Advertisement

Recommendations for shadow fading estimation from received composite signal samples

  • Pamela Njemcevic
  • Adriana Lipovac
  • Vlatko Lipovac
Article
  • 1 Downloads

Abstract

Evident lack of verified and comprehensive theoretical models for shadow fading, which enable analytical estimation of its statistical distribution and statistical parameters, has promoted the empirical alternatives for this task in many aspects of a wireless system lifecycle, such as e.g. coverage planning, where the imperative is to determine the optimal number of base stations, which balances the system performance and implementation efficiency. However, in addition to shadow fading, the composite received signal is simultaneously affected by deterministic path loss and multipath fading, too, so the prerequisite for accurate estimation of shadow fading from the composite received signal samples is efficient elimination of its fast temporal variations, fast spatial variations, and finally, path loss. Unfortunately, the available methods for this are often not appropriate for measurements related to shadow fading estimation. So, for example, even the commonly used drive/walk test provides neither elimination of time variations, nor accurate path loss estimation. So, in this paper, after identifying drawbacks of the existing techniques for shadow fading estimation, the appropriate procedure is proposed with concrete and unambiguous guidelines for elimination of undesirable components of the composite signal through a five-step algorithm, which is shown to provide significant advantage with respect to common drive/walk testing, in terms of shadow fading values and its statistical parameters estimation.

Keywords

Shadow fading Temporal averaging Spatial averaging Composite signal 

Notes

Acknowledgements

The authors appreciate valuable suggestions and dedication from Prof. Ivo M. Kostić during this research.

