Advertisement

Error Rate Analysis of ZF and MMSE Decoders for Massive Multi Cell MIMO Systems in Impulsive Noise Channels

  • Hasan Abu HilalEmail author
Article

Abstract

In wireless channels, Non-Gaussian noise is one of the most common noise models that is observed. This type of noise has a severe impact on wireless systems with linear and multiuser detection devices. In this paper, We study the performance of zero forcing (ZF) and minimum mean square error (MMSE) ZF detection methods in Impulsive multi-cell MIMO channels. We start by showing the Bit Error Rate performance in non-Gaussian channels for ZF Detection, then we extend the derivations for MMSE ZF system. We clearly show the lower and upper bound derivations and verify it through simulations. The sum rate analysis for this case is also examined. Finally, we address the ZF matrix inversion complexity problem, and propose a simple ZF algorithm that does not necessitate the matrix inversion. We then investigate the convergence of such a detector and look at the Symbol Error rate SER performance through simulation again.

Keywords

Multicell Impulsive noise MIMO Linear precoding 

Notes

References

  1. 1.
    M. Chiani, M. Z. Win and H. Shin, MIMO networks: the effects of interference, IEEE Transactions on Information Theory, Vol. 56, No. 1, pp. 336–349, 2010.  https://doi.org/10.1109/TIT.2009.2034810.MathSciNetzbMATHGoogle Scholar
  2. 2.
    Y. Li and Z. Zhang, Co-channel interference suppression for multi-cell MIMO heterogeneous network, EURASIP Journal on Advances in Signal Processing, Vol. 2016, No. 1, pp. 1–12, 2016.Google Scholar
  3. 3.
    H. Zhang, et al., On massive MIMO performance with semi-orthogonal pilot-assisted channel estimation, EURASIP Journal on Wireless Communications and Networking, Vol. 2014, No. 1, pp. 1–14, 2014.Google Scholar
  4. 4.
    A. Y. Panah, K. Yogeeswaran and Y. Maguire, Performance of regression-based precoding for multi-user massive MIMO-OFDM systems, EURASIP Journal on Advances in Signal Processing, Vol. 2016, No. 1, p. 1, 2016.Google Scholar
  5. 5.
    X. Jia, et al., Performance analysis of cooperative cognitive MIMO multiuser downlink transmission with perfect and imperfect CSI over Rayleigh fading channels, EURASIP Journal on Wireless Communications and Networking, Vol. 2015, No. 1, pp. 1–21, 2015.Google Scholar
  6. 6.
    A. Kammoun, et al., Linear precoding based on polynomial expansion: large-scale multi-cell MIMO systems, IEEE Journal of Selected Topics in Signal Processing, Vol. 8, No. 5, pp. 861–875, 2014.Google Scholar
  7. 7.
    S. Verdu, Multiuser Detection. Cambridge University Press, Cambridge, 1998.Google Scholar
  8. 8.
    H. Wang, et al., On error rate performance of multi-cell massive MIMO systems with linear receivers, Physical Communication, Vol. 20, pp. 123–132, 2015.  https://doi.org/10.1016/j.phycom.2015.10.002.
  9. 9.
    D. N. C. Tse and S. V. Hanly, Linear multiuser receivers: effective interference, effective bandwidth and user capacity, IEEE Transactions on Information Theory, Vol. 45, No. 2, pp. 641–657, 1999.MathSciNetzbMATHGoogle Scholar
  10. 10.
    K. L. Blackard, T. S. Rappaport and C. W. Bostian, Measurements and models of radio frequency impulsive noise for indoor wireless communications, IEEE Journal on Selected Areas in Communications, Vol. 11, pp. 991–1001, 1993.Google Scholar
  11. 11.
    T. K. Blankenship, D. M. Krizman, and T. S. Rappaport, Measurements and simulation of radio frequency impulsive noise in hospitals and clinics. In Proceedings of the IEEE Vehicular Technology Conference, pages 1942–1946, 1997.Google Scholar
  12. 12.
    G. Madi, F. Sacuto, B. Vrigneau , B. L. Agba, Y. Pousset, R. Vauzelle, and F. Gagnon, Impacts of impulsive noise from partial discharges on wireless systems performance: application to MIMO precoders, EURASIP Journal on Wireless Communications and Networking, 2011.  https://doi.org/10.1186/1687-1499-2011-186.
  13. 13.
