Skip to main content

Advertisement

Low-Complexity Geometry-Based MIMO Channel Simulation

Abstract

The simulation of electromagnetic wave propagation in time-variant wideband multiple-input multiple-output mobile radio channels using a geometry-based channel model (GCM) is computationally expensive. Due to multipath propagation, a large number of complex exponentials must be evaluated and summed up. We present a low-complexity algorithm for the implementation of a GCM on a hardware channel simulator. Our algorithm takes advantage of the limited numerical precision of the channel simulator by using a truncated subspace representation of the channel transfer function based on multidimensional discrete prolate spheroidal (DPS) sequences. The DPS subspace representation offers two advantages. Firstly, only a small subspace dimension is required to achieve the numerical accuracy of the hardware channel simulator. Secondly, the computational complexity of the subspace representation is independent of the number of multipath components (MPCs). Moreover, we present an algorithm for the projection of each MPC onto the DPS subspace in operations. Thus the computational complexity of the DPS subspace algorithm compared to a conventional implementation is reduced by more than one order of magnitude on a hardware channel simulator with 14-bit precision.

References

  1. 1.

    Correia LM (Ed): Mobile Broadband Multimedia Networks Techniques, Models and Tools for 4G. Elsevier, New York, NY, USA; 2006.

  2. 2.

    Kaltenberger F, Steinböck G, Kloibhofer R, Lieger R, Humer G: A multi-band development platform for rapid prototyping of MIMO systems. Proceedings of ITG Workshop on Smart Antennas, April 2005, Duisburg, Germany 1–8.

  3. 3.

    Kolu J, Jamsa T: A real-time simulator for MIMO radio channels. Proceedings of the 5th International Symposium on Wireless Personal Multimedia Communications (WPMC '02), October 2002, Honolulu, Hawaii, USA 2: 568–572.

  4. 4.

    Azimuth Systems Inc : ACE 400NB MIMO channel emulator. Product Brief, 2006, https://doi.org/www.azimuthsystems.com/files/public/PB_Ace400nb_final.pdf

  5. 5.

    Spirent Communications Inc : SR5500 wireless channel emulator. Data Sheet, 2006, https://doi.org/www.spirentcom.com/documents/4247.pdf

  6. 6.

    Zemen T, Mecklenbräuker CF: Time-variant channel estimation using discrete prolate spheroidal sequences. IEEE Transactions on Signal Processing 2005,53(9):3597-3607.

  7. 7.

    Clarke R: A statistical theory of mobile-radio reception. The Bell System Technical Journal 1968,47(6):957-1000.

  8. 8.

    Jakes W: Microwave Mobile Communications. John Wiley & Sons, New York, NY, USA; 1974.

  9. 9.

    Pätzold M, Laue F: Statistical properties of Jakes' fading channel simulator. Proceedings of the 48th IEEE Vehicular Technology Conference (VTC '98), May 1998, Ottawa, Canada 2: 712–718.

  10. 10.

    Pop MF, Beaulieu NC: Limitations of sum-of-sinusoids fading channel simulators. IEEE Transactions on Communications 2001,49(4):699-708. 10.1109/26.917776

  11. 11.

    Dent P, Bottomley GE, Croft T: Jakes fading model revisited. Electronics Letters 1993,29(13):1162-1163. 10.1049/el:19930777

  12. 12.

    Li Y, Huang X: The simulation of independent Rayleigh faders. IEEE Transactions on Communications 2002,50(9):1503-1514. 10.1109/TCOMM.2002.802562

  13. 13.

    Zheng YR, Xiao C: Simulation models with correct statistical properties for Rayleigh fading channels. IEEE Transactions on Communications 2003,51(6):920-928. 10.1109/TCOMM.2003.813259

  14. 14.

    Zajić AG, Stüber GL: Efficient simulation of Rayleigh fading with enhanced de-correlation properties. IEEE Transactions on Wireless Communications 2006,5(7):1866-1875.

  15. 15.

