Advertisement

New Deterministic and Stochastic Simulation Models for UAV-MIMO Ricean Fading Channels

  • Xi Zhang
  • Xiang ChengEmail author
Research paper
  • 14 Downloads

Abstract

For the practical simulation and performance evaluation of unmanned aerial vehicle (UAV) multiple-input multiple-output (MIMO) Ricean fading channels, it is desirable to develop accurate UAV-MIMO channel simulation models for more realistic scenarios of non-isotropic scattering. In this study, using a two-cylinder reference model to describe the distribution of scatterers, we propose new deterministic and stochastic simulation models. Analytical and numerical results indicate that both simulation models provide good approximations to the desired statistical properties of the reference model, and the stochastic simulation model results in a better performance under comparable computational complexity.

Keywords

UAV-MIMO channel two-cylinder model deterministic simulation model stochastic simulation model statistical properties 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    D. W. Matolak. Unmanned aerial vehicles: Communications challenges and future aerial networking [C]//International Conference on Computing, Networking and Communications, Garden Grove, 2015: 567–572.Google Scholar
  2. [2]
    R. Pabst, B. H. Walke, D. C. Schultz, et al. Relay-based deployment concepts for wireless and mobile broadband radio [J]. IEEE Communications Magazine, 2004, 42(9): 80–89.CrossRefGoogle Scholar
  3. [3]
    Z. Shi, P. Xia, Z. Gao, et al. Modeling of wireless channel between UAV and vessel using the FDTD method [C]//International Conference on Wireless Communications, Networking and Mobile Computing, Beijing, 2015: 100–104.Google Scholar
  4. [4]
    D. Kai, M. Putzke, B. Dusza, et al. Three dimensional channel characterization for low altitude aerial vehicles [C]//International Symposium on Wireless Communication Systems, York, 2010: 756–760.Google Scholar
  5. [5]
    D. W. Matolak, R. Sun. Initial results for air-ground channel measurements & modeling for unmanned aircraft systems: Over-sea [C]//IEEE Aerospace Conference, Big Sky, 2014: 1–15.Google Scholar
  6. [6]
    M. Simunek, F. P. Fontán, P. Pechac. The UAV low elevation propagation channel in urban areas: Statistical analysis and time-series generator [J]. IEEE Transactions on Antennas & Propagation, 2013, 61(7): 3850–3858.CrossRefGoogle Scholar
  7. [7]
    X. Yin, X. Cheng. Propagation channel characterization, parameter estimation, and modeling for wireless communications [M]. Wiley-IEEE Press, 2014.Google Scholar
  8. [8]
    X. Cheng, C. X. Wang, D. I. Laurenson, et al. An adaptive geometrybased stochastic model for non-isotropic MIMO mobile-to-mobile channels [J]. IEEE Transactions on Wireless Communications, 2009, 8(9): 4824–4835.CrossRefGoogle Scholar
  9. [9]
    L. Zeng, X. Cheng, C. X. Wang, et al. Second order statistics of nonisotropic UAV Ricean fading channels [C]//IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, 2018: 1–5.Google Scholar
  10. [10]
    K. Jin, X. Chen, X. Ge, et al. Three dimensional modeling and spacetime correlation for UAV channels [C]//IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, 2017: 1–5.Google Scholar
  11. [11]
    X. Cheng, Y. Li. A 3D geometry-based stochastic model for UAVMIMO wideband non-stationary channels [J]. IEEE Internet of Things Journal, 2018.Google Scholar
  12. [12]
    D. W. Matolak, R. Sun. Air-ground channel characterization for unmanned aircraft systems—Part I: Methods, measurements, and models for over-water settings [J]. IEEE Transactions on Vehicular Technology, 2017, 66(1): 26–44.CrossRefGoogle Scholar
  13. [13]
    R. Sun, D. Matolak. Air-ground channel characterization for unmanned aircraft systems—Part II: Hilly & mountainous settings [J]. IEEE Transactions on Vehicular Technology, 2017, 66(3): 1913–1925.CrossRefGoogle Scholar
  14. [14]
    D. W. Matolak. Air-ground channels & models: Comprehensive review and considerations for unmanned aircraft systems [C]//IEEE Aerospace Conference, Big Sky, 2012: 1–17.Google Scholar
  15. [15]
    A. G. Zajic, G. L. Stuber. Three-dimensional modeling and simulation of wideband MIMO mobile-to-mobile channels [J]. IEEE Transactions on Wireless Communications, 2009, 8(3): 1260–1275.CrossRefGoogle Scholar
  16. [16]
    Y. Li, X. Cheng. New deterministic and statistical simulation models for non-isotropic UAV-MIMO channels [C]//9th International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, 2017: 1–6.Google Scholar
  17. [17]
    A. Abdi, J. Barger, M. Kaveh. A parametric model for the distribution of the angle of arrival and the associated correlation function and power spectrum at the mobile station [J]. IEEE Transactions on Vehicular Technology, 2002, 51(3): 425–434.CrossRefGoogle Scholar
  18. [18]
    C. A. Gutierrez-Diaz-de-Leon, M. Patzold. Sum-of-sinusoids-based simulation of flat fading wireless propagation channels under nonisotropic scattering conditions [C]//IEEE Global Telecommunications Conference, Washington, DC, 2007: 3842–3846.Google Scholar
  19. [19]
    M. Patzold, U. Killat, F. Laue. A deterministic digital simulation model for Suzuki processes with application to a shadowed Rayleigh land mobile radio channel [J]. IEEE Transactions on Vehicular Technology, 1996, 45(2): 318–331.CrossRefGoogle Scholar
  20. [20]
    X. Cheng, C. X. Wang, D. I. Laurenson, et al. New deterministic and stochastic simulation models for non-isotropic scattering mobileto-mobile Rayleigh fading channels [J]. Wireless Communications & Mobile Computing, 2011, 11(7): 829–842.CrossRefGoogle Scholar
  21. [21]
    X. Cheng, Q. Yao, C. X. Wang, et al. An improved parameter computation method for a MIMO V2V Rayleigh fading channel simulator under non-isotropic scattering environments [J]. IEEE Communications Letters, 2013, 17(2): 265–268.CrossRefGoogle Scholar
  22. [22]
    M. Patzold. On the stationarity and ergodicity of fading channel simulators based on Rice’s sum-of-sinusoids [J]. International Journal of Wireless Information Networks, 2004, 11(2): 63–69.CrossRefGoogle Scholar
  23. [23]
    Y. R. Zheng. A non-isotropic model for mobile-to-mobile fading channel simulations [C]//IEEE Military Communications Conference, Washington, DC, 2006: 1–7.Google Scholar

Copyright information

© Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

Personalised recommendations