    H. Abuhilal, A. Hocanin and H. Bilgekul, Successive interference cancelation for a CDMA system with diversity reception in non-Gaussian noise, International Journal of Communication Systems, Vol. 26, No. 7, pp. 875–887, 2013.Google Scholar
  14. 14.
    H. A. Hilal, Neural networks applications for CDMA systems in non-Gaussian multi-path channels, AEU—International Journal of Electronics and Communications, Vol. 73, pp. 150–156, 2017.  https://doi.org/10.1016/j.aeue.2017.01.006.
  15. 15.
    J.-M. Kwadjane, B. Vrigneau, C. Langlais, Y. Cocheril and M. Berbineau, Performance evaluation of max-dmin precoding in impulsive noise for train-to-wayside communications in subway tunnels, EURASIP Journal on Wireless Communications and Networking, Vol. 2014, p. 83, 2014.  https://doi.org/10.1186/1687-1499-2014-83.Google Scholar
  16. 16.
    Z. Ali, F. Ayaz and C.-S. Park, Optimized threshold calculation for blanking nonlinearity at OFDM receivers based on impulsive noise estimation, EURASIP Journal on Wireless Communications and Networking, Vol. 2015, No. 1, pp. 1–8, 2015.Google Scholar
  17. 17.
    A. Kammoun, et al., Linear precoding based on polynomial expansion: large-scale multi-cell MIMO systems, IEEE Journal of Selected Topics in Signal Processing, Vol. 8, No. 5, pp. 861–875, 2014.Google Scholar
  18. 18.
    Q. H. Spencer, A. Lee Swindlehurst, and M. Haardt, Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels, IEEE Transactions on Signal Processing, Vol. 52, No. 2, pp. 461–471, 2004.Google Scholar
  19. 19.
    M. Schubert and H. Boche, An efficient algorithm for optimum joint downlink beamforming and power control. In Vehicular Technology Conference 2002. VTC Spring 2002. IEEE 55th, Vol. 4. IEEE, 2002.Google Scholar
  20. 20.
    M. Bengtsson and B. Ottersten, Optimal and suboptimal transmit beamforming, Handbook of Antennas in Wireless Communications, pp. 18-1, 2001.Google Scholar
  21. 21.
    D. Middleton, Statistical-physical models of electromagnetic interference, IEEE Transactions on Electromagnetic Compatibility, Vol. EC-19, pp. 106-127, 1977.Google Scholar
  22. 22.
    A. D. Spaulding and D. Middleton, Optimum reception in an impulsive interference environment-part I: coherent detection, IEEE Transactions on Communications, Vol. 25, No. 9, pp. 910–923, 1977.zbMATHGoogle Scholar
  23. 23.
    J. Haring and A. J. H. Vinck, Performance bounds for optimum and suboptimum reception under class-A impulsive noise, IEEE Transactions on Communications, Vol. 50, No. 7, pp. 1130–1136, 2002.Google Scholar
  24. 24.
    P. A. Delaney, Signal detection in multivariate class-A interference, IEEE Transactions on Communications, Vol. 43, No. 2, pp. 365–373, 1995.Google Scholar
  25. 25.
    S. Buzzi, E. Conte and M. Lops, Optimum detection over Rayleigh fading, dispersive channels, with non-Gaussian noise, IEEE Transactions on Communications, Vol. 45, No. 9, pp. 1061–1069, 1997.Google Scholar
  26. 26.
    N. Kim, Y. Lee and H. Park, Performance analysis of MIMO system with linear MMSE receiver, IEEE Transactions on Wireless Communications, Vol. 7, No. 11, pp. 4474–4478, 2008.Google Scholar
  27. 27.
    M. Matthaiou, C. Zhong and T. Ratnarajah, Novel generic bounds on the sum rate of MIMO ZF receivers, IEEE Transactions on Signal Processing, Vol. 59, No. 9, pp. 4341–4353, 2011.MathSciNetzbMATHGoogle Scholar
  28. 28.
    I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and Products, 7th ed. Academic, San Diego, CA, 2007.Google Scholar
  29. 29.
    D. A. Gore, R. W. Heath Jr. and A. Paulraj, Transmit selection in spatial multiplexing systems, IEEE Communications Letters, Vol. 6, No. 11, pp. 491–493, 2002.Google Scholar
  30. 30.
    G. L. Stüber, Principles of Mobile Communications, 2nd ed. Kluwer Academic Publishers, Dordrecht, 2002.Google Scholar
  31. 31.
    M. K. Simon and M.-S. Alouini, Digital Communication over Fading Channels, 2nd ed. Wiley, New York, 2005.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Higher Colleges of TechnologyAbu DhabiUnited Arab Emirates

Personalised recommendations