Wireless Personal Communications

, Volume 85, Issue 3, pp 1635–1651 | Cite as

Performance Analysis of a Reduced Rank Spatial Filter for Interference Cancellation

Article

Abstract

In the interference mitigation context, it has been shown by simulations in Fety et al. (International symposium on wireless communication systems, pp 241–245, 2012), the outperformance of the coefficient constraints (CC) versus the power constraint on the channel impulse response, in terms of BER about 1–3 dB. However, no theoretical justification has been introduced. In this paper, we have proved theoretically this result. Moreover, we have investigated an interesting issue of the CC constraint concerning the choice of the coefficient position; we have given a full theoretical framework analysis about this feature. Two substantial propositions have been introduced to this end. Theoretical results were validated by simulations.

Keywords

Interference Reduced rank filter Condition number Power constraint 

References

  1. 1.
    Fety, L., Maoudj, R., Terre, M., Martinod, L., & Mege, P. (2012, August). Reduced rank spatial filter for interference cancellation. In International symposium on wireless communication systems, pp. 241–245.Google Scholar
  2. 2.
    Andrews, J. G. (2005). Interference cancellation for cellular systems: A contemporary overview. IEEE Wireless Communications Magazine, 12, 19–29.CrossRefGoogle Scholar
  3. 3.
    Paulraj, A., Naber, R., & Gore, D. (2003). Introduction to space–time wireless communications. Cambridge: Cambridge University Press.Google Scholar
  4. 4.
    Foschini, G. J., & Gans, M. J. (1998). On limits of wireless communications in a fading environment when using multiple antennas. Wireless Personal Communication, 6, 311–335.CrossRefGoogle Scholar
  5. 5.
    Daleh, M., Daleh, M.A., & Verghese, G. (2000). Lectures on dynamic systems and control. Dept. of Elec. Eng. and Comp. Sci., Mass. Inst. Tech.Google Scholar
  6. 6.
    Higham, N. J. (1987). A survey of condition number estimation for triangular matrices. SIAM Review, 29(4), 575–596.MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Lagunas, M. A., Vidal, J., & Pérez Neira, A. I. (2000). Joint array combining and MLSE for single-user receivers in multipath Gaussian multiuser channels. IEEE Journal on Selected Areas in Communication, 18(11), 2252–2259.CrossRefGoogle Scholar
  8. 8.
    Pérez Neira, A. I., & Mestre, X. (2002, May). A comparative performance study of different space-frequency filters for OFDM. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing. Google Scholar
  9. 9.
    Vidal, J., Cabrera, M., & Augustin, A. (2000). Full exploitation of diversity in space–time MMSE receivers. In 52nd Vehicular technology conference, IEEE VTS-Fall VTC 2000, Vol. 5, pp. 2497–2502.Google Scholar
  10. 10.
    Maoudj, R., & Terre, M. (2012, September). Post-combiner for interference cancellation algorithm. In International conference on software, telecommunications and computer networks.Google Scholar
  11. 11.
    Edelman, A. (1988). Eigenvalues and condition number of random matrices. SIAM Journal on Matrix Analysis and Applications, 9(4), 543–560.MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Ratnarajah, T., Vaillancourt, R., & Alvo, M. (2005). Eigenvalues and condition numbers of complex random matrices. SIAM Journal on Matrix Analysis and Applications, 26(2), 441–456.MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Matthaiou, M., McKay, M. R., Smith, P. J., & Nossek, J. A. (2010). On the condition number distribution of complex wishart matrices. IEEE Transactions on Communications, 58(6), 1705–1717.CrossRefGoogle Scholar
  14. 14.
    Okumura, Y., et al. (1968). Field strength and its variability in VHF and UHF land-mobile radio service. Review of the Electrical Communication Laboratory NTT, 16, 9–10.Google Scholar
  15. 15.
    Hata, M., et al. (1980). Empirical formula for propagation loss in land mobile radio services. IEEE Transactions On Vehicular Technology, 29, 317–325.CrossRefGoogle Scholar
  16. 16.
    Mullen, J., &, Huang, H. (2005). Impact of multipath fading in wireless ad hoc networks. In Proceedings of the 2nd ACM international workshop on performance evaluation of wireless ad hoc, sensor, and ubiquitous networks, NY, USA.Google Scholar
  17. 17.
    Jakes, W. C. (1975). Microwave mobile communications. New York: Wiley.Google Scholar
  18. 18.
    COST-207: Digital land mobile radio communications. Final report of the COST-Project 207, Commission of the European Community, Brussels, 1989.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Lab. Cedric/Laetitia CnamParis Cedex 03France

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