26–30, May 2010. DOI: 10.1109/MNET.2010.5464224.
• [9] H. Tang, F. R. Yu, M. Huang, and Z. Li, “Distributed consensus-based security mechanisms in cognitive radio mobile ad hoc networks,” IET Commun., vol. 6, no. 8, pp. 974–983, May 2012. DOI: 10.1049/ietcom.2010.0553.
• [10] B. Kim, G.-M. Lee, and B.-H. Roh, “ASPD: Adaptive sensing period decision for time-varying channel in military MANETs,” in 2014 IEEE Military Communications Conference, 2014, pp. 643–648. DOI: 10.1109/MILCOM.2014.113.
• [11] P. Skokowski, K. Malon, and J. Łopatka, “Properties of centralized cooperative sensing in cognitive radio networks,” in Proceedings of SPIE 10418, 2016 XI Conference on Reconnaissance and Electronic Warfare Systems (CREWS), Ołtarzew, Poland, 2017, vol. 10418, p. 1041807. DOI: 10.1117/12.2269996.
• [12] K. Malon, P. Skokowski, and J. Łopatka, “Optimization of wireless sensor network deployment for electromagnetic situation monitoring,” Int. J. Microw. Wirel. Technol., pp. 1–8, 2018. DOI: 10.1017/S1759078718000211.
• [13] M. Kustra, K. Kosmowski, and M. Suchański, “Hybrid sensing method in mobile ad-hoc networks (MANET),” in 2019 20th International Conference on Military Communications and Information Systems (ICMCIS), Budva, Montenegro, 2019, pp. 1–8. DOI: 10.1109/ICMCIS.2019.8842695.
• [14] J. Mitola and G. Q. Maguire, “Cognitive radio: Making software radios more personal,” IEEE Pers. Commun., vol. 6, no. 4, pp. 13–18, Aug. 1999. DOI: 10.1109/98.788210.
• [15] S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201– 220, Feb. 2005. DOI: 10.1109/JSAC.2004.839380.
• [16] F. R. Yu, Cognitive radio mobile ad-hoc networks. New York, NY, USA: Springer, 2011.
• [17] O. Younis, L. Kant, K. Chang, K. Young, and C. Graff, “Cognitive MANET design for mission-critical networks,” IEEE Commun. Mag., vol. 47, no. 10, pp. 64–71, Oct. 2009. DOI: 10.1109/MCOM.2009.5273810.
• [18] O. Younis et al., “Cognitive tactical network models,” IEEE Commun. Mag., vol. 48, no. 10, pp. 70–77, Oct. 2010. DOI: 10.1109/MCOM.2010.5594679.
• [19] H. B. Yilmaz, T. Tugcu, F. Alagöz, and S. Bayhan, “Radio environment map as enabler for practical cognitive radio networks,” IEEE Commun. Mag., vol. 51, no. 12, pp. 162–169, Dec. 2013. DOI: 10.1109/MCOM.2013.6685772.
• [20] M. Pesko, T. Javornik, A. Košir, M. Štular, and M. Mohorčič, “Radio environment maps: The survey of construction methods,” KSII Trans. Internet Inf. Syst., vol. 8, no. 11, pp. 3789–3809, Nov. 2014. DOI: 10.3837/tiis.2014.11.008.
• [21] P. Bednarek, J. Łopatka, and D. Bicki, “Radio environment map for the cognitive radio network simulator,” Int. J. Electron. Telecommun., vol. 64, no. 1, pp. 45–49, Jan. 2018. DOI: 10.24425/118145.
• [22] P. Gajewski, “Propagation models in radio environment map design,” in 2018 Baltic URSI Symposium (URSI), Poznan, Poland, 2018, pp. 234–237. DOI: 10.23919/URSI.2018.8406696.
• [23] M. Suchański, P. Kaniewski, J. Romanik, and E. Golan, “Radio environment map to support frequency allocation in military communications systems,” in 2018 Baltic URSI Symposium (URSI), Poznan, Poland, 2018, pp. 230–233. DOI: 10.23919/URSI.2018.8406717.
• [24] M. Suchanski, P. Kaniewski, J. Romanik, E. Golan, and K. Zubel, “Radio environment maps for military cognitive networks: Deployment of sensors vs. map quality,” in 2019 19th International Conference on Military Communications and Information Systems (ICMCIS), Warsaw, Poland, 2019, pp. 1–6. DOI: 10.1109/ICMCIS.2019.8842720.
• [25] M. F. Levy, Parabolic equation methods for electromagnetic wave propagation. London, UK: The Institution of Engineering and Technology (IET), 2000.
• [26] G. Apaydin and L. Sevgi, Radio wave propagation and parabolic equation modeling. Hoboken, NJ, USA: Wiley-IEEE Press, 2017.
• [27] K. H. Craig, “Propagation modelling in the troposphere: Parabolic equation method,” Electron. Lett., vol. 24, no. 18, pp. 1136–1139, Sep. 1988. DOI: 10.1049/el:19880773.
• [28] G. Apaydin and L. Sevgi, “MATLAB-based FEM - parabolic-equation tool for path-loss calculations along multi-mixed-terrain paths,” IEEE Antennas Propag. Mag., vol. 56, no. 3, pp. 221–236, Jun. 2014. DOI: 10.1109/MAP.2014.6867720.
• [29] National Geospatial-Intelligence Agency (NGA), “Digital Terrain Elevation Data.” [Online]. Available: https://www.nga.mil/ProductsServices/TopographicalTerrestrial/Pages/D igitalTerrainElevationData.aspx. [Accessed: 25-Jun-2017].
• [30] “Google Maps,” Google Maps. [Online]. Available: https://www.google.pl/maps/. [Accessed: 30-Jan-2018]
• [31] T. S. Rappaport, Wireless communications: Principles and practice, 2nd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2002.
• [32] T. S. Rappaport, G. R. MacCartney, M. K. Samimi, and S. Sun, “Wideband millimeter-wave propagation measurements and channel models for future wireless communication system design,” IEEE Trans. Commun., vol. 63, no. 9, pp. 3029–3056, Sep. 2015. DOI: 10.1109/TCOMM.2015.2434384.
• [33] J. M. Kelner and C. Ziółkowski, “Evaluation of angle spread and power balance for design of radio links with directional antennas in multipath environment,” Phys. Commun., vol. 32, pp. 242–251, Feb. 2019. DOI: 10.1016/j.phycom.2018.12.005.
• [34] F. Qamar, M. N. Hindia, T. Abbas, K. B. Dimyati, and I. S. Amiri, “Investigation of QoS performance evaluation over 5G network for indoor environment at millimeter wave bands,” Int. J. Electron. Telecommun., vol. 65, no. 1, pp. 95–101, Feb. 2019. DOI: 10.24425/ijet.2019.126288.
• [35] J. R. Taylor, An introduction to error analysis: The study of uncertainties in physical measurements, 2nd ed. Sausalito, Calif: University Science Books, 1997.
• [36] P. C. Hansen, V. Pereyra, and G. Scherer, Least squares data fitting with applications. Baltimore, MD, USA: Johns Hopkins University Press, 2013