    Zemen T, Mecklenbräuker C, Kaltenberger F, Fleury BH: Minimum-energy band-limited predictor with dynamic subspace selection for time-variant flat-fading channels. to appear in IEEE Transactions on Signal Processing

  16. 16.

    Zemen T: OFDM multi-user communication over time-variant channels, Ph.D. dissertation.

  17. 17.

    Slepian D: Prolate spheroidal wave functions, Fourier analysis, and uncertainty—V: the discrete case. The Bell System Technical Journal 1978,57(5):1371-1430.

  18. 18.

    Thomson DJ: Spectrum estimation and harmonic analysis. Proceedings of the IEEE 1982,70(9):1055-1096.

  19. 19.

    Dharanipragada S, Arun KS: Bandlimited extrapolation using time-bandwidth dimension. IEEE Transactions on Signal Processing 1997,45(12):2951-2966. 10.1109/78.650256

  20. 20.

    Percival DB, Walden AT: Spectral Analysis for Physical Applications. Cambridge University Press, Cambridge, UK; 1963.

  21. 21.

    Kaltenberger F, Zemen T, Ueberhuber CW: Low complexity simulation of wireless channels using discrete prolate spheroidal sequences. Proceedings of the 5th Vienna International Conference on Mathematical Modelling (MATHMOD '06), February 2006, Vienna, Austria

  22. 22.

    Slepian D, Pollak HO: Prolate spheroidal wave functions, Fourier analysis and uncertainty—I. The Bell System Technical Journal 1961,40(1):43-64.

  23. 23.

    Papoulis A: Probability, Random Variables and Stochastic Processes. 3rd edition. McGraw-Hill, New York, NY, USA; 1991.

  24. 24.

    Holma H, Tskala A (Eds): WCDMA for UMTS. 2nd edition. John Wiley & Sons, New York, NY, USA; 2002.

  25. 25.

    Steinbauer M, Molisch AF, Bonek E: The double-directional radio channel. IEEE Antennas and Propagation Magazine 2001,43(4):51-63. 10.1109/74.951559

  26. 26.

    Almers P, Bonek E, Burr A, et al.: Survey of channel and radio propagation models for wireless MIMO systems. EURASIP Journal on Wireless Communications and Networking 2007, 2007: 19 pages.

  27. 27.

    Members of 3GPP : Technical specification group radio access network; User Equipment (UE) radio transmission and reception (FDD). In Tech. Rep. 3GPP TS 25.101 version 6.4.0. 3GPP, Valbonne, France; 2004.

  28. 28.

    Erceg V, Schumacher L, Kyritsi P, et al.: TGn channel models. Tech. Rep. IEEE P802.11 2004.

  29. 29.

    Degli-Esposti V, Fuschini F, Vitucci EM, Falciasecca G: Measurement and modelling of scattering from buildings. IEEE Transactions on Antennas and Propagation 2007,55(1):143-153.

  30. 30.

    Pedersen T, Fleury BH: A realistic radio channel model based on stochastic propagation graphs. Proceedings of the 5th Vienna International Conference on Mathematical Modelling (MATHMOD '06), February 2006, Vienna, Austria

  31. 31.

    Norklit O, Andersen JB: Diffuse channel model and experimental results for array antennas in mobile environments. IEEE Transactions on Antennas and Propagation 1998,46(6):834-840. 10.1109/8.686770

  32. 32.

    Czink N, Bonek E, Yin X, Fleury B: Cluster angular spreads in a MIMO indoor propagation environment. Proceedings of the 16th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '05), September 2005, Berlin, Germany 1: 664–668.

  33. 33.

    Kaltenberger F, Steinböck G, Humer G, Zemen T: Low-complexity geometry based MIMO channel emulation. Proceedings of European Conference on Antennas and Propagation (EuCAP '06), November 2006, Nice, France

  34. 34.

    Moon TK, Stirling W: Mathematical Methods and Algorithms. Prentice-Hall, Upper Saddle River, NJ, USA; 2000.

Download references

Author information

Correspondence to Florian Kaltenberger.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Keywords

  • Computational Complexity
  • Prolate
  • Radio Channel
  • Mobile Radio
  • Channel Transfer