Analyses of UWB-IR in Statistical Models for MIMO Optimal Designs

  • Xu HuangEmail author
  • Dharmendra Sharma
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 52)


The third generation partnership projects spatial channel model has been attracting great and wider interests from the researchers for a stochastic channel model for MIMO systems and multi-antenna-based multi-input multi-output (MIMO) communications as they become the next revolution in wireless data communications. MIMO has gone through the adoption curve for commercial wireless systems to the today’s situation, all high throughput commercial standards, i.e. WiMax, Wi-Fi, cellular, etc., have adopted MIMO as part of the optional. This paper is to present our investigations of the behaviors of the MIMO Ultra-Wide-Band-Impulse Radio (UWB-IR) systems, which will contribute to optimal designs for the low-power high-speed data communication over unlicensed bandwidth spanning several GHz, such as IEEE 802.15 families. We have developed and analyzed three no coherent transceiver models without requiring any channel estimation procedure. The massive simulations are made based on the established models. Our investigations show that the Poisson distribution of the path arriving will affect the signal-noise ratio (SNR) and that for the Nakagami distributed multipath fading channel the “m” factor, together with receiver number, will impact on the SNR of the MIMO UWB-IR systems.


MIMO WiMax UWB-IR Poisson distribution Nakagami distribution 


  1. 1.
    Reed, J.H. (2005). An introduction to ultra wideband communication systems. Englewood Cliff, NJ: Prentice Hall.Google Scholar
  2. 2.
    Molishch, A.F., Cassion, D., et al. (2006). A comprehensive standardized model for ultrawideband propagation channels. IEEE Transactions on Antennas and Propagation, 54(11), 3151–3166.CrossRefGoogle Scholar
  3. 3.
    Ohtsuki, T. Space-time Trellis coding for UWB-IR. Vehicular Technology Conference, VTC 2004, Spring 2004 IEEE 59th, 2, 1054–1058.Google Scholar
  4. 4.
    Ezaki, T., & Ohtsuki, T. (2005). Rake performance for UWB-IR system with SISO and MISO. IEICE Transactions on Communications, E88-B(10), 4112–4116.CrossRefGoogle Scholar
  5. 5.
    Barton, R.J., & Rao, D. (2008). Performance capabilities of long-range UWB-IR TDOA localization systems. EURASIP Journal on Advances in Signal Processing, 2008(81), 1–17.Google Scholar
  6. 6.
    Lee, S.-H., Kim, N.-S., Kang, H.-J., Kim, S.-G. (2006). Performance improvement of intelligent UWB-IR communication system in multipath channel. ICIC 2006 (pp. 1103–1108). Berlin, Heidelberg: Springer.Google Scholar
  7. 7.
    Reed, J.H., Baccarelli, E., Biagi, M., Pelizzoni, C., Cordeschi, N. (Jan 2008). Optimal MIMO UWB-IR transceiver for Nakagami-fading and Poisson-Arrivals. Journal of Communications, 3(1), 27–40.Google Scholar
  8. 8.
    Promwong, S., Hanitach, W., Takada, J.-I., Koon, P.S., Tangtisanon, P. (Oct–Dec 2003). Measurement and analysis of UWB-IR antenna performance for WPANs. Thammasat International Journal of Science and Technology, 8(4), 56–62.Google Scholar
  9. 9.
    Duenas, S.R., Dno, X., Yamac, S., Lsmail, M., Zheng, L.-R. (2006). CMOS UWB IR non-coherent receiver for RF-ID applications. Applications, Circuits and Systems, IEEE, 2006 IEEE North-East Workshop on Circuits and Systems. Gatineau, Que., Canada, 18–21 June 2006, 213–216.Google Scholar
  10. 10.
    Proakis, J. (2001). Digital communications (4th ed.). New York: McGraw-Hill.Google Scholar
  11. 11.
    Huang X., & Madoc, A.C. (2005). Image multi-noise removal via Levy process analysis. Lecture Notes in Computer Science (vol. 3684, pp. 22–25). Berlin, Heideberg: Springer.Google Scholar
  12. 12.
    Huang, X. (June 23–26, 2008). Noise removal for image in Nakagami fading channels by Wavelet-based Bayesian Estimator. IEEE International Conference on Multimedia & Expo 2008 (pp. 21–24). Germany.Google Scholar
  13. 13.
    Huang, X., & Sharma, D. (11–13 Feb 2009). MIMO UWB-IR noncoherent transceiver with Poisson wireless models. IEEE International Symposium on Wireless and Pervasive Computing (pp. 11–16). Melbourne, Australia. ISBN: 978–1–4244–2966–0.Google Scholar
  14. 14.
    Huang, X., & Sharma, D. (Mar 2009). Behaviours of MIMO UWB-IR transceiver with statistical models. International Multi-Conference of Engineers and Computer Sciences 2009 (Proc. pp. 440). (IMECS 2009), Hong Kong.Google Scholar
  15. 15.
    Huang, X., & Sharma, D. (18–19 Mar 2009). Behaviours of MIMO UWB-IR transceiver with statistical models. International Multi-Conference of Engineers and Computer Sciences 2009 (pp. 440). (IMECS 2009), Hong Kong.Google Scholar
  16. 16.
    Baccarelli, E., Biagi, M., Pelizzoni, C., Cordeschi, N. (2007) Non-coherent transceivers for multipath-affected MIMO UWB-IR communications.
  17. 17.
    Nagesh Polu, V.V.S., Colpits, B.G., Peterson, B.R. (2006). Symbol-wavelength MMSE gain in a multi-antenna UWB system. IEEE Proceedings of the 4th Annual Communication Networks ans Service Research Conference (CNSR’06), (pp. 1–5).Google Scholar
  18. 18.
    Ezaki, T., & Ohtsuki, T. (2004). Performance evaluation of space hopping ultra wideband impulse radio (SH-UWB-IR) system. IEEE International Conference on Communication (ICC’04), 6, 3591–3595.Google Scholar
  19. 19.
    Wei, Y.R., & Wang, M.Z. (Feb 2007). Efficient capacity-based joint transmit and receive antenna selection schemes in MIMO systems. IEICE Transactions on Communication, E90-B(2), 372–376.CrossRefGoogle Scholar
  20. 20.
    Winters, J. (Aug 1984). Optimum combining in digital mobile radio with cochannel interference. Special Issue on Mobile Radio Communications IEEE Journal on Selected Areas in Communications. IEEE Transactions on Vehicular Technology, 2(4), 528–539.Google Scholar
  21. 21.
    Releigh, G.G., & Jones, V.K. (May 1999). Multivariate modulation and coding for wireless communication. IEEE Journal of Selected B Areas in Communication, 17(5), 851–866.CrossRefGoogle Scholar
  22. 22.
    Gerard, J.F. (Oct 1996). Layer space-time architecture for wireless communication in a fading environment when using multi-element antennas. Bell Laboratory Technical Journal, 1(2), 41–59.Google Scholar
  23. 23.
    Emre Telatar I. (Nov 1999). Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications, 10, 585–595.CrossRefGoogle Scholar
  24. 24.
    Bolcskei, H. (13 Oct 2005). MIMO Systems. ETH Zurich: Communication technology laboratory.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Faculty of Information Sciences and EngineeringUniversity of CanberraCanberraAustralia

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