References

  1. 1.
    Salo, J, Vuokko, L., & Vainikainen, L. (2005). Why is shadow fading lognormal? In Proceedings of International Symposium on Wireless Personal Multimedia Communications (pp. 522–526).Google Scholar
  2. 2.
    Coulson, J., Williamson, A. G., & Vaughan, R. G. (1998). Improved fading distribution for mobile radio. IEE Proceedings - Communications, 145(3), 197–202.CrossRefGoogle Scholar
  3. 3.
    Abdi, A., & Kaveh, M. (1999). On the utility of gamma PDF in modeling shadow fading (slow fading). In Proceedings on 49th IEEE Vehicular Technology Conference, (Vol. 3, pp. 2308–2312).Google Scholar
  4. 4.
    Abdi, A., & Kaveh, M. (2011). A comparative study of two shadow fading models in ultrawideband and other wireless systems. IEEE Transactions on Wireless Communications, 10(5), 1428–1434.CrossRefGoogle Scholar
  5. 5.
    Jalden, N., Zetterberg, P., Ottersten, B., Aihua, H., & Thoma, R. (2007). Correlation properties of large scale fading based on indoor measurements. Proceedings on Wireless Communications and Networking Conference WCNC, 2007, 1894–1899.Google Scholar
  6. 6.
    Zhang, Y., Zhang, J., Dong, D., Nie, X., Liu, G. & Zhang, P. (2008). A novel spatial autocorrelation model for shadow fading in urban macro environments. In Proceedings on IEEE Global Telecommunications Conference Globcom 2008 (pp. 1–5).Google Scholar
  7. 7.
    Weitzen, J., & Lowe, T. J. (2002). Measurement of angular and distance correlation properties of log-normal shadowing at 1900 MHz and its application to design of PCS system. IEEE Transactions on Vehicular Technology, 51(2), 265–273.CrossRefGoogle Scholar
  8. 8.
    Rautiainen, T., Kalliola, K., & Juntunen, J. (2005). Wideband radio propagation characteristics at 5.3 GHz in suburban environments. In Proceedings on 16th IEEE International Symposium on Personal, Indoor and Mobile Communications (pp. 868–872)Google Scholar
  9. 9.
    Zhang, J., Dong, D., Liang, Y., Huang, C., Liu, G., & Dong, W. (2010). Propagation characteristics of wideband relay channels in urban micro-cell environment. IEEE Antennas and Wireless Propagation Letters, 9, 657–661.CrossRefGoogle Scholar
  10. 10.
    Sharma, R. K., & Wallace, J. W. (2009). Experimental characterization of indoor multiuser shadowing for collaborative cognitive radio. In Proceedings on 3rd European Conference on Antennas and Propagation EuCAP 2009 (pp. 3844–3848).Google Scholar
  11. 11.
    Liberti, J. C., & Rappaport, T. S. (1992). Statistics of shadowing in indoor radio channels at 900 and 1900 MHz. In Proceedings on IEEE Military Communications Conference MILCOM’92, (Vol. 3, pp. 1066–1070).Google Scholar
  12. 12.
    Njemcevic, P. (2017). Contribution to statistical modeling of shadow fading parameters. Ph.D. theses, University of Sarajevo, Bosnia and Herzegovina.Google Scholar
  13. 13.
    Walker E., Zepernick, H. J., & Wysocki, T. (1998). Fading measurements at 2.4 GHz for the indoor radio propagation channel. In International Zurich Seminar on Broadband Communications (pp. 171–176).Google Scholar
  14. 14.
    Nikookar, H. (1995) Wireless channel modeling and code division multiple access for indoor communications. Ph.D. thesis, Technische Universiteit Delft, NetherlandGoogle Scholar
  15. 15.
    Valenzuela, R. A., Landron, O., & Jacobs, D. (1997). Estimating local mean signal strength of indoor multipath propagation. IEEE Transactions on Vehicular Technology, 46(1), 203–212.CrossRefGoogle Scholar
  16. 16.
    Moraitis, N., & Constantinou, N. (2004). Indoor channel measurements and characterization at 60 GHz for wireless local area networks applications. IEEE Transactions on Antennas and Propagation, 52(12), 3180–3189.CrossRefGoogle Scholar
  17. 17.
    Nerguizian, C., Despins, C. L., Affès, S., & Djadel, M. (2005). Radio-channel characterization of an underground mine at 2.4 GHz. IEEE Transactions on Wireless Communications, 4(5), 2441–2453.CrossRefGoogle Scholar
  18. 18.
    Tanghe, E., Verloock, L., Martens, L., Capoen, H., Van Herwegen, K., & Vantomme, W. (2008). The industrial indoor channel: Large-scale and temporal fading at 900, 2400, and 5200 MHz. IEEE Transactions on Wireless Communications, 7(7), 2740–2751.CrossRefGoogle Scholar
  19. 19.
    Hashemi, H., McGuire, M., Vlasschaert, T., & Tholl, D. (1994). Measurements and modeling of temporal variations of the indoor radio propagation channel. IEEE Transactions on Vehicular Technology, 43(3), 733–737.CrossRefGoogle Scholar
  20. 20.
