Wireless Personal Communications

, Volume 39, Issue 1, pp 15–24 | Cite as

The Application of Successive Quadratic Programming Algorithm to Multiuser Detection in CDMA

Article
  • 57 Downloads

Abstract

In this paper, based on the semidefinite programming relaxation of the CDMA maximum likelihood (ML) multiuser detection problem, a detection strategy by the successive quadratic programming algorithm is presented. Coupled with the randomized cut generation scheme, we obtain the suboptimal solution of multiuser detection problem. Comparing with the reported interior point methods based on semidefinite programming, simulations demonstrate that the successive quadratic programming algorithm often yields the similar BER performances of the multiuser detection problem. But the average CPU time of this approach is significantly reduced.

Keywords

code division multiple access multiuser detection semidefinite programming successive quadratic programming probabilistic data association 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    S. Verdu, Multiuser Detection, Cambridge University Press: Cambridge, 1998.MATHGoogle Scholar
  2. 2.
    H.T. Peng and L.K. Rasmussen, “The Application of Semidefinite Programming for Detection in CDMA”, IEEE Selected in Communication, Vol. 19, No. 8, pp. 1442–1449, 2001.CrossRefGoogle Scholar
  3. 3.
    L. Wei, L. Krasmussen, and R. Wyrwas, “Near Optimum Tree-Search Detection Schemes for Bit-Synchronous Multiuser CDMA System Over Gaussian and Two-Path Rayleigh-Fading Channels”, IEEE Trans. Communication, Vol. 39, No 5, pp. 725–736, 1991.CrossRefGoogle Scholar
  4. 4.
    Sharfer and A.O. Hero, “A Maximum Likelihood Digital Receiver Using Coordinate Ascent and the Discrete Wavelet Transform”, IEEE Trans. Signal Processing, Vol. 47, No. 3,pp. 813–825, 1999.CrossRefGoogle Scholar
  5. 5.
    X.M. Wang, W. S. Lu, and A. Antoniou, “A Near-Optimal Detector for DS-CDMA Systems Using Semidefinite Programming Relaxation”, IEEE Trans, Signal Processing, Vol. 51, No. 9, pp. 2446–2450, 2003.CrossRefGoogle Scholar
  6. 6.
    C. Helmberg, Semidefinite Programming for Combinatorial Optimization, Konrad-Zuse-Zentrum fur informationstechnik:Berlin, Germany, 2000.Google Scholar
  7. 7.
    C. Helmberg and F. Rendl, “An Interior-Point Method for Semidefinite Programming”, SIAM J. Optim., Vol.6, No. 2, pp. 342–361, 1996.MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    M. Marko, Makeka, and Pekka Neittaanmaki, Nonsmooth Optimization: Analysis and Algorithms with Application to Optimization Control, World scientific:Singapore, 1992.Google Scholar
  9. 9.
    M.X. Goeman and D.P. Williamson, “Improved Approximation Algorithms for Maximum Cut and Satisfiably Problem Using Semidefinite Programming”, Jour. of ACM, Vol. 42, pp. 1115–1145, 1995.CrossRefGoogle Scholar
  10. 10.
    M.V. Nayakakuppam, M.L. Overton, and S. Schemita, “SDPpack User's Guide-Version 0.9 Beta”, Technical Report, Courtant Institute of Math. Science, NYU, New York, 1997.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Department of Applied MathematicsXidian UniversityXi’anChina

Personalised recommendations