Stochastic model for the mean weight evolution of the IAF-PNLMS algorithm

Authors:
Francisco das Chagas de Souza;Orlando José Tobias;Rui Seara;Dennis R. Morgan
Affiliations:
LINSE, Circuits and Signal Processing Laboratory, Department of Electrical Engineering, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil;LINSE, Circuits and Signal Processing Laboratory, Department of Electrical Engineering, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil;LINSE, Circuits and Signal Processing Laboratory, Department of Electrical Engineering, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil;Bell Laboratories, Alcatel-Lucent, Murray Hill, NJ
Venue:
IEEE Transactions on Signal Processing
Year:
2010

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Abstract

This correspondence studies the adaptive weight evolution of the individual-activation-factor proportionate normalized least-mean-square (IAF-PNLMS) algorithm. For such, the modeling methodology used considers that the gain matrix is time varying and the input signal is not restricted to be white. A model is obtained that predicts the algorithm mean weight behavior for both transient and steady-state phases. Through simulation results, the accuracy of the proposed model is verified. In addition, the approach developed here is general and can be applied to other PNLMS-type algorithms.