    Bultitude, R. J. C., Mahmoud, S. A., & Sullivan, W. A. (1989). A comparison of indoor radio propagation characteristics at 910 MHz and 1.75 GHz. IEEE Journal on Selected Areas in Communications, 7(1), 851–852.Google Scholar
  21. 21.
    Lee, W. C. Y. (1985). Estimate of local average power of a mobile radio signal. IEEE Transactions on Vehicular Technology, 34(1), 22–27.CrossRefGoogle Scholar
  22. 22.
    Clarke, R. H. (1968). A statistical theory of mobile-radio reception. The Bell System Technical Journal, 47(6), 957–1000.CrossRefGoogle Scholar
  23. 23.
    Austin, M. D., & Stuber, G. L. (1994). Velocity adaptive handoff algorithms for microcellular systems. IEEE Transactions on Vehicular Technology, 43(3), 549–561.CrossRefGoogle Scholar
  24. 24.
    Tepedelenlioglu, C., Abdi, A., Giannakis, G. B., & Kaveh, M. (2001). Estimation of doppler spread and signal strength in mobile communications with applications to handoff and adaptive transmission. In Wireless Communications and Mobile Computing (pp. 221–242). New York: WileyGoogle Scholar
  25. 25.
    Njemcevic, P. (2014) Local average signal estimation in Nakagami-m channels. In Proceedings on IEEE 6th International Symposium on Communications, Control, and Signal Processing.Google Scholar
  26. 26.
    Njemcevic, P. (2015). A novel approach in determination of the appropriate spatial averaging signal length. Wireless Personal Communications, 82(3), 1851–1861.CrossRefGoogle Scholar
  27. 27.
    Njemcevic, P., & Lipovac, V. (2016). Estimation of radio signal spatial local mean. In Proceedings of 24rd International Conference on Software, Telecommunications and Computer Networks.Google Scholar
  28. 28.
    Vega, D., Lopez, S., Matias, J., Gil, U., Pena, I., Velez, M., et al. (2007). Generalization of lee method for the analysis of the signal variability. IEEE Transactions on Vehicular Technology, 58(2), 506–516.CrossRefGoogle Scholar
  29. 29.
    Matias, J. M., de la Vega, D., Lopez, S., Pena, I., Fernandez, I, & Angueira, P. (2008). Location correction factors for coverage planning tools for DRM (Digital Radio Mondiale) in 26 MHz band. In Proceedings on IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (pp. 1–7).Google Scholar
  30. 30.
    Prieto, G., Guerra, D., Matias, J. M., Velez, M. M., & Arrinda, A. (2008). Digital-radio-mondiale (DRM) measurement-system design and measurement methodology for fixed and mobile reception. IEEE Transactions on Instrumentation and Measurement, 57(3), 565–670.CrossRefGoogle Scholar
  31. 31.
    Cheung, K., Sau, J. H. M., & Murch, R. D. (1998). A new empirical model for indoor propagation prediction. IEEE Transactions on Vehicular Technology, 47(3), 996–1001.CrossRefGoogle Scholar
  32. 32.
    Njemcevic, P., Lipovac, A., & Lipovac, V. (2018). Improved model for estimation of spatial averaging path length. Wireless Communications and Mobile Computing, 2018, 1–13.CrossRefGoogle Scholar
  33. 33.
    Laselva, D., Zhao, X., Meinili, J., Jamsa, T., Nuutinen, J., Kyosti, P., & Hentila, L. (2005). Empirical models and parameters for rural and indoor wideband radio channels at 2.45 and 5.25 GHz. In Proceedings on 16th International Symposium on Personal, Indoor and Mobile Radio Communications PIMRC 2005, (Vol. 1, pp. 654–658).Google Scholar
  34. 34.
    Lipovac, A., Lipovac, V., & Hamza, M. (2017). Verification of OFDM error floor prediction in time-dispersive LTE FDD DL channel. Wireless Personal Communications, 93(3), 853–875.CrossRefGoogle Scholar
  35. 35.
    Gonzalez-Ruiz, A., & Cai H. (2016). Wireless channel simulator. University of California, Santa BarbaraGoogle Scholar
  36. 36.
    Cai, X., & Giannakis, G. B. (2003). A two-dimensional channel simulation model for shadowing processes. IEEE Transactions on Vehicular Technology, 52(6), 1558–1567.CrossRefGoogle Scholar
  37. 37.
    Gonzalez-Ruiz, A., Ghaffarkhah, A., & Mostofi, Y. (2011). A comprehensive overview and characterization of wireless channels for networked robotic and control systems. Journal of Robotics, 2011, 340372.CrossRefGoogle Scholar
  38. 38.
    Gudmundson, M. (1991). Correlation model for shadowing fading in mobile radio systems. Electronics Letters, 27, 2145–2146.CrossRefGoogle Scholar
  39. 39.
    Shankar, P. M. (2012). Fading and shadowing in wireless systems. New York: Springer.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Telecommunications, Faculty of Electrical EngineeringUniversity of SarajevoSarajevoBosnia and Herzegovina
  2. 2.Department of Electrical Engineering and ComputingUniversity of DubrovnikDubrovnikCroatia

Personalised recommendations