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Krylov Subspace Methods in Application to WCDMA Network Optimization

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Krylov subspace methods, which include, e.g. CG, CGS, Bi-CG, QMR or GMRES, are commonly applied as linear solvers for sparse large-scale linear least squares problems. In the paper, we discuss the usefulness of such methods to the optimization of WCDMA networks. We compare the selected methods with respect to their convergence properties and computational complexity, using a typical uplink model for a WCDMA network. The comparison shows that GMRES is the most suitable method for our task.
Rocznik
Strony
22--25
Opis fizyczny
Bibliogr. 15 poz., rys.
Twórcy
autor
  • Institute of Telecommunications, Teleinformatics, and Acoustics, Wroclaw University of Technology, 50–370 Wroclaw, Poland
autor
  • OPTYME Consulting, Wroclaw, Poland
Bibliografia
  • [1] M. J. Nawrocki, M. Dohler, and A. H. Aghvami, Eds., Understanding UMTS Radio Network Modelling, Planning and Automated Optimisation: Theory and Practice. John Wiley and Sons, 2006.
  • [2] L. Mendo and J. M. Hernando, “On dimension reduction for the power control,” IEEE Trans. On Communications, vol. 49, no. 2, pp. 243–248, 2001.
  • [3] R. Zdunek and M. J. Nawrocki, “Improved modeling of highly loaded UMTS network with nonnegative constraints,” in IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2006), Helsinki, Finland, September 2006.
  • [4] J. Laiho, A. Wacker, and T. Novosad, Radio Network Planning and Optimization for UMTS. Chichester: John Wiley and Sons, 2002.
  • [5] A.Wacker, J. Laiho-Steffens, K. Sipila, and M. Jasberg, “Static simulator for studying WCDMA radio network planning issues,” in Proc. IEEE Vehicular Technology Conference, Houston, Texas, USA, May 1999, pp. 2436–2440.
  • [6] R. Zdunek, M. J. Nawrocki, M. Dohler, and A. H. Aghvami, “Application of linear solvers to UMTS network optimization without and with smart antennas,” in IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2005), vol. 4, Berlin, Germany, September 11–14 2005, pp. 2322–2326.
  • [7] R. Zdunek and M. J. Nawrocki, “On linear solvers in applications to WCDMA network optimization,” in Proc. National Conference on Radio-communication, Radio and Television (KKRRiT), Krakow, Poland, June 15–17 2005, pp. 77–80.
  • [8] G. H. Golub and H. A. V. der Vorst, “Closer to the solution: Iterative linear solvers,” in The State of the Art in Numerical Analysis, I. Duff and G. Watson, Eds. Clarendon Press, Oxford, 1997, pp. 63–92.
  • [9] Y. Saad and H. A. V. der Vorst, “Iterative solution of linear systems in the 20-th century,” Journal of Computational and Applied Mathematics, vol. 123, no. 1–2, pp. 1–33, 2000.
  • [10] M. R. Hestenes and E. Stiefel, “Method of conjugate gradients for solving linear systems,” J. Res. Nat. Bur. Standards, vol. 49, pp. 409–436, 1952.
  • [11] P. C. Hansen, “Regularization tools version 4.0 for matlab 7.3,” Numerical Algorithms, vol. 46, pp. 189–194, 2007.
  • [12] P. Sonneveld, “CGS: A fast lanczos-type solver for nonsymmetric linear systems,” SIAM J. Sci. Statist. Comput., vol. 10, pp. 36–52, 1989.
  • [13] R. Fletcher, “Conjugate gradient methods for indefinite systems,” in Numerical Analysis, ser. Lecture Notes Math., G. Watson, Ed. Berlin-Heidelberd-New York: Springer-Verlag, 1976, vol. 506, pp. 73–89.
  • [14] R. W. Freund and N. M. Nachtigal, “QMR: a quasi-minimal residual method for non-hermitian linear systems,” Numer. Math., vol. 60, pp. 315–339, 1991.
  • [15] Y. Saad and M. H. Schultz, “GMRES: a generalized minimal residual algorithm for solving nonsymmetic linear systems,” SIAM J. Sci. Statist. Comput., vol. 7, pp. 856–869, 1986.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-68d70700-ef27-418e-ba57-958989b53c4b
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