Alternative approach for computing the activation factor of the PNLMS algorithm

Abstract

This paper presents a proportionate normalized least-mean-square (PNLMS) algorithm using an individual activation factor for each adaptive filter coefficient. Such strategy is used instead of a global activation factor as in the standard PNLMS algorithm. The proposed individual activation factors, determined in terms of the corresponding adaptive filter coefficients, lead to a better distribution of the adaptation energy over the filter coefficients than the standard PNLMS does. Thereby, for impulse responses exhibiting high sparseness, the proposed algorithm achieves faster convergence, outperforming both the PNLMS and improved PNLMS (IPNLMS) algorithms.