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
Link To Document