• DocumentCode
    1386829
  • Title

    New insights on the transient and steady-state behavior of the quantized LMS algorithm

  • Author

    Bershad, Neil J. ; Bermudez, José Carlos M

  • Author_Institution
    Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA
  • Volume
    44
  • Issue
    10
  • fYear
    1996
  • fDate
    10/1/1996 12:00:00 AM
  • Firstpage
    2623
  • Lastpage
    2625
  • Abstract
    This correspondence investigates the transient and steady-state behavior of the quantized LMS algorithm for Gaussian inputs. It is shown here that the so-called “stopping” phenomenon is really a “slow-down” phenomenon, which, because of an extremely slow convergence rate, looks as if the algorithm has stopped. The true steady-state MSE is shown to be nearly independent of the number of bits in the digital word-length and very nearly the steady-state MSE of the infinite precision LMS realization. These results assume that the algorithm “misadjustment” effects due to coefficient quantization are negligible in comparison with those due to the “stopping” phenomena. Since the true steady state is rarely achievable with a finite number of iterations, determination of the step size μ that minimizes the residual MSE must be based on a stochastic model for the transient mode of algorithm operation. It is shown that the finite word length and infinite precision design cases differ only in degree and not in kind as far as the selection of μ is concerned
  • Keywords
    Gaussian processes; adaptive filters; convergence of numerical methods; least mean squares methods; signal processing; stochastic processes; transient analysis; Gaussian inputs; algorithm operation; coefficient quantization; convergence rate; digital word-length; misadjustment effects; quantized LMS algorithm; slow-down phenomenon; steady-state behavior; step size; stochastic model; stopping phenomenon; transient behavior; transient mode; Adaptive filters; Algorithm design and analysis; Convergence; Error analysis; Fixed-point arithmetic; Least squares approximation; Quantization; Signal processing algorithms; Steady-state; Stochastic processes;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/78.539047
  • Filename
    539047