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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Chen, Sheng; Hong, Xia; Harris, Chris J. (2014)
Languages: English
Types: Unknown
Subjects:

Classified by OpenAIRE into

ACM Ref: ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, Data_CODINGANDINFORMATIONTHEORY, InformationSystems_MISCELLANEOUS
We develop an on-line Gaussian mixture density estimator (OGMDE) in the complex-valued domain to facilitate\ud adaptive minimum bit-error-rate (MBER) beamforming\ud receiver for multiple antenna based space-division multiple access systems. Specifically, the novel OGMDE is proposed to adaptively model the probability density function of the beamformer’s output by tracking the incoming data sample by sample. With the aid of the proposed OGMDE, our adaptive beamformer is capable of updating the beamformer’s weights sample by sample to directly minimize the achievable bit error rate (BER). We show that this OGMDE based MBER beamformer outperforms the existing on-line MBER beamformer, known as the least BER beamformer, in terms of both the convergence speed and the achievable BER.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] J. Litva and T. K. Y. Lo, Digital Beamforming in Wireless Communications. London: Artech House, 1996.
    • [2] P. Vandenameele, L. van Der Perre, and M. Engels, Space Division Multiple Access for Wireless Local Area Networks. Boston: Kluwer Academic Publishers, 2001.
    • [3] J. S. Blogh and L. Hanzo, Third Generation Systems and Intelligent Wireless Networking - Smart Antenna and Adaptive Modulation. Chichester, U.K.: John Wiley, 2002.
    • [4] S. Chen, N. N. Ahmad, and L. Hanzo, “Adaptive minimum bit error rate beamforming,” IEEE Trans. Wireless Communications, vol. 4, no. 2, pp. 341-348, March 2005.
    • [5] S. Chen, L. Hanzo, N. N. Ahmad, and A. Wolfgang, “Adaptive minimum bit error rate beamforming assisted receiver for QPSK wireless communication,” Digital Signal Processing, vol. 15, no. 6, pp. 545-567, Nov. 2005.
    • [6] S. Haykin, Adaptive Filter Theory (2nd Edition). Englewood, NJ: Prentice Hall, 1991.
    • [7] J. A. Bilmes, “A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models,” International Computer Science Institute, vol. 4, no. 510, 281 pages, 1998.
    • [8] G. McLachlan and D. Peel, Finite Mixture Models. John Wiley & Sons, Inc., 2004.
    • [9] S. Chen, X. Hong, and C. J. Harris, “Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization,” IEEE Trans. Systems, Man, and Cybernetics, Part B, vol. 34, no. 4, pp. 1708-1717, Aug. 2004.
    • [10] X. Hong, S. Chen, and C. J. Harris, “A forward-constrained regression algorithm for sparse kernel density estimation,” IEEE Trans. Neural Networks, vol. 19, no. 1, pp. 193-198, Jan. 2008.
    • [11] S. Chen, X. Hong, and C. J. Harris, “An orthogonal forward regression technique for sparse kernel density estimation,” Neurocomputing, vol. 71, nos. 4-6, pp. 931-943, Jan. 2008.
    • [12] S. Chen, X. Hong, and C. J. Harris, “Regression based D-optimality experimental design for sparse kernel density estimation,” Neurocomputing, vol. 73, nos. 4-6, pp. 727-739, Jan. 2010.
    • [13] S. Chen, X. Hong, and C. J. Harris, “Particle swarm optimization aided orthogonal forward regression for unified data modelling,” IEEE Trans. Evolutionary Computation, vol. 14, no. 4, pp. 477-499, Aug. 2010.
    • [14] S. Chen, A. K. Samingan, B. Mulgrew, and L. Hanzo, “Adaptive minimum-BER linear multiuser detection for DS-CDMA signals in multipath channels,” IEEE Trans. Signal Processing, vol. 49, no. 6, pp. 1240-1247, June 2001.
    • [15] S. Chen, A. Livingstone, H.-Q. Du, and L. Hanzo, “Adaptive minimum symbol error rate beamforming assisted detection for quadrature amplitude modulation,” IEEE Trans. Wireless Communications, vol. 7, no. 4, pp. 1140-1145, April 2008.
    • [16] M. S. Bazaraa, H. D. Sherali, and C. M. Shetty, Nonlinear Programming: Theory and Algorithms. New York: John Wiley, 1993.
    • [17] E. Parzen, “On estimation of a probability density function and mode,” The Annals of Mathematical Statistics, vol. 33, pp. 1066-1076, 1962.
    • [18] B. W. Silverman, Density Estimation. London: Chapman Hall, 1996.
    • [19] A. W. Bowman and A. Azzalini, Applied Smoothing Techniques for Data Analysis. Oxford, U.K.: Oxford University Press, 1